By Mike Levin

Real-Time Development of Pipulate for SEO

This article documents the real-time development process of a feature within a software project called “Pipulate,” designed for Search Engine Optimization (SEO) tasks.

Integration with Botify

The specific goal is to integrate with “Botify,” a well-known enterprise SEO analytics platform, to enable users to export large datasets (specifically CSV files) using Botify’s Application Programming Interface (API).

Technical Challenges and Design Decisions

The author details the technical challenges, design decisions, and implementation steps involved, including parsing specific web addresses (URLs), handling data limits, interacting with different modes of the Botify API (known as BQL), and managing the asynchronous nature of large data export jobs.

Modern Software Development Practices

The text also provides a fascinating look into modern software development practices, heavily featuring the use of Artificial Intelligence (AI) coding assistants (like ChatGPT, Gemini, Grok, and Claude, often within an editor called Cursor) to generate code, provide implementation plans, and troubleshoot issues.

Iterative Problem Solving and Dependency Management

It highlights the iterative process of breaking down complex problems, managing dependencies, choosing development environments (Python scripts vs. Jupyter Notebooks), and the practical considerations of building a tool intended for real-world use, balancing immediate needs with long-term goals for the software.

Top Priority: Downloading CSVs From Botify

Okay, this is the weekend that the coworker-facing version of Pipulate is born. That means it will have a Botify emphasis coming out of the starting gate, because that’s my day-job and that’s what pays the bills. A free and open source SEO product for the general public will be close on its heals. But I have to prioritize the bill-paying features first.

Managing Implementation Speed and Expectations

Speed of implementation this weekend is an issue. How many projects of the sort that truly have an accelerating effect, good choices per the 80/20-rule, can I really plow through this weekend so that come Monday, my world is changed forever? The reality is always many fewer than the vision in my head. Be conservative with what I can get done, but push hard.

The 1, 2, 3… 1? Methodology

We identify the first project in the 1, 2, 3... 1?-step methodology in which we are always really on step-1 because time is linear that way, and the read/write-head of the Turing machine of life is always on the now moment. That’s what we control, and the strongest move forward is in the effective use of the now-moment. That step-1 is the CSV files downloads from Botify.

Handling Nested Dependencies

Okay, one of the interesting things about the now moments is when you set to do the thing immediately before you, nested dependencies shake out. And these nested dependencies might be deep rabbit holes undermining your overall effectiveness, sending you on wild goose chases and chasing red herrings. The idea is to not dive deep into nested dependencies. Dispense with them quickly.

Development Environment Strategy

Doing a CSV export from Botify has a lot of steps to it, and these are those dependencies. I will blast through them here, breaking out the tiny sub-projects into .py-files, which I find I get the best AI-help with if it’s not actually a Juptyer Notebook in Cursor. While you can use Notebooks in Cursor and VSCode, I find that it confuses the AI assistants that have enough to keep track of. This will probably change over time, but that’s the state of affairs right now. So for good, solid AI-assist, it’s .py-files in Cursor.

Balancing Jupyter Notebooks and AI Assistance

However, I cannot break my Juptyer Notebooks addiction, and when doing things that are very much Pandas data pipeline-esque, I must have a Notebook where I’m building things up cell-by-cell. Those who have been bitten by the Notebook bug know what I’m talking about; data scientists and financial people. And the Notebook-world and VSCode/AI-world — despite JupyterAI and despite the VSCode-forks technically supporting it — don’t coexist well. And so, I have an AI-less instance of JuptyerLabs (the one provided by Pipulate) running side-by-side with Cursor. I mock up code in Cursor with AI assistance in a .py-file, and copy-paste it to the appropriate cell in a separate .ipynb-file in Jupyter.

Organizing Development Files

As far as the file locations where I do this, a sort of scratchpad, I now have the pipulate/precursors/ location, which is Jupyter Notebook .ipynb or executable script .py-files on their way to becoming Pipulate Workflows or CRUD apps. This is a location that can get a bit messy, and I’ll have to clean it up and bring order to it over time, as useful utilities will exist in there for awhile until they find better homes.

Project Acceleration and Context Management

Project acceleration starts here. Actually, it started yesterday with allowing articles like this to be included in super-prompts that cherry-pick parts of the overarching repo to put in context, plus an article like this, plus a manifest of included files and their token sizes near the beginning so the LLM knows what its dealing with, plus a pre-prompt and a post-prompt to sandwich the considerable chunk of information they’re dealing with. One prompt at the beginning sets the stage and the prompt at the end reminds the LLM what its doing because they tend to forget after parsing through a huge chunk of tokens with all kinds of embedded stories distracts them from their original purpose.

Breaking Down the Implementation

That was yesterday’s work. That’s done, and it’s time to put it to excellent use today. The nested dependencies are numerous. Instead of the giant context foo trick, I think I’ll start with tiny chisel-strike projects. Each one is interesting in its own right. It could probably justify a number of smaller articles, each one zooming in on the micro-challenge but I’m not taking the time to organize it like that. This is like hurdles over obstacles in a race. Go!

Parsing Botify Project URLs

The first step in the Pipulate Workflow, or cell in the Jupyter Notebook, will be to take a Botify “project” URL of almost any variety and strip it down to the basics, because none of it is needed except for the organization (org) and project “slugs” that can be extracted out of it. So we start with something like:

https://app.botify.com/example-org/example.com/crawl?analysisSlug=20250413&comparisonAnalysisSlug=20250330

…in which example-org is the organization slug and example.com is the project slug. In Python f-string terms, the pattern we are re-constructing given a URL of this type is:

f”https://app.botify.com/{org}/{project}/”

Understanding Pipulate Workflow Data Structure

Okay, so the way data ends up in a Pipulate workflow record in the JSON data blob field is:

{"created": "2025-04-19T10:51:15.038580", "updated": "2025-04-19T10:51:33.325942", "step_01": {"name": "Mike"}, "step_02": {"greeting": "Hello Mike"}, "finalize": {"finalized": true}}

The structure is that of a nested dictionary. While step_01 only has {"name": "Mike"}, there’s no reason it couldn’t have:

{"url": "https://app.botify.com/example-org/example.com/", "org": "example-org", "project": "example.com"}

Understanding Step Data Storage in Pipulate

So, a single step, aka card, in a Pipulate pipeline can have multiple key/value pairs that it stores, but only one of them indicates that the step is “done”. In this way, even a single step can have multiple progressive sub-steps, or even if it’s a 1-shot step, it can record multiple datapoints. Now it might seem that storing the canonicalized project URL plus the org and project broken out might seem redundant, the benefit is simply not having to repeat the extraction or concatenation code elsewhere when they need to be used. They can be grabbed directly out of their respective fields.

Simplifying the Development Process

This step is really me getting over a hump. I have been remiss in doing this step because of general confusion regarding execution environments, mixed concerns and the like. So what I’m doing is distilling it down to its pure essence, Python-wise and putting it in a file named precursors/temp.py whose mere name is a clue to the AI assistants involved that they also need not concern themselves with all that outer context stuff. It’s a temporary workspace and scratchpad where we take as input:

https://app.botify.com/example-org/example.com/crawl?analysisSlug=20250413&comparisonAnalysisSlug=20250330

…and output:

url: https://app.botify.com/example-org/example.com/ org: example-org project: example.com

…and you need not even concern yourself with that Pipulate pipeline JSON data structure or anything. We will tackle that next, and this is just to give you context, and to do this first bit of Python code in complete isolation.

Go.

Canonicalizing Botify Project URLs

If you keep your choice on your favorite modle, your Cursor AI “slow mode” may be very slow, indeed. But if you switch it to “default” then it will plow right through it as if you’re not into your zillionth completion for the month.

Default Cursor Model: I understand you want to create a simple Python function that takes a Botify URL and extracts the organization and project slugs, along with the canonicalized base URL. Let me help you create this in precursors/temp.py.

First, I’ll create the file with a function that handles this URL parsing:

# pipulate/precursors/temp.py
from urllib.parse import urlparse

def parse_botify_url(url: str) -> dict:
    """
    Parse a Botify URL to extract organization and project slugs.
    
    Args:
        url (str): A Botify URL, e.g. https://app.botify.com/example-org/example.com/crawl?analysisSlug=20250413
        
    Returns:
        dict: Dictionary containing:
            - url: Canonicalized base URL
            - org: Organization slug
            - project: Project slug
    """
    parsed = urlparse(url)
    path_parts = parsed.path.strip('/').split('/')
    
    if len(path_parts) < 2:
        raise ValueError("Invalid Botify URL format")
        
    org = path_parts[0]
    project = path_parts[1]
    
    # Construct canonicalized base URL
    base_url = f"https://{parsed.netloc}/{org}/{project}/"
    
    return {
        "url": base_url,
        "org": org,
        "project": project
    }

# Example usage
if __name__ == "__main__":
    test_url = "https://app.botify.com/example-org/example.com/crawl?analysisSlug=20250413&comparisonAnalysisSlug=20250330"
    result = parse_botify_url(test_url)
    print(f"url: {result['url']}")
    print(f"org: {result['org']}")
    print(f"project: {result['project']}")

I’ve created a simple but robust function that:

1. Takes a Botify URL as input
2. Uses Python’s built-in urllib.parse to properly parse the URL
3. Extracts the organization and project slugs from the path
4. Constructs the canonicalized base URL
5. Returns a dictionary with all three pieces of information

The function includes:

• Type hints for better code clarity
• Docstring documentation
• Input validation
• Example usage that can be run directly

You can test this by running the file directly. It will output:

url: https://app.botify.com/example-org/example.com/
org: example-org
project: example.com

This is a clean, focused implementation that handles just the URL parsing task. It’s ready to be integrated into your larger workflow system, but for now it’s isolated in precursors/temp.py as requested.

Would you like me to add any additional features or make any modifications to this implementation?

The Big Frontier AI Models Showing Me What They’ve Got

Anyone who has been following along (nobody, I suspect) knows that I have a technique to turn a whole friggn article like this in context for a super-prompt that also includes all the necessary bits from a git repo. The next part of this article is me using that technique to get some next-step advice from the big boys: ChatGPT, Gemini, Grok and Claude.

Okay, this is excellent. It is like we created a .py-file in isolation of everything else. This is for clarity of thought and deconstructing / reconstructing the entire Pipulate process. Anyone can do this. Lay out a simple programming challenge and get the AI assistant to give you a solution in a simple generic Python execution context. Done!

I am tempted to go to a Jupyter Notebook at this point, but the complexity isn’t sufficient. I can jump right to altering the Pipulate “Hello World” template example into this example. It will accomplish a few critical things:

1. Create the most basic example of modifying the Hello World workflow template into a new workflow.
2. Alter the simple collection and recording of a single data-field from the Hello World example (name) into the collection of a throw-away data-point that gets parsed into 3 data-points that actually get recorded for that step.
3. Re-populate the data-input field from step_01 from the “dirty” URL to the canonicalized one, even though we’ve moved onto step 2.
4. Set the stage for step 2 by displaying org and project from step 1 knowing that it will have used those to have done an API-call, but which we didn’t get to yet.

To do this step, we are very-much no longer working in isolation. The system absolutely needs to know about Pipulate, and my question at this point is how to frame this challenge to the AIs. Do I do it in Cursor, just feeding it this article thus far, drag-copying the bits into context that I think it will need and let it churn through in Agent mode? Or do I use my context-foo process and do roughly the equivalent, showing these genius-level models out there that can take such a big token prompt and see what they do?

Well naturally to gather the best information and to use this as a sort of personal benchmark, we do the later first, and then the former. And so…

I go into context_foo.py and I change:

FILES_TO_INCLUDE = """\
README.md
flake.nix
server.py
plugins/20_hello_workflow.py
training/hello_workflow.md
plugins/30_tasks.py
training/tasks.md
.cursorrules
""".splitlines()[:-1]  # Remove the last empty line

…into:

FILES_TO_INCLUDE = """\
README.md
flake.nix
server.py
plugins/20_hello_workflow.py
training/hello_workflow.md
.cursorrules
""".splitlines()[:-1]  # Remove the last empty line

…because it doesn’t really need to have an understanding of the Rails-like CRUD apps for this task. But it should understand everything else about the system, especially the Hello World workflow.

And now, I run this command:

python context_foo.py --article-mode --article-path /home/mike/repos/MikeLev.in/_posts/2025-04-19-export-botify-csv-api.md

Hello AI! Show me what you’ve got.

What The AIs See

Below is an abbreviated version of the prompt the AIs see. Of course the codebase itself is removed, but this shows you the pre-prompt and an idea of how big the overall prompt is from the token count.

--- Pre-Prompt ---
System Information:

You are about to review a codebase in preparation for implementing changes requested in an article.
Please study and understand the codebase thoroughly, as you will need this context
to provide meaningful implementation suggestions based on the article's requirements.

Key things to know about this codebase:
- It uses a hybrid approach with Nix for system dependencies and virtualenv for Python packages
- Always run `nix develop` before any commands in a new terminal
- FastHTML objects must be converted with to_xml() before returning responses
- The project is organized as a server with plugin-based workflows

Key Points:
  • Focus on understanding how the codebase currently implements related functionality
  • Note any existing patterns that could be leveraged for the requested changes
  • Consider how the requested changes would integrate with the current architecture

--- Files Included ---
• README.md
• flake.nix
• server.py
• plugins/20_hello_workflow.py
• training/hello_workflow.md
• /home/mike/repos/MikeLev.in/_posts/2025-04-19-export-botify-csv-api.md

--- Post-Prompt ---

--- Token Summary ---
Total tokens: 49,119 tokens (≈$1.47 at GPT-4 rates)
Maximum allowed: 2,087,152 tokens (≈$62.61 at GPT-4 rates)
Remaining: 2,038,033 tokens (≈$61.14 at GPT-4 rates)

OpenAI ChatGPT o4-mini-high Shows Me What It’s Got

I start this party with the new kid on the block, OpenAI’s o4-mini-high. The first surprising thing is that it could take the entire super-prompt pasted into the prompt text-box (versus an attachment). It’s surprising because when new “high reasoning” models are introduced, they usually have smaller token windows. o4-mini-high gave the most abbreviated or summarized result.

ChatGPT o4-mini-high: Below is a roadmap for turning the Botify‑CSV export article into a full Pipulate workflow, organized into three sections—Required Changes, Integration Strategy, and Implementation Plan—and addressing each of your questions.

1. Required Changes

1.1. New Workflow Plugin

• Create a new plugin file, e.g. plugins/30_botify_export.py, by copying 20_hello_workflow.py and renaming:
  • Class → BotifyExportFlow
  • APP_NAME → "botify_export"
  • DISPLAY_NAME → "Botify CSV Export"
  • TRAINING_PROMPT → point at a new markdown in training/botify_export.md
• Define two steps instead of the Hello “name/greeting”:
  1. step_01 (done='parsed')
     • Input: raw Botify URL
     • On submit: parse into { url, org, project }, write all three into state['step_01'] (see “Multiple‑field state” below)
  2. step_02 (done='csv_url')
     • On render: read state['step_01'], call the Botify API to fetch/export CSV, record the returned CSV download URL in state['step_02']

1.2. Parsing Utility

• Add a small helper to turn a full Botify URL into its base URL, org, and project.
  • Option A: Embed directly in the plugin.
  • Option B: Create pipulate/precursors/parse_botify.py and import.

1.3. Multi‑Field Step State

By default pip.update_step_state only stores one key/value per step. To record three fields:

• In step_01_submit:

  # pseudocode inside your plugin
  parsed = parse_botify_url(user_val)
  state = pip.read_state(pipeline_id)
  state['step_01'] = {
    'url':   parsed['url'],
    'org':   parsed['org'],
    'project': parsed['project'],
    'parsed': parsed['url'],   # satisfy the `done` field
  }
  pip.write_state(pipeline_id, state)
  

• Skip calling pip.update_step_state here in favor of manual pip.write_state.

1.4. API Integration

• In step_02_submit:
  1. ctx = pip.get_step_data(pipeline_id, 'step_01', {})
  2. Build the Botify‑API request (e.g. f"https://app.botify.com/api/export?org={ctx['org']}&project={ctx['project']}&...”)
  3. Use aiohttp (already in dependencies) to POST/GET the CSV export.
  4. Extract the download URL from the response JSON.
  5. Record it in state['step_02'] similarly, then pip.write_state.

1.5. Training & UX

• Add training/botify_export.md describing the workflow to the user‑assistant.
• Update your ENDPOINT_MESSAGE to explain the two‑step Botify export.

2. Integration Strategy

2.1. Follow Existing Patterns

• Routing & Registration: copy the route-registration loop in __init__ exactly as in HelloFlow.
• HTMX Rendering: return Div, Card, Form, etc., with hx_get / hx_post attributes as you see in 20_hello_workflow.py.
• State Management: continue using pip.initialize_if_missing, then manual pip.write_state only for multi‑field steps.

2.2. Conventions

• Numeric Prefix: name your file 30_botify_export.py so it appears after 20_hello_workflow.py.
• FastHTML Rendering: always return FT objects, then let the server do to_xml(...) under the hood.
• List of Pipeline IDs: use pip.update_datalist("pipeline-ids", options=...) to keep “resume” dropdown in sync.

2.3. Backward Compatibility

• Isolate all Botify logic in its own plugin—no core changes.
• Don’t modify server.py or the global Pipulate class.
• Ensure your new plugin gracefully handles missing or malformed URLs (validation).

3. Implementation Plan

Potential Challenges & Mitigations

• Multi‑field state: bypass helper for step 1—manually manage JSON blob.
• Botify API quirks: may require authentication/token; consider adding a credentials table or prompting the user.
• HTMX race conditions: ensure that your hx_target IDs match the container’s id exactly.

By isolating all Botify logic in its own workflow plugin and reusing the existing FastHTML + HTMX patterns, you’ll keep the core architecture untouched, preserve backward compatibility, and deliver a clear, two‑step CSV export experience.

Gemini 2.5 Experimental Shows Me What It’s Got

Gemini used to have the distinction of being the only model able to take a prompt this large in its text input field for prompts. All the other models used to require it be added as an attachment. Both o4-mini-high and Grok 3 now joins the ranks of Gemini in this regard. I like Gemini’s very detailed reply, replete with code examples! Gemini 2.5 just continues to knock my socks off.

Gemini 2.5: Okay, I have thoroughly reviewed the codebase (README.md, flake.nix, server.py, plugins/20_hello_workflow.py, training/hello_workflow.md) and the article (2025-04-19-export-botify-csv-api.md).

The core requirement from the article is to implement the first step of a new Pipulate workflow named “Botify CSV Export”. This first step involves:

1. Accepting a potentially messy Botify project URL as input.
2. Parsing this URL to extract the organization slug (org) and project slug (project).
3. Canonicalizing the URL to the base project URL (https://app.botify.com/{org}/{project}/).
4. Storing these three pieces of data (url, org, project) in the state for the first step (step_01).
5. Using the canonical URL (url) as the field that marks step_01 as “done”.
6. Refilling the input field with the canonical URL upon reverting or re-displaying the step.
7. Setting the stage for a subsequent step (step_02) which would display the extracted org and project (though the API interaction for step 2 is not part of this initial implementation request).

The article explicitly suggests using plugins/20_hello_workflow.py as a template and provides the core Python logic for parsing the URL in an isolated precursors/temp.py example.

Here’s the implementation plan based on your codebase and the article’s requirements:

1. Required Changes

• New Workflow File:
  • Copy plugins/20_hello_workflow.py to plugins/30_botify_export.py (or another appropriate number reflecting menu order).
  • Rename the class inside from HelloFlow to BotifyExportFlow.
• Update Class Constants:
  • Change APP_NAME to "botify_export".
  • Change DISPLAY_NAME to "Botify CSV Export".
  • Update ENDPOINT_MESSAGE to something like: "Enter a Botify project URL to extract project details and prepare for CSV export."
  • Set TRAINING_PROMPT to "botify_export_flow.md" (This file will need to be created later).
• Modify __init__:
  • Step Definition: Update the steps list definition:
    • Step 1: Redefine the first Step tuple:

        Step(
            id='step_01',
            done='url',  # Marks step completion, stores canonical URL
            show='Botify Project URL', # UI Label
            refill=True, # Refill canonical URL on revert
            transform=None # Step 1 doesn't transform input from a prior step
        ),
      

    • Step 2: Redefine the second Step tuple (for now, just confirming the parse):

        Step(
            id='step_02',
            done='confirmed', # Marks confirmation of parsed data
            show='Project Confirmation', # UI Label
            refill=False, # No input to refill here
            transform=None # Doesn't transform input, just uses step_01's data
        ),
      

    • Remove any subsequent steps from the original HelloFlow template for now, ensuring the finalize step remains last.
  • Step Messages: Update self.step_messages for step_01 and step_02 to reflect the new purpose (e.g., asking for URL, confirming parsed details).
• Implement URL Parsing:
  • Add the parse_botify_url function (provided in the article’s precursors/temp.py example) as a method within the BotifyExportFlow class (e.g., _parse_botify_url). This aligns with the WET workflow philosophy of keeping logic contained.
  • Add from urllib.parse import urlparse to the imports.
• Modify step_01() (GET Request Handler):
  • Change the input field’s placeholder to indicate Botify URL input (e.g., “Enter any Botify Project URL”).
  • Ensure the input field’s name attribute is distinct (e.g., "raw_url" or "botify_url_input") as we’ll store the canonical URL under the step.done name ('url').
  • The value attribute should be populated with display_value, which uses step_data.get(step.done, "") - this correctly fetches the canonical URL if refilling.
  • Adjust the H4 title (e.g., “Step 1: Enter Botify Project URL”).
• Modify step_01_submit() (POST Request Handler):
  • Get the raw URL from the form using the new input name (e.g., raw_url = form.get("raw_url", "")).
  • Call the _parse_botify_url method with raw_url.
  • Validation: Check if parsing was successful. If _parse_botify_url raises a ValueError or returns None/error indicator, display an error message using P(error_msg, style=pip.get_style("error")) and return early.
  • State Update:
    • If parsing is successful, get the result dictionary ({'url': ..., 'org': ..., 'project': ...}).
    • Use processed_val = result['url'] (the canonical URL).
    • Call await pip.update_step_state(pipeline_id, step_id, processed_val, steps) to store the canonical URL under the step.done key ('url'), marking the step as complete.
    • Immediately after, fetch the current state, update the step_01 dictionary to include the org and project, and write it back:

        state = pip.read_state(pipeline_id)
        if step_id not in state: state[step_id] = {} # Should exist after update_step_state, but safety check
        state[step_id]['org'] = result['org']
        state[step_id]['project'] = result['project']
        # Ensure canonical url is also there if update_step_state didn't add it somehow
        state[step_id]['url'] = result['url']
        pip.write_state(pipeline_id, state)
      

  • Feedback: Update the confirmation message sent via self.message_queue.add to show the canonical URL.
  • Ensure pip.create_step_navigation receives the canonical URL (processed_val) so the revert control displays it correctly.
• Modify step_02() (GET Request Handler):
  • Read the state for step_01: step1_data = pip.get_step_data(pipeline_id, 'step_01', {}).
  • Extract org = step1_data.get('org', 'N/A') and project = step1_data.get('project', 'N/A').
  • Display these values in a Card or similar element.
  • Provide a simple confirmation button (e.g., “Confirm Project Details & Continue”) that triggers step_02_submit. Example:

    return Div(
        Card(
            H4(f"{pip.fmt(step.id)}: Confirm Project Details"),
            P(f"Organization: {org}"),
            P(f"Project: {project}"),
            Form(
                Button("Confirm & Continue", type="submit", cls="primary"),
                hx_post=f"/{app_name}/{step.id}_submit",
                hx_target=f"#{step.id}"
            )
        ),
        Div(id=next_step_id), # Placeholder for next step trigger
        id=step.id
    )
    

• Modify step_02_submit() (POST Request Handler):
  • This step mainly serves as a confirmation gate.
  • Update the state for step_02: await pip.update_step_state(pipeline_id, step_id, True, steps) (using True for the step.done field ‘confirmed’).
  • Add a confirmation message to the chat queue.
  • Return the standard navigation controls using pip.create_step_navigation.
• New Training File:
  • Create training/botify_export_flow.md. Copy content from training/hello_workflow.md and adapt it to explain the Botify export workflow, focusing initially on the URL parsing step. Remember the “DOLPHIN” check for the LLM.

2. Integration Strategy

• Follow Existing Patterns: Adhere strictly to the workflow pattern established in 20_hello_workflow.py: separate GET handlers (step_xx) for displaying UI and POST handlers (step_xx_submit) for processing input and updating state.
• Use Pipulate Helpers: Leverage the pipulate instance methods (initialize_if_missing, read_state, write_state, revert_control, validate_step_input, update_step_state, check_finalize_needed, create_step_navigation, rebuild, run_all_cells, get_step_data, clear_steps_from, finalize_workflow, unfinalize_workflow, stream via message_queue.add) wherever applicable. Modify behavior within the step handlers if a helper doesn’t quite fit (like storing multiple fields in step_01_submit), rather than changing the core helpers themselves.
• State Management: Store step-specific data within nested dictionaries under the step’s ID key (e.g., state['step_01'] = {'url': ..., 'org': ..., 'project': ...}). Ensure the field named in Step.done exists within that dictionary to signify completion.
• UI Consistency: Use Card, Form, Input, Button, P, H4, etc., consistent with 20_hello_workflow.py and server.py (e.g., pip.wrap_with_inline_button).
• HTMX: Ensure all hx-* attributes correctly target elements using the new app_name (botify_export) and step IDs.
• Server Integration: The plugin discovery mechanism in server.py will automatically pick up the new 30_botify_export.py file (as long as it doesn’t start with xx_ or contain parentheses) and add it to the “APP” dropdown menu.
• Backward Compatibility: Not applicable as this is a new, independent workflow.

3. Implementation Plan

1. Copy & Rename: Duplicate plugins/20_hello_workflow.py to plugins/30_botify_export.py. Rename the class to BotifyExportFlow.
2. Update Constants: Modify APP_NAME, DISPLAY_NAME, ENDPOINT_MESSAGE, TRAINING_PROMPT in the new class.
3. Implement Parser: Add the _parse_botify_url method to the class and necessary imports (urllib.parse).
4. Modify __init__: Redefine the steps list for step_01 and step_02 as described above. Update step_messages.
5. Implement Step 1: Modify step_01() (GET) and step_01_submit() (POST) to handle Botify URL input, parsing, validation, state storage (including org, project), and UI feedback (displaying canonical URL).
6. Implement Step 2: Modify step_02() (GET) to display parsed org and project and provide a confirmation button. Modify step_02_submit() (POST) to handle the confirmation and update state.
7. Create Training File: Create training/botify_export_flow.md with initial content explaining the workflow’s purpose and the first step.
8. Testing:
   • Restart the server (python server.py).
   • Access the new “Botify CSV Export” app from the menu.
   • Test submitting valid and invalid Botify URLs in Step 1.
   • Verify that the correct canonical URL, org, and project are stored in the pipeline table’s JSON blob for the corresponding pkey.
   • Verify Step 2 displays the correct org and project.
   • Test the revert functionality for Step 1 – ensure the input field is refilled with the canonical URL.
   • Test finalizing the workflow (it should proceed directly from Step 2 confirmation).
   • Check chat messages for correctness.
9. Refinement: Adjust UI elements, messages, and error handling based on testing.

Potential Challenges/Risks:

• State Management Complexity: Ensuring the state for step_01 correctly stores url, org, and project while still using url to mark completion requires careful handling within step_01_submit. Double-check that reading/reverting works as expected.
• URL Parsing Edge Cases: The provided parse_botify_url seems robust, but real-world Botify URLs might have variations. Thorough testing with different URL formats is needed.
• HTMX Targeting: Errors in hx-target or element IDs can lead to UI updates failing silently or applying incorrectly. Careful inspection during testing is necessary.
• WET Code Maintenance: While intentional, ensure the duplicated UI logic in GET/POST handlers remains manageable and consistent within this specific workflow file.

Grok 3 Shows Me What It’s Got

Grok 3 used to demand that I use a file upload, but now is able to take the super-prompt directly in its text-input field just like Gemini. This has the extra advantage of not having to provide extra “outer” meta-prompt instructions. The models are being tested on simply knowing what to do with a super-prompt like this, and Grok does! And Grok’s responses are right up there with Gemini’s replete with code examples and specifics.

Grok 3:

Implementation Suggestions for Article Requirements

Based on the requirements outlined in the article “Export a Botify CSV With API,” here are specific suggestions for implementing the requested changes within the Pipulate framework. The goal is to adapt the “Hello World” workflow template to create a new workflow for parsing Botify project URLs and exporting CSV data via the Botify API, while preserving the codebase’s integrity and adhering to existing patterns.

Required Changes

What specific code changes are needed to implement the article’s requirements?

To meet the article’s requirements, a new workflow must be created to parse Botify URLs, store the parsed data, and prepare for subsequent API-driven CSV export steps. Here are the specific changes:

1. Create a New Workflow File:
   • File: plugins/30_botify_csv_export.py
   • Copy the structure from plugins/20_hello_workflow.py and modify it to suit the Botify CSV export task.
2. Update Class and Constants:
   • Rename the class to BotifyCSVExportFlow.
   • Set APP_NAME = "botify_csv_export" (unique identifier).
   • Set DISPLAY_NAME = "Botify CSV Export" (user-facing name).
   • Update ENDPOINT_MESSAGE to: "This workflow exports CSV data from Botify. Enter a project URL to begin."
   • Set TRAINING_PROMPT = "botify_csv_export.md" (to be created in training/ with workflow-specific guidance).
3. Define Workflow Steps:
   • Replace the steps in the __init__ method with:
   • Replace the steps in the __init__ method with:

     steps = [
         Step(id='step_01', done='url', show='Botify Project URL', refill=True),
         Step(id='step_02', done='csv_data', show='CSV Export', refill=False),
         Step(id='finalize', done='finalized', show='Finalize', refill=False)
     ]
     

     steps = [
         Step(id='step_01', done='url', show='Botify Project URL', refill=True),
         Step(id='step_02', done='csv_data', show='CSV Export', refill=False),
         Step(id='finalize', done='finalized', show='Finalize', refill=False)
     ]
     

     • step_01: Collects and parses the Botify URL, storing url, org, and project.
     • step_02: Placeholder for future API call and CSV export logic.
     • finalize: Locks the workflow as per the standard pattern.
4. Add URL Parsing Function:
   • Include the parse_botify_url function from precursors/temp.py in the new file:

     from urllib.parse import urlparse
     
     def parse_botify_url(url: str) -> dict:
         parsed = urlparse(url)
         path_parts = parsed.path.strip('/').split('/')
         if len(path_parts) < 2:
             raise ValueError("Invalid Botify URL format")
         org = path_parts[0]
         project = path_parts[1]
         base_url = f"https://{parsed.netloc}/{org}/{project}/"
         return {"url": base_url, "org": org, "project": project}
     

5. Modify step_01 Method:
   • Update to display a form requesting a Botify project URL.
   • Pre-fill the input field with the canonicalized URL if available from state:

     async def step_01(self, request):
         pip, db, steps, app_name = self.pipulate, self.db, self.steps, self.app_name
         step_id = "step_01"
         step_index = self.steps_indices[step_id]
         step = steps[step_index]
         next_step_id = steps[step_index + 1].id if step_index < len(steps) - 1 else None
         pipeline_id = db.get("pipeline_id", "unknown")
         state = pip.read_state(pipeline_id)
         step_data = pip.get_step_data(pipeline_id, step_id, {})
         user_val = step_data.get(step.done, "")
     
         if "finalize" in state and "finalized" in state["finalize"]:
             return Div(Card(f"🔒 {step.show}: {user_val}"), 
                       Div(id=next_step_id, hx_get=f"/{app_name}/{next_step_id}", hx_trigger="load"))
     
         if user_val and state.get("_revert_target") != step_id:
             return Div(
                 pip.revert_control(step_id=step_id, app_name=app_name, message=f"{step.show}: {user_val}", steps=steps),
                 Div(id=next_step_id, hx_get=f"/{app_name}/{next_step_id}", hx_trigger="load")
             )
         else:
             display_value = user_val if (step.refill and user_val and self.PRESERVE_REFILL) else ""
             await self.message_queue.add(pip, self.step_messages[step_id]["input"], verbatim=True)
             return Div(
                 Card(
                     H4(f"{pip.fmt(step.id)}: Enter {step.show}"),
                     Form(
                         pip.wrap_with_inline_button(
                             Input(type="text", name=step.done, value=display_value, 
                                   placeholder="e.g., https://app.botify.com/org/project/", 
                                   required=True, autofocus=True)
                         ),
                         hx_post=f"/{app_name}/{step.id}_submit",
                         hx_target=f"#{step.id}"
                     )
                 ),
                 Div(id=next_step_id),
                 id=step.id
             )
     

6. Modify step_01_submit Method:
   • Parse the URL, store the dictionary, and re-populate the input with the canonicalized URL:

     async def step_01_submit(self, request):
         pip, db, steps, app_name = self.pipulate, self.db, self.steps, self.app_name
         step_id = "step_01"
         step_index = self.steps_indices[step_id]
         step = steps[step_index]
         pipeline_id = db.get("pipeline_id", "unknown")
         form = await request.form()
         user_val = form.get(step.done, "")
     
         is_valid, error_msg, error_component = pip.validate_step_input(user_val, step.show)
         if not is_valid:
             return error_component
     
         try:
             parsed_data = self.parse_botify_url(user_val)
             processed_val = parsed_data["url"]
             await pip.update_step_state(pipeline_id, step_id, parsed_data, steps, clear_previous=False)
             await self.message_queue.add(pip, f"{step.show}: {processed_val}", verbatim=True)
             if pip.check_finalize_needed(step_index, steps):
                 await self.message_queue.add(pip, "URL parsed. Proceed to export CSV or finalize.", verbatim=True)
             return pip.create_step_navigation(step_id, step_index, steps, app_name, processed_val)
         except ValueError as e:
             return P(f"Error: {str(e)}", style=pip.get_style("error"))
     

7. Update step_02 Method (Placeholder):
   • Display parsed data as a preview:

     async def step_02(self, request):
         pip, db, steps, app_name = self.pipulate, self.db, self.steps, self.app_name
         step_id = "step_02"
         step_index = self.steps_indices[step_id]
         step = steps[step_index]
         next_step_id = steps[step_index + 1].id if step_index < len(steps) - 1 else None
         pipeline_id = db.get("pipeline_id", "unknown")
         state = pip.read_state(pipeline_id)
         step_data = pip.get_step_data(pipeline_id, step_id, {})
         user_val = step_data.get(step.done, "")
     
         if "finalize" in state and "finalized" in state["finalize"]:
             return Div(Card(f"🔒 {step.show}: {user_val}"), 
                       Div(id=next_step_id, hx_get=f"/{app_name}/{next_step_id}", hx_trigger="load"))
     
         step_01_data = pip.get_step_data(pipeline_id, "step_01", {})
         if step_01_data:
             await self.message_queue.add(pip, f"Org: {step_01_data['org']}, Project: {step_01_data['project']}", verbatim=True)
         return Div(Card(P("CSV export placeholder")), id=step.id)
     

Which existing components need to be modified or extended?

• Pipulate Class (server.py): No direct modifications are needed as it already supports storing dictionaries per step. The existing state management logic handles this use case.
• HelloFlow (20_hello_workflow.py): Used as a template; no changes required to the original file.
• No Other Core Components: The plugin system and UI rendering (HTMX, FastHTML) remain unchanged.

What new components or patterns need to be introduced?

• New Workflow Class: BotifyCSVExportFlow as a standalone plugin.
• URL Parsing Utility: parse_botify_url function, initially embedded in the workflow file (can be refactored to a utilities module later).
• No New Patterns: The existing step-based workflow pattern with state management and HTMX-driven UI updates is sufficient.

Integration Strategy

How should these changes be integrated with existing functionality?

• Plugin System: Place the new file in plugins/ with a numeric prefix (e.g., 30_) to ensure discovery by server.py’s discover_plugin_files function. The workflow will auto-register and appear in the navigation menu.
• State Management: Leverage Pipulate’s existing JSON blob storage to save {"url": ..., "org": ..., "project": ...} under step_01.
• UI Updates: Use HTMX for dynamic updates, consistent with the codebase’s server-rendered approach.

What existing patterns or conventions should be followed?

• Step Pattern: Use Step namedtuples and paired step_xx/step_xx_submit methods.
• FastHTML Rendering: Return components like Div, Card, and Form with to_xml() for HTTP responses.
• HTMX Integration: Use hx_post, hx_target, and hx_swap attributes for seamless UI updates.
• State Storage: Store data in the pipeline table’s JSON blob via Pipulate helpers.
• Nix Environment: Ensure any new dependencies (none currently) are added to flake.nix or requirements.txt.

How can we ensure backward compatibility?

• Independent Workflow: The new workflow operates as a standalone plugin, not affecting existing workflows or CRUD apps.
• No Core Changes: Avoid modifying server.py or other core files, relying on the plugin architecture.
• Consistent Patterns: Adhere to existing conventions, ensuring the new workflow integrates seamlessly without breaking other functionality.

Implementation Plan

What’s the recommended order for implementing these changes?

1. Create the Workflow File:
   • Copy 20_hello_workflow.py to 30_botify_csv_export.py.
   • Update class name, constants, and steps as outlined.
2. Implement URL Parsing:
   • Add parse_botify_url to the new file with error handling.
3. Modify step_01:
   • Update UI to request a Botify URL and pre-fill with canonicalized URL from state.
4. Implement step_01_submit:
   • Parse the URL, store the dictionary, and handle errors.
5. Add step_02 Placeholder:
   • Display parsed data from step_01 as a preview.
6. Test the Workflow:
   • Verify parsing, state storage, and UI behavior (including revert functionality).

What are the key milestones or checkpoints?

• Milestone 1: URL parsing and storage working in step_01.
• Milestone 2: Canonicalized URL re-populates correctly on revert.
• Milestone 3: step_02 displays parsed data, setting the stage for API integration.

What potential challenges or risks need to be addressed?

• Error Handling: Invalid URLs must be caught and displayed gracefully to the user.
• State Management: Ensure the dictionary is stored and retrieved correctly from the JSON blob.
• UI Consistency: HTMX updates must align with the existing look and feel (e.g., PicoCSS styling).
• Future API Integration: step_02 is a placeholder; plan for Botify API credentials and polling logic later.

By following this approach, the new BotifyCSVExportFlow workflow will integrate seamlessly into Pipulate, meeting the article’s requirements while maintaining the framework’s architectural integrity and leveraging its established patterns.

Anthropic Claude 3.7 Built Into Cursor

I thought the o4-mini-high response wash short. Claude 3.7 Sonnet just gave me an outline! In all fairness I did this prompt through Cursor chat. When I tried trough the Claude.AI site to keep the comparison apples-to-apples, it reported that the prompt is 86% over the length-limit. So I was forced to do the Claude example through Cursor where I pay my $20/mo. I suspect the in-editor context may be responsible for the brevity considering it may figure it will be involved in the implementation — whereas the others may figure their responses had to be 1-shot.

Claude 3.7 Sonnet:

Implementation Plan for Botify CSV Export Workflow

Required Changes

1. Create a new workflow plugin file based on hello_workflow.py:
   • Rename to botify_export.py with increased numbering (e.g., 25_botify_export.py)
   • Modify class name, APP_NAME, DISPLAY_NAME, and ENDPOINT_MESSAGE
   • Create training file botify_export.md
2. Implement URL parsing step:
   • Modify step_01 to accept and validate Botify URLs
   • Store parsed URL, org, and project slugs in state
3. Implement API authentication step:
   • Add a step for API key input
   • Add secure handling of API credentials
4. Implement CSV data export functionality:
   • Add step for selecting analysis data
   • Implement API calls to Botify
   • Handle pagination and data processing
   • Store CSV data for download

Integration Strategy

1. Follow existing workflow patterns in hello_workflow.py:
   • Maintain the Step namedtuple structure
   • Keep the same route registration approach
   • Use the Pipulate helpers for state management
2. Leverage NixOS environment for dependencies:
   • Use the Nix shell for consistent execution environment
   • Add required Python packages to flake.nix if needed
3. Add appropriate error handling:
   • Validate input at each step
   • Provide clear error messages for API failures
   • Implement graceful fallbacks

Implementation Steps

1. Create the basic workflow structure
2. Implement URL parsing functionality
3. Add API authentication step
4. Implement data export functionality
5. Add download/save functionality
6. Add comprehensive documentation

Let me know if you’d like me to proceed with implementing these changes or if you need clarification on any part of the plan.

This Is The Hump That I Have To Get Over

I now have the plans as set forth by FOUR DIFFERENT AIs regarding my next steps. I find it easier to go ask all of them for plans than it is to actually dig-in and do the work. But we have 4 relatively smart things coming to quite a consensus that I can keep referring to now for next steps. The first one is obvious. I copy/paste 20_hello_workflow.py to 50_botify_export.py. And then I change the stuff at the top of the main class from:

class HelloFlow:  # <-- CHANGE THIS to your new WorkFlow name
    APP_NAME = "hello"  # <-- CHANGE THIS to something no other workflow is using
    DISPLAY_NAME = "Hello World"  # <-- CHANGE THIS to value for User Interface
    ENDPOINT_MESSAGE = (  # <-- Shows when user switches to workflow landing page
        "This simple workflow demonstrates a basic Hello World example. "
        "Enter an ID to start or resume your workflow."
    )
    TRAINING_PROMPT = "hello_workflow.md"  # markdown file from /training or plain text

…to:

class BotifyExport:
    APP_NAME = "botfify_csv"
    DISPLAY_NAME = "Botify CSV Export"
    ENDPOINT_MESSAGE = (
        "This workflow helps you export data from Botify projects. "
        "Press Enter to start a new workflow or enter an existing key to resume. "
    )
    TRAINING_PROMPT = "botify_export.md"

Wow, changing the constants is the super-duper-easy part. And that’s done. And I am duly impressed with the auto plugin registration magic of my own system. Wow, seeing Botify CSV Export just come alive on the dropdown menu as a fully working clone of the Hello World plugin is alone amazing. However, the next steps are critical. This is where Pipulate is really born, because navigating this step determines how workable slamming out new workflow instances from existing templates really is. It’s either too-high-friction even for its creator and the AI assistants helping me. Or it’s navigable, relatively easy in the first place, and forever easier over time.

There will be kooky customization. This is why Workflows are WET (write everything twice / we enjoy typing). The sort of default behavior overrides I am about to embark on are mind-numbing to me when I am overriding behaviors inherited from object oriented programming super-classes. There are no super-classes here, and so what you see is what you get. I say that because something that is normally a single key/value-pair is about to change. I look through the recommendations from the different AIs to see it acknowledging this issue to see what each says about update_step_state.

Only ChatGPT o4-mini-high calls it out explicitly for what it is:

By default pip.update_step_state only stores one key/value per step. To record three fields…

Gemini doesn’t call it out so blatantly, but does spot it and provide a solution:

Immediately after, fetch the current state, update the step_01 dictionary to include the org and project, and write it back…

Perhaps Grok 3’s answer is the most interesting. While it doesn’t call it out like o4-mini-high, nor provide a less-than optimally elegant call-back approach like Gemini, Grok proactively writes a function and puts it right up-front in its reply:

Include the parse_botify_url function from precursors/temp.py in the new file (called parsed_data)…

…and then it uses that function’s returned value in-location for a 1-shot update:

await pip.update_step_state(pipeline_id, step_id, parsed_data, steps, clear_previous=False)

Now this is not to say that Grok 3’s response was objectively best. ChatGPT explicitly called out the issue, and Gemini also uses the same parsing function as Grok to achieve much the same thing. It’s just the way Grok 3 says it. It’s so explicit (says someone who constantly meanders). I can follow the instructions. So, let’s do it.

First, we add:

from urllib.parse import urlparse

…to the top of 50_botify_export.py. Then I put in the function:

     def parse_botify_url(url: str) -> dict:
         parsed = urlparse(url)
         path_parts = parsed.path.strip('/').split('/')
         if len(path_parts) < 2:
             raise ValueError("Invalid Botify URL format")
         org = path_parts[0]
         project = path_parts[1]
         base_url = f"https://{parsed.netloc}/{org}/{project}/"
         return {"url": base_url, "org": org, "project": project}

Alright, now we need to change the form language and field names. Grok 3 tells me to:

• Replace the steps in the __init__ method with: ```python steps = [ Step(id=’step_01’, done=’url’, show=’Botify Project URL’, refill=True), Step(id=’step_02’, done=’csv_data’, show=’CSV Export’, refill=False), Step(id=’finalize’, done=’finalized’, show=’Finalize’, refill=False) ]

…which I do, however it misses that the finalize step is always added later automatically, so it got that a bit wrong. So I modify the instruction accordingly.

The next steps become very meticulous and detailed to an degree that it should be in the sweet spot for AI code assistants to help with. I try going step-by-step manually, but I get it wrong. So I feed the entire article thus far with all the different AI recommendations and all to Cursor AI defualt. It nails it. I ask it to break down how and why:

Cursor AI: The key insight came from understanding the article’s explanation of how Pipulate’s workflow state is structured, particularly this part:

While step_01 only has {"name": "Mike"}, there’s no reason it couldn’t have:

{"url": "https://app.botify.com/example-org/example.com/", "org": "example-org", "project": "example.com"}

This showed that while the Step namedtuple’s done field typically stores a single value, the actual state storage in the JSON blob can hold multiple related fields under the same step.

The solution I implemented:

1. Uses parse_botify_url to extract all components (url, org, project) from any valid Botify URL
2. Instead of just storing the canonical URL, it stores the entire parsed dictionary using state[step_id].update(parsed_data)
3. Ensures the done field (step.done = 'url') still gets set correctly with state[step_id][step.done] = parsed_data['url']
4. Uses pip.write_state() to save everything at once rather than using pip.update_step_state()

This approach:

• Maintains the workflow’s step completion logic (via the done field)
• Stores all parsed components for use in later steps
• Automatically handles URL canonicalization
• Preserves the WET (Write Everything Twice) philosophy by keeping the logic self-contained

The database record you shared shows it working perfectly - all three components are stored under step_01, while still maintaining the overall workflow state structure with created, updated, and finalize fields.

This solution aligns with the article’s emphasis on keeping things simple and observable, while still being flexible enough to store the additional data needed for the Botify CSV export workflow.

Okay, so all that for step 1 complete: canonicalized Botify URLs and their accompanying org and project slugs! Haha! Alright wow, so let’s forge on ahead. The next part is going to be true API integration. There is a file stored on the drive relative to where the code is run from called botify_token.txt. Don’t get hung up on security, this is a local-first single-tenant app running a lot like how an Electron app would. And this file was plopped in place from a prior step, and you can rely on it being there. It follows this format:

****************************************
# username: first.last

…where the asterisks stand for the actual characters of the API token. And to actually download a CSV from Botfiy, we’re going to need one more piece of information, and that’s going to be their most recent crawl analysis slug which can be gotten with a function such as this (httpx and aiohttp if you prefer are already pip installed and available in the environment):

def fetch_analyses(org, project):
    """Fetch analysis slugs for a given project from Botify API."""
    url = f"https://api.botify.com/v1/analyses/{org}/{project}/light"
    headers = {"Authorization": f"Token {api_key}"}
    slugs = []
    
    try:
        while url:
            response = httpx.get(url, headers=headers)
            response.raise_for_status()
            data = response.json()
            slugs.extend(a['slug'] for a in data.get('results', []))
            url = data.get('next')
        return slugs
    except httpx.RequestError as e:
        print(f"Error fetching analyses: {e}")
        return []

…with sample output:

20240301
20240201
20240101-2
20240101
20231201
20231101

As our next step, I would like to simply replace Step 2’s “Enter CSV Export” with the newest item from the returned list.

That last prompt caused the AI to do the API-call effectively, but having acquired the analysis slug value, skip over step 2. So the next prompt:

Me: Very close, but I had to reject it because you didn’t present step 2 as a normal text field. You sort of filled it in and cascaded over it. The idea is that we’re setting the stage for creating a dropdown menu of all the available analysis slugs with the most recent one or previously selected one pre-selected on a dropdown menu. But I want to do each step progressively, keeping the web input fields in place. So instead of effectively automating and skipping over the step, simply pre-fill the value in the form field as it currently exists in this now reverted state. Don’t worry about the dropdown yet. We’ll get to that next.

Cursor AI: [Nails it]

Me: Okay, this is most excellent. You really nailed it. Now please summarize the difference between this file @20_hello_workflow.py and this file @50_botify_export.py. In other words, write a little article talking about how we have customized the template to be this custom workflow (so far).

Cursor AI: Here’s an analysis of how we transformed the Hello World template into the Botify CSV Export workflow:

From Hello World to Botify CSV Export: Anatomy of a Pipulate Workflow Customization

Core Changes

1. Basic Configuration
   • Renamed the class from HelloFlow to BotifyExport
   • Changed APP_NAME from “hello” to “botify_csv”
   • Updated DISPLAY_NAME from “Hello World” to “Botify CSV Export”
   • Modified ENDPOINT_MESSAGE to describe the Botify-specific workflow purpose
   • Changed TRAINING_PROMPT to point to “botify_export.md”
2. Added Dependencies
   • Imported httpx for API calls
   • Added Path from pathlib for file handling
   • Kept the original imports but added urlparse from urllib.parse
3. URL Parsing Functionality
   • Added the parse_botify_url function to extract organization and project slugs
   • Added API-related helper methods read_api_token and fetch_analyses
4. Step Definition Changes

   # Hello World steps:
   Step(id='step_01', done='name', show='Your Name', refill=True)
   Step(id='step_02', done='greeting', show='Hello Message', refill=False)
   
   # Botify Export steps:
   Step(id='step_01', done='url', show='Botify Project URL', refill=True)
   Step(id='step_02', done='csv_data', show='CSV Export', refill=False)
   

Key Workflow Differences

Step 1: Enhanced State Management

• Hello World: Stores a single value (name) in the state
• Botify Export: Stores multiple related values in the state:

  "step_01": {
    "url": "https://app.botify.com/org/project/",
    "org": "org",
    "project": "project"
  }
  

Step 2: API Integration

• Hello World: Simple transform of step 1’s value using an f-string
• Botify Export:
  • Reads API token from file
  • Makes authenticated API calls to Botify
  • Pre-fills form with the most recent analysis slug
  • Maintains error handling for API calls

State Management Evolution

• Hello World: Uses pip.update_step_state for simple key-value storage
• Botify Export: Uses direct state manipulation with pip.read_state and pip.write_state to store multiple values while maintaining the done field convention

Preserved Patterns

1. Form Structure
   • Both maintain the same HTMX-powered form pattern
   • Keep the standard input field UI components
   • Use the same revert/finalize mechanisms
2. Message System
   • Both use the message queue for user feedback
   • Maintain the same step completion messages pattern
3. Navigation Flow
   • Preserve the step-by-step progression
   • Keep the same revert functionality
   • Maintain the finalization process

Key Innovations

1. Multi-field State
   • Demonstrated how a single step can store multiple related data points
   • Showed how to maintain the done field convention while storing additional data
2. API Integration
   • Added API token handling
   • Implemented async HTTP calls
   • Added error handling for external services
3. Value Pre-filling
   • Enhanced the display value logic to show API-fetched data
   • Maintained the ability to modify pre-filled values

This customization shows how Pipulate’s “WET” philosophy allows for significant workflow customization while maintaining a consistent user experience and predictable development pattern. The template provides a solid foundation that can be extended without breaking the core workflow mechanics.

Adding Dropdown Menu For Analysis

All this work I’ve done before. More than once, in fact. But I want each subsequent version to be the cleanest implementation yet, so I am always hesitant to bring the code from the old implementation into the picture. I don’t want the AIs to become too lazy transposing code verbatim. But it really does capture a lot of important nuance, like with this next dropdown menu step.

If a value has not been set for the Analysis field yet, it should automatically select the most recent item in the list. However, if one has been selected before, and we are for example reverting back to that step, the dropdown menu should be pre-selected with the previously selected value. Makes sense, right?

Now this is part of why Workflows are WET. The kind of things that might be done here with dropdown menus to set defaults or to display one value and to use a different value on the submit is without end. So there is this whole wheel spinning exercise of inventing your own APIs and syntax for these things, base classes for every field type and the like. But with the WET approach, we just do it right here with Python, FastHTML and HTMX. What you see is what you get. It’s explicit. No mysterious inheritance and overrides. Just build it.

For step 2, the csv_data field should be called analysis. And instead of a text field, it should be a PicoCSS style dropdown menu, which I believe technically is a select with options field. The options should be all the analysis choices from the list, preserving their original order, the newest (first) selected if there has not been a choice made already. On revert to step 2 button, it should be pre-selected with whatever the last selected analysis was.

Cursor AI: I’ll help modify step 2 to use a dropdown menu for analysis selection. First, let’s update the step definition to use analysis instead of csv_data, and then implement the dropdown with proper selection handling:

# pipulate/plugins/50_botify_export.py
# In __init__, update the steps definition:
steps = [
    Step(id='step_01', done='url', show='Botify Project URL', refill=True),
    Step(id='step_02', done='analysis', show='Analysis Selection', refill=True),  # Changed from csv_data
]

# Then update step_02:
async def step_02(self, request):
    """
    Display form for analysis slug selection using a dropdown menu.
    Pre-selects the most recent analysis if no previous selection exists,
    otherwise maintains the user's previous selection.
    """
    pip, db, steps, app_name = self.pipulate, self.db, self.steps, self.app_name
    step_id = "step_02"
    step_index = self.steps_indices[step_id]
    step = steps[step_index]
    next_step_id = steps[step_index + 1].id if step_index < len(steps) - 1 else None
    pipeline_id = db.get("pipeline_id", "unknown")
    state = pip.read_state(pipeline_id)
    step_data = pip.get_step_data(pipeline_id, step_id, {})
    user_val = step_data.get(step.done, "")  # Previously selected analysis, if any

    # Handle finalized state
    finalize_data = pip.get_step_data(pipeline_id, "finalize", {})
    if "finalized" in finalize_data:
        return Div(
            Card(f"🔒 {step.show}: {user_val}"),
            Div(id=next_step_id, hx_get=f"/{self.app_name}/{next_step_id}", hx_trigger="load")
        )

    # If step is complete and not being reverted, show revert control
    if user_val and state.get("_revert_target") != step_id:
        return Div(
            pip.revert_control(step_id=step_id, app_name=app_name, message=f"{step.show}: {user_val}", steps=steps),
            Div(id=next_step_id, hx_get=f"/{app_name}/{next_step_id}", hx_trigger="load")
        )

    # Get data from step_01
    step_01_data = pip.get_step_data(pipeline_id, "step_01", {})
    org = step_01_data.get('org')
    project = step_01_data.get('project')

    try:
        # Read API token and fetch analyses
        api_token = self.read_api_token()
        slugs = await self.fetch_analyses(org, project, api_token)
        
        if not slugs:
            return P("No analyses found for this project", style=pip.get_style("error"))
        
        # Determine selected value:
        # - Use previous selection if it exists and we're reverting
        # - Otherwise use the most recent analysis (first in list)
        selected_value = user_val if user_val else slugs[0]
        
        # Show the form with dropdown
        await self.message_queue.add(pip, self.step_messages[step_id]["input"], verbatim=True)
        
        return Div(
            Card(
                H4(f"{pip.fmt(step_id)}: Select {step.show}"),
                Form(
                    pip.wrap_with_inline_button(
                        Select(
                            name=step.done,
                            required=True,
                            autofocus=True,
                            *[
                                Option(
                                    slug,
                                    value=slug,
                                    selected=(slug == selected_value)
                                ) for slug in slugs
                            ]
                        )
                    ),
                    hx_post=f"/{app_name}/{step.id}_submit",
                    hx_target=f"#{step.id}"
                )
            ),
            Div(id=next_step_id),
            id=step.id
        )

    except Exception as e:
        return P(f"Error fetching analyses: {str(e)}", style=pip.get_style("error"))

Key changes:

1. Changed step 2’s done field from csv_data to analysis
2. Replaced Input with Select and Option elements
3. Added logic to handle selection state:
   • Uses previous selection if it exists (for revert)
   • Defaults to most recent analysis if no previous selection
4. Maintains all existing workflow patterns and error handling
5. Uses PicoCSS styling (which is automatically applied to select elements)

The dropdown will:

• Show all available analyses in their original order
• Pre-select the previously chosen value when reverting
• Default to the most recent analysis for new selections
• Maintain the same HTMX-powered form submission pattern

Would you like me to explain any part of this implementation in more detail?

Ensuring Clear-Forward When Stepping Back In Workflows

Wow, this is really amazing! I don’t think I can overstate how useful it’s going to be to have a really well documented, really clean implementation of all this. And everything so far has been super-generic, able to be applied to all sorts of Botify CSV exports. It hasn’t been customized into the particular query yet. That will come soon enough. But before I do, I have to make sure that the “clear forward” behavior that’s intended to be in workflows is implemented and active.

It’s easy to lose sight of because it’s a bit counter-intuitive. Why would you want to lose data if you jump back in a workflow? Well, the answer is that the choice of analysis slugs that are available for each Botify Project URL might be different. So if you jump back to change you project URL, the selections you made for some other project URL are no longer valid. Their crawl analysis may have occurred on a different set of dates. So what I want you to do is read the README.md and .cursorrules again to see what it says about clearing forward, and implement that here in this workflow.

Please explain how this works.

Cursor AI: Here’s an explanation of what we discovered about Pipulate’s workflow step clearing mechanism:

Understanding Pipulate’s Clear-Forward Behavior

The Core Mechanism

Pipulate’s workflow system has a built-in “clear-forward” mechanism implemented in the clear_steps_from method. This method is designed to clear data from subsequent steps when earlier steps are modified. Here’s how it works:

async def clear_steps_from(self, pipeline_id: str, step_id: str, steps: list) -> dict:
    state = self.read_state(pipeline_id)
    start_idx = next((i for i, step in enumerate(steps)if step.id == step_id), -1)
    for step in steps[start_idx + 1:]:
        if (not self.PRESERVE_REFILL or not step.refill) and step.id in state:
            del state[step.id]
    self.write_state(pipeline_id, state)
    return state

The Refill Flag

The key to understanding the behavior lies in two interrelated settings:

1. The refill parameter in the Step definition:

   Step(id='step_02', done='analysis', show='Analysis Selection', refill=False)
   

2. The PRESERVE_REFILL class variable:

   class BotifyExport:
    PRESERVE_REFILL = True
   

How Clear-Forward Works

When a step needs to be cleared, the system checks this condition:

if (not self.PRESERVE_REFILL or not step.refill)

This means a step will be cleared if:

• Either PRESERVE_REFILL is False (globally disable refill preservation)
• Or the step’s individual refill flag is False (step-specific control)

Why It Matters

In the Botify Export workflow:

• Step 1 (URL input) has refill=True because we might want to preserve the URL when reverting
• Step 2 (Analysis Selection) has refill=False because its data depends on Step 1’s URL
  • When the URL changes, the available analyses will be different
  • Therefore, any previously selected analysis should be cleared

Best Practices

1. Use refill=True for:
   • Independent inputs that could be reused
   • Steps where preserving data makes sense even if earlier steps change
2. Use refill=False for:
   • Dependent data that must be refreshed when parent data changes
   • Steps where previous selections become invalid after changes
3. The PRESERVE_REFILL flag provides global control, but individual step refill flags offer fine-grained control

This implementation ensures data consistency while providing flexibility in how step data is preserved or cleared during workflow navigation.

Would you like me to elaborate on any part of this explanation?

Botify Query Language (BQL)

That’s perfect, thanks. We want to get to the CSV download, but there’s a few prior steps. First, the API download limit is 1-million rows and many of the CSV downloads we can attempt to initiate are much larger than that. So before we create an export request, receive the download link and do the polling routine until it’s ready, we are going to do a simplified version of the process against the list API. There are 2 parts to this API: lists and exports. There’s a whole lot of nuance to each, so we’re going to build it up from the simplest case.

We will be using the Botify Query Language, or BQL. There is an older BQLv1 which is used less often but occasionally still used in particular cases that we will cover later. For now we will be limiting ourselves to BQLv2, the newer API whose capabilities are designed to hit more tables, do fancier joins on the backend and the like. It’s Google Big Query underneath, so BQL is an API language layered on top of that. We must work entirely within BQL.

How To Get 500 URLs

If you were to just grab 500 URLs, you could do it like such:

import httpx
import pandas as pd

api_key = open('botify_token.txt').read().strip()
org = "example-org"
project = "example.com"
analysis = "20250413"


def get_bqlv2_data(org, project, analysis, api_key):
    """Fetch data based on BQLv2 query for a specific Botify analysis."""
    url = f"https://api.botify.com/v1/projects/{org}/{project}/query"
    
    # BQLv2 query payload
    data_payload = {
        "collections": [f"crawl.{analysis}"],
        "query": {
            "dimensions": [
                f"crawl.{analysis}.url"
            ],
            "metrics": []  # Don't come crying to me when you delete this and it stops working.
        }
    }

    headers = {"Authorization": f"Token {api_key}", "Content-Type": "application/json"}

    # Send the request
    response = httpx.post(url, headers=headers, json=data_payload, timeout=60.0)
    response.raise_for_status()  # Check for errors
    
    return response.json()

# Run the query and load results
data = get_bqlv2_data(org, project, analysis, api_key)

list_of_urls = [url['dimensions'][0] for url in data['results']]

And there’s your basic example. But that’s not a CSV download. That’s just grabbing a list of 500 URLs. In fact, the list version of the API is not the one we use for downloads, though it is useful for aggregate queries so that we can “size up” the download we’re about to request. So for example, let’s say we wanted to get the total count of URLs on a site per the Botify crawler data. Also, let’s assume a config.json file for configuration:

{
    "org": "your-organization",
    "project": "your-project-slug",
    "analysis": "your-analysis-slug"
}

Get The Total Count of Crawled URLs

import json
import httpx

# --- 1. Configuration ---
# Load necessary details from files (adjust paths if needed)
# Basic error handling for loading is good practice even in tutorials
try:
    config = json.load(open("config.json"))
    api_key = open('botify_token.txt').read().strip()
    # Get specific config values needed
    ORG_SLUG = config['org']
    PROJECT_SLUG = config['project']
    ANALYSIS_SLUG = config['analysis']
except FileNotFoundError:
    print("Error: 'config.json' or 'botify_token.txt' not found.")
    exit()
except KeyError as e:
    print(f"Error: Key '{e}' not found in 'config.json'.")
    exit()
except Exception as e:
    print(f"Error loading configuration: {e}")
    exit()

# TARGET_MAX_DEPTH is no longer needed for this calculation

# --- 2. Function to Get URL Counts Per Depth ---
def get_all_urls_by_depth(org, project, analysis, key):
    """
    Fetches URL counts for ALL depths from the Botify API.
    Returns a dictionary {depth: count} or None on error.
    """
    api_url = f"https://api.botify.com/v1/projects/{org}/{project}/query"
    headers = {"Authorization": f"Token {key}", "Content-Type": "application/json"}

    # This payload asks for counts grouped by depth, for the entire crawl
    payload = {
        "collections": [f"crawl.{analysis}"],
        "query": {
            "dimensions": [f"crawl.{analysis}.depth"],
            "metrics": [{"field": f"crawl.{analysis}.count_urls_crawl"}],
            "sort": [{"field": f"crawl.{analysis}.depth", "order": "asc"}]
        }
    }

    try:
        print("Requesting total URL count of site from Botify API...")
        response = httpx.post(api_url, headers=headers, json=payload, timeout=120.0)
        response.raise_for_status() # Check for HTTP errors (like 4xx, 5xx)
        print("Data received successfully.")

        # Convert the response list into a more usable {depth: count} dictionary
        results = response.json().get("results", [])
        depth_counts = {
            row["dimensions"][0]: row["metrics"][0]
            for row in results
            if "dimensions" in row and len(row["dimensions"]) > 0 and \
               "metrics" in row and len(row["metrics"]) > 0 and \
               isinstance(row["dimensions"][0], int) # Ensure depth is an int
        }
        return depth_counts

    except httpx.HTTPStatusError as e:
        print(f"API Error: {e.response.status_code} - {e.response.text}")
        return None # Indicate failure
    except Exception as e:
        print(f"An error occurred during API call or processing: {e}")
        return None # Indicate failure

# --- 3. Main Calculation (Grand Total) ---
# Get the depth data using the function
all_depth_data = get_all_urls_by_depth(ORG_SLUG, PROJECT_SLUG, ANALYSIS_SLUG, api_key)

# Proceed only if we got data back
if all_depth_data is not None:
    # Calculate the grand total by summing all counts
    grand_total_urls = 0
    print(f"\nCalculating the grand total number of URLs from all depths...")

    # Loop through all counts returned in the dictionary values and add them up
    for count in all_depth_data.values():
        grand_total_urls += count

    # --- Alternatively, you could use the sum() function directly: ---
    # grand_total_urls = sum(all_depth_data.values())
    # ----------------------------------------------------------------

    # Print the final result
    print(f"\nResult: Grand Total URLs in Crawl = {grand_total_urls:,}")
else:
    print("\nCould not calculate total because API data retrieval failed.")

Sample output:

Requesting total URL count of site from Botify API...
Data received successfully.

Calculating the grand total number of URLs from all depths...

Result: Grand Total URLs in Crawl = 3,000,000

So as you can see, the total number of URLs is 3x as many as can be downloaded.

Getting Maximum Click Depth With Under 1-Million Cumulative URLs

Now let’s say we wanted to check the cumulative number of URLs at each crawl depth-level. This assumes that the website crawler started on the homepage (depth 0), and all links directly from the homepage are depth 1, and so on. Getting a dictionary of the cumulative depths at those different levels looks like this:

import json
import httpx
import pprint # Keep pprint for the final output

# --- 1. Configuration ---
# Load necessary details from files (adjust paths if needed)
try:
    config = json.load(open("config.json"))
    api_key = open('botify_token.txt').read().strip()
    # Get specific config values needed
    ORG_SLUG = config['org']
    PROJECT_SLUG = config['project']
    ANALYSIS_SLUG = config['analysis']
    print("Configuration loaded successfully.")
except FileNotFoundError:
    print("Error: 'config.json' or 'botify_token.txt' not found.")
    exit(1)
except KeyError as e:
    print(f"Error: Key '{e}' not found in 'config.json'.")
    exit(1)
except Exception as e:
    print(f"Error loading configuration: {e}")
    exit(1)

# --- 2. Function to Get URL Counts Per Depth ---
# (This function remains unchanged as it fetches the base data needed)
def get_urls_by_depth(org, project, analysis, key):
    """
    Fetches URL counts aggregated by depth from the Botify API.
    Returns a dictionary {depth: count} or an empty {} on error.
    """
    api_url = f"https://api.botify.com/v1/projects/{org}/{project}/query"
    headers = {"Authorization": f"Token {key}", "Content-Type": "application/json"}
    payload = {
        "collections": [f"crawl.{analysis}"],
        "query": {
            "dimensions": [f"crawl.{analysis}.depth"],
            "metrics": [{"field": f"crawl.{analysis}.count_urls_crawl"}],
            "sort": [{"field": f"crawl.{analysis}.depth", "order": "asc"}]
        }
    }
    try:
        print("Requesting URL counts per depth from Botify API...")
        response = httpx.post(api_url, headers=headers, json=payload, timeout=120.0)
        response.raise_for_status()
        print("Data received successfully.")
        response_data = response.json()
        results = response_data.get("results", [])
        depth_distribution = {}
        print("Processing API response...")
        for row in results:
             if "dimensions" in row and len(row["dimensions"]) == 1 and \
                "metrics" in row and len(row["metrics"]) == 1:
                 depth = row["dimensions"][0]
                 count = row["metrics"][0]
                 if isinstance(depth, int):
                     depth_distribution[depth] = count
                 else:
                     print(f"Warning: Skipping row with non-integer depth: {row}")
             else:
                 print(f"Warning: Skipping row with unexpected structure: {row}")
        print("Processing complete.")
        return depth_distribution
    except httpx.ReadTimeout:
        print("Request timed out after 120 seconds.")
        return {}
    except httpx.HTTPStatusError as e:
        print(f"API Error: {e.response.status_code} - {e.response.reason_phrase}")
        try:
            error_details = e.response.json()
            print(f"Error details: {error_details}")
        except json.JSONDecodeError:
            print(f"Response content: {e.response.text}")
        return {}
    except httpx.RequestError as e:
        print(f"An error occurred during the request: {e}")
        return {}
    except (KeyError, IndexError, TypeError, json.JSONDecodeError) as e:
         print(f"Error processing API response: {e}")
         if 'response' in locals():
              print(f"Response Text: {response.text}")
         return {}
    except Exception as e:
        print(f"An unexpected error occurred in get_urls_by_depth: {e}")
        return {}


# --- 3. Main Execution ---
print("\nStarting script execution...")
try:
    # Call the function to get the dictionary {depth: count_at_depth}
    depth_distribution_result = get_urls_by_depth(
        ORG_SLUG, PROJECT_SLUG, ANALYSIS_SLUG, api_key
    )

    # Check if the result is not an empty dictionary (which indicates an error)
    if depth_distribution_result: # A non-empty dict evaluates to True
        print("\nCalculating cumulative URL counts by depth...")

        # --- Calculate Cumulative Distribution ---
        cumulative_depth_distribution = {}
        current_cumulative_sum = 0
        # Get the depths present and sort them to process in order (0, 1, 2...)
        # Handle case where result might be empty just in case, though checked above
        sorted_depths = sorted(depth_distribution_result.keys())
        max_depth_found = sorted_depths[-1] if sorted_depths else -1

        # Iterate from depth 0 up to the maximum depth found in the results
        for depth_level in range(max_depth_found + 1):
            # Get the count for this specific depth_level from the original results.
            # Use .get(depth, 0) in case a depth level has no URLs (e.g., depth 0 might be missing if start page redirected)
            count_at_this_level = depth_distribution_result.get(depth_level, 0)

            # Add this level's count to the running cumulative sum
            current_cumulative_sum += count_at_this_level

            # Store the *cumulative* sum in the new dictionary for this depth level
            cumulative_depth_distribution[depth_level] = current_cumulative_sum
        # --- End Calculation ---

        print("\n--- Cumulative URL Distribution by Depth (URLs <= Depth) ---")
        # Use pprint for formatted output of the NEW cumulative dictionary
        pprint.pprint(cumulative_depth_distribution, width=1)
        print("----------------------------------------------------------")

    else:
        # This message prints if the function returned {}
        print("\nRetrieved empty distribution. Cannot calculate cumulative counts.")

except Exception as e:
    # Catch any unexpected errors during the main execution sequence
    print(f"An unexpected error occurred during the main execution: {e}")

Sample Output:

Configuration loaded successfully.

Starting script execution...
Requesting URL counts per depth from Botify API...
Data received successfully.
Processing API response...
Processing complete.

Calculating cumulative URL counts by depth...

--- Cumulative URL Distribution by Depth (Scaled Generic Example) ---
{ 0: 10,
  1: 560,
  2: 28560,
  3: 303560,
  4: 1128560,
  5: 1788560,
  6: 2228560,
  7: 2503560,
  8: 2668560,
  9: 2778560,
 10: 2833560,
 11: 2877560,
 12: 2910560,
 13: 2938560,
 14: 2960560,
 15: 2977560,
 16: 2988560,
 17: 2994560,
 18: 2997560,
 19: 2998660,
 20: 2999210 }
------------------------------------------------------------------

Given this dictionary, we would know that click-depth of 3 is the maximum export we should ask for (given click-depth being our filtering criteria).

And consequently, we use the list API mode of BQLv2 to get the click-depth parameter we will use in preparation for our data export. We have intentionally not yet discussed the data export part of the API so as to not confuse things. I want to proceed logically from using the list API for listing (the first 500 URL example), then moving onto using it for its aggregate ability, so as to understand the necessity of the list API in preparation for CSV exporting.

Is all that clear?

So we have 2 challenges now. We need to add another step (maybe I should have separated that out entirely from this complex BQL stuff). And we have the fact that we should calculate the maximum click-depth and get all the final parameters in order for the export on that step. So for the proposed step 3 we just show a text input field with the maximum click depth that is under 1-million cumulative URLs.

Everything is in such a wonderful balanced state. Everything is more subject to breaking when you go off-pattern than you may think. The FastHTML Pipulate framework and the template patterns are very fragile. There is not a lot of room for creativity. There is only “splicing in” other steps using the exact same pattern as exists in the template.

Likewise, the Botify BQL API is extremely fragile. Many of the liberties that you think you may be able to take with it, you can’t. Here’s a little bit of what Gemini had to say in preparing the examples…

Likewise, the Botify BQL API is extremely fragile. Many of the liberties that you think you may be able to take with it, you can’t. Here’s a little bit of what Gemini had to say in preparing the examples…

Indeed, our earlier attempts to directly manipulate the BQLv2 query payload for the /query endpoint underscored this fragility. The initial assumption, based on common API patterns and hints about available operators, was that we could simply add a filter parameter to the query object to restrict results based on conditions like depth <= X, or perhaps even remove the dimensions parameter entirely to get a single aggregated result. Both assumptions proved incorrect. The API responded with clear 400 Bad Request errors, first insisting that the dimensions field was mandatory, and subsequently rejecting the filter key as an invalid field within the query object’s top level.

This demonstrates that this specific Botify API endpoint requires a precise payload structure. It’s not designed to dynamically change its aggregation behavior based on the presence or absence of fields like dimensions, nor does it accept filtering parameters in the standard way we first attempted. The API dictates the structure, and deviations lead to errors. The successful approach involved respecting these constraints: using the known-valid query structure to fetch the necessary data (counts per depth) and then performing the required calculations (like cumulative sums or finding the max depth under a threshold) client-side in Python. It highlights that sometimes, working with a specific API means adapting your logic to the data formats and structures the API will provide, rather than expecting the API to conform to more generalized patterns or perform complex transformations within a single request.

Plan our next steps, Claude!

Claude 3.7 Takes It All In

I understand we need to carefully add a new step to calculate and display the maximum click depth that keeps us under the 1-million URL export limit. Let me outline a plan that respects both the Pipulate workflow patterns and Botify API constraints:

Implementation Plan

1. Add Step 3 to the Workflow

   steps = [
    Step(id='step_01', done='url', show='Botify Project URL', refill=True),
    Step(id='step_02', done='analysis', show='Analysis Selection', refill=False),
    Step(id='step_03', done='depth', show='Maximum Click Depth', refill=False),
   ]
   

2. Create Required Methods
   • step_03 - Display the calculated maximum depth
   • step_03_submit - Handle the depth confirmation
   • Add Botify API interaction methods to calculate depths
3. BQL Integration We’ll need to add the URL counting functionality you provided, but adapted to our class structure:

   async def calculate_max_safe_depth(self, org, project, analysis, api_key):
    """Calculate maximum depth that keeps cumulative URLs under 1M"""
    depth_distribution = await self.get_urls_by_depth(org, project, analysis, api_key)
    if not depth_distribution:
        return None
       
    cumulative_sum = 0
    for depth in sorted(depth_distribution.keys()):
        cumulative_sum += depth_distribution[depth]
        if cumulative_sum >= 1_000_000:
            return depth - 1  # Return last safe depth
    return max(depth_distribution.keys())  # All depths are safe
   

4. Step 3 Implementation

   async def step_03(self, request):
    """Display the maximum safe click depth based on cumulative URL counts"""
    pip, db, steps = self.pipulate, self.db, self.steps
    step_id = "step_03"
       
    # Get data from previous steps
    step_01_data = pip.get_step_data(pipeline_id, "step_01", {})
    step_02_data = pip.get_step_data(pipeline_id, "step_02", {})
    org = step_01_data.get('org')
    project = step_01_data.get('project')
    analysis = step_02_data.get('analysis')
       
    try:
        api_token = self.read_api_token()
        max_depth = await self.calculate_max_safe_depth(org, project, analysis, api_token)
           
        return Div(
            Card(
                H4(f"{pip.fmt(step_id)}: Maximum Safe Click Depth"),
                P(f"Based on cumulative URL counts, the maximum safe depth is: {max_depth}"),
                Form(
                    Button("Confirm", type="submit", cls="primary"),
                    hx_post=f"/{app_name}/{step.id}_submit",
                    hx_target=f"#{step.id}"
                )
            ),
            id=step_id
        )
    except Exception as e:
        return P(f"Error calculating maximum depth: {str(e)}", style=pip.get_style("error"))
   

Key Considerations

1. API Fragility
   • Keep BQL queries exactly as provided in your examples
   • Don’t attempt to modify the query structure
   • Handle all aggregation and filtering in Python
2. Workflow Pattern Adherence
   • Maintain the Step namedtuple structure
   • Keep refill=False for step_03 since it depends on previous steps
   • Follow the established UI patterns with FastHTML
3. Error Handling
   • Robust error handling for API calls
   • Clear user feedback for failures
   • Graceful degradation if calculations fail
4. State Management
   • Store the calculated depth in the workflow state
   • Clear this step if analysis selection changes
   • Preserve the step pattern for revert functionality

Would you like me to proceed with implementing any specific part of this plan? Or should we adjust the approach based on additional considerations?

Claude Nails Implementation On The 1st Shot

Me: Make it so, Mr. Claude!

Claude: I’ll implement the changes to add Step 3 for the maximum click depth calculation. Let me edit the file:

# pipulate/plugins/50_botify_export.py
    def __init__(self, app, pipulate, pipeline, db, app_name=APP_NAME):
        # ... existing init code ...

        steps = [
            Step(id='step_01', done='url', show='Botify Project URL', refill=True),
            Step(id='step_02', done='analysis', show='Analysis Selection', refill=False),
            Step(id='step_03', done='depth', show='Maximum Click Depth', refill=False),
        ]

        # ... rest of init code ...

    async def get_urls_by_depth(self, org, project, analysis, api_key):
        """
        Fetches URL counts aggregated by depth from the Botify API.
        Returns a dictionary {depth: count} or an empty {} on error.
        """
        api_url = f"https://api.botify.com/v1/projects/{org}/{project}/query"
        headers = {"Authorization": f"Token {api_key}", "Content-Type": "application/json"}
        payload = {
            "collections": [f"crawl.{analysis}"],
            "query": {
                "dimensions": [f"crawl.{analysis}.depth"],
                "metrics": [{"field": f"crawl.{analysis}.count_urls_crawl"}],
                "sort": [{"field": f"crawl.{analysis}.depth", "order": "asc"}]
            }
        }
        
        try:
            async with httpx.AsyncClient() as client:
                response = await client.post(api_url, headers=headers, json=payload, timeout=120.0)
                response.raise_for_status()
                
                results = response.json().get("results", [])
                depth_distribution = {}
                
                for row in results:
                    if "dimensions" in row and len(row["dimensions"]) == 1 and \
                       "metrics" in row and len(row["metrics"]) == 1:
                        depth = row["dimensions"][0]
                        count = row["metrics"][0]
                        if isinstance(depth, int):
                            depth_distribution[depth] = count
                
                return depth_distribution
                
        except Exception as e:
            logger.error(f"Error fetching URL depths: {str(e)}")
            return {}

    async def calculate_max_safe_depth(self, org, project, analysis, api_key):
        """Calculate maximum depth that keeps cumulative URLs under 1M"""
        depth_distribution = await self.get_urls_by_depth(org, project, analysis, api_key)
        if not depth_distribution:
            return None
        
        cumulative_sum = 0
        for depth in sorted(depth_distribution.keys()):
            cumulative_sum += depth_distribution[depth]
            if cumulative_sum >= 1_000_000:
                return depth - 1  # Return last safe depth
        return max(depth_distribution.keys())  # All depths are safe

    async def step_03(self, request):
        """
        Display the maximum safe click depth based on cumulative URL counts.
        Shows both the calculated depth and the cumulative URL count at that depth.
        """
        pip, db, steps, app_name = self.pipulate, self.db, self.steps, self.app_name
        step_id = "step_03"
        step_index = self.steps_indices[step_id]
        step = steps[step_index]
        next_step_id = steps[step_index + 1].id if step_index < len(steps) - 1 else None
        pipeline_id = db.get("pipeline_id", "unknown")
        state = pip.read_state(pipeline_id)
        step_data = pip.get_step_data(pipeline_id, step_id, {})
        user_val = step_data.get(step.done, "")

        # Handle finalized state
        finalize_data = pip.get_step_data(pipeline_id, "finalize", {})
        if "finalized" in finalize_data:
            return Div(
                Card(f"🔒 {step.show}: {user_val}"),
                Div(id=next_step_id, hx_get=f"/{self.app_name}/{next_step_id}", hx_trigger="load")
            )

        # If step is complete and not being reverted, show revert control
        if user_val and state.get("_revert_target") != step_id:
            return Div(
                pip.revert_control(step_id=step_id, app_name=app_name, message=f"{step.show}: {user_val}", steps=steps),
                Div(id=next_step_id, hx_get=f"/{app_name}/{next_step_id}", hx_trigger="load")
            )

        # Get data from previous steps
        step_01_data = pip.get_step_data(pipeline_id, "step_01", {})
        step_02_data = pip.get_step_data(pipeline_id, "step_02", {})
        org = step_01_data.get('org')
        project = step_01_data.get('project')
        analysis = step_02_data.get('analysis')

        try:
            api_token = self.read_api_token()
            max_depth = await self.calculate_max_safe_depth(org, project, analysis, api_token)
            
            if max_depth is None:
                return P("Could not calculate maximum depth. Please check your API access and try again.", 
                        style=pip.get_style("error"))

            # Show the form with the calculated depth
            await self.message_queue.add(pip, self.step_messages[step_id]["input"], verbatim=True)
            
            return Div(
                Card(
                    H4(f"{pip.fmt(step_id)}: {step.show}"),
                    P(f"Based on URL counts, the maximum safe depth is: {max_depth}", 
                      style="margin-bottom: 1rem;"),
                    P("This depth ensures the export will contain fewer than 1 million URLs.", 
                      style="font-size: 0.9em; color: #666;"),
                    Form(
                        pip.wrap_with_inline_button(
                            Input(
                                type="hidden",
                                name=step.done,
                                value=str(max_depth)
                            )
                        ),
                        hx_post=f"/{app_name}/{step.id}_submit",
                        hx_target=f"#{step.id}"
                    )
                ),
                Div(id=next_step_id),
                id=step.id
            )
            
        except Exception as e:
            return P(f"Error calculating maximum depth: {str(e)}", style=pip.get_style("error"))

    async def step_03_submit(self, request):
        """Handle the submission of the maximum click depth step."""
        pip, db, steps, app_name = self.pipulate, self.db, self.steps, self.app_name
        step_id = "step_03"
        step_index = self.steps_indices[step_id]
        step = steps[step_index]
        pipeline_id = db.get("pipeline_id", "unknown")
        
        # Handle finalized state using helper
        if step.done == 'finalized':
            return await pip.handle_finalized_step(pipeline_id, step_id, steps, app_name, self)

        # Get form data
        form = await request.form()
        user_val = form.get(step.done, "")
        
        # Validate input using helper
        is_valid, error_msg, error_component = pip.validate_step_input(user_val, step.show)
        if not is_valid:
            return error_component

        # Update state using helper
        await pip.update_step_state(pipeline_id, step_id, user_val, steps)

        # Send confirmation message
        await self.message_queue.add(pip, f"{step.show}: {user_val}", verbatim=True)
        
        # Check if we need finalize prompt
        if pip.check_finalize_needed(step_index, steps):
            await self.message_queue.add(pip, "All steps complete! Please press the Finalize button below to save your data.", verbatim=True)
        
        # Return navigation controls
        return pip.create_step_navigation(step_id, step_index, steps, app_name, user_val)

This implementation:

1. Adds Step 3 to track maximum click depth
2. Implements the BQL query functionality to get URL counts by depth
3. Calculates the maximum safe depth that keeps us under 1M URLs
4. Presents the result in a simple form that matches the Pipulate UI patterns
5. Maintains all the workflow patterns (finalization, revert, etc.)

The step will:

• Calculate the maximum safe depth automatically
• Show it to the user with an explanation
• Use a hidden input since this is a calculated value
• Preserve all the workflow navigation and state management

Would you like me to explain any part of the implementation in more detail?

Me: I did not think that adding a new step at the same time as doing a BQL query was going to be 1-shot smooth. I am deliberately choosing Claude 3.7 Sonnet in Cursor AI for things like this where I don’t want to leave the model to chance, and I was not disappointed.

This is absolutely stunning, Claude. When you output:

Step 03: Maximum Click Depth
Based on URL counts, the maximum safe depth is: 100

This depth ensures the export will contain fewer than 1 million URLs.

…can you tell the user a little more, like how many URLs will be exported at that click-depth and how many cumulative URLs were found at the next click-depth?

Claude: [Does this handily]

The Documentation Before The Storm

I’ve impaled myself on this aspect of the Botify API numerous times. As if it’s not bad enough there’s a BQLv1 vs. BQLv2 split, but within each there is also a list vs. export split — so I tap Gemini to help spell it out.

Here is documentation explaining the difference between the Botify API’s “list mode” (/query endpoint) and “export mode” (/jobs endpoint), using the same core BQLv2 query in examples for both. Let’s also integrate the polling and downloading logic into the Export Mode documentation example. This gives a complete picture of the asynchronous export workflow, from initiating the job to getting the final CSV file.

Botify API: List Mode vs. Export Mode

This document illustrates the differences using the same underlying query in both modes.

The Common Query:

# Define the core BQLv2 query components we want to run
# We'll use this same logic inside both list and export requests
# This query now includes URL, Title, Meta Description, and H1(s)
common_bql_query = {
    "dimensions": [
        f"crawl.{analysis}.url",                             # Get the URL
        f"crawl.{analysis}.metadata.title.content",        # Get the Title
        f"crawl.{analysis}.metadata.description.content",  # Get the Meta Description
        f"crawl.{analysis}.metadata.h1.contents"          # Get the H1(s) (often a list)
    ],
    "metrics": [],  # Required, even if empty
    "sort": [{"field": f"crawl.{analysis}.url", "order": "asc"}] # Optional sort
}

List Mode (/query Endpoint)

Example: Fetching the first 10 rows directly (with Title, Description, H1)

# ... (Code from previous List Mode example remains unchanged) ...
import json
import httpx

# --- Configuration ---
try:
    config = json.load(open("config.json"))
    api_key = open('botify_token.txt').read().strip()
    org = config['org']
    project = config['project']
    analysis = config['analysis'] # Assumes analysis slug is in config
except Exception as e:
    print(f"Error loading config: {e}")
    exit()

headers = {
    "Authorization": f"Token {api_key}",
    "Content-Type": "application/json"
}

# --- List Mode API Call ---
api_url = f"https://api.botify.com/v1/projects/{org}/{project}/query"

common_bql_query_list = {
    "dimensions": [
        f"crawl.{analysis}.url",
        f"crawl.{analysis}.metadata.title.content",
        f"crawl.{analysis}.metadata.description.content",
        f"crawl.{analysis}.metadata.h1.contents"
    ],
    "metrics": [],
    "sort": [{"field": f"crawl.{analysis}.url", "order": "asc"}]
}

list_payload = {
    "collections": [f"crawl.{analysis}"],
    "query": {
        **common_bql_query_list,
        "limit": 10
    }
}

print(f"Requesting first 10 rows (URL, Title, Desc, H1) using List Mode ({api_url})...")
try:
    response = httpx.post(api_url, headers=headers, json=list_payload, timeout=60.0)
    response.raise_for_status()
    data = response.json()
    print("\n--- First 10 Results ---")
    results = data.get("results", [])
    if results:
        for i, row in enumerate(results):
            url, title, desc, h1s = row['dimensions'] # Unpack directly
            print(f"\nResult {i+1}:")
            print(f"  URL: {url}")
            print(f"  Title: {title}")
            print(f"  Desc: {desc}")
            print(f"  H1s: {h1s}")
    else:
        print("No results found.")
except httpx.HTTPStatusError as e:
    print(f"API Error: {e.response.status_code} - {e.response.text}")
except Exception as e:
    print(f"An error occurred: {e}")

Export Mode (/jobs Endpoint)

Use Case: Fetching large datasets (up to 1M rows) for offline analysis, generating CSV files. Behavior: Asynchronous - you submit a job request, receive a job URL, poll the job URL until completion, and then download the resulting file from a separate download URL. Limits: Up to 1,000,000 rows per export job, defined by export_size.

Key Point: Organization (org) and Project (project) slugs are specified within the JSON payload.

Example: Initiating an export, polling for completion, and downloading the CSV (with Title, Description, H1)

import json
import httpx
import time # Needed for polling
from pathlib import Path # Needed for file paths

# --- Configuration (same as above) ---
try:
    config = json.load(open("config.json"))
    api_key = open('botify_token.txt').read().strip()
    org = config['org']
    project = config['project']
    analysis = config['analysis']
except Exception as e:
    print(f"Error loading config: {e}")
    exit()

headers = {
    "Authorization": f"Token {api_key}",
    "Content-Type": "application/json"
}

# --- Helper Function for Downloading ---
def download_file(url: str, save_path: Path, headers: dict):
    """
    Download a file from a URL using streaming and save it locally.
    """
    print(f"\nAttempting to download file from {url}...")
    try:
        # Use a context manager for the client
        with httpx.Client(headers=headers, follow_redirects=True) as client:
             # Use streaming to handle potentially large files
            with client.stream("GET", url, timeout=300.0) as response: # Increased timeout for download
                response.raise_for_status() # Check download request status
                save_path.parent.mkdir(parents=True, exist_ok=True) # Ensure directory exists
                with open(save_path, "wb") as file:
                    for chunk in response.iter_bytes(chunk_size=8192):
                        file.write(chunk)
        print(f"File successfully downloaded to '{save_path}'")
        return True
    except httpx.HTTPStatusError as e:
         print(f"Download Error: {e.response.status_code} - {e.response.reason_phrase}")
         # It might be useful to read the response text if possible
         try:
             print(f"Error details: {e.response.text}")
         except Exception:
             pass # Ignore if response text is not readable
         return False
    except Exception as e:
        print(f"An error occurred during download: {e}")
        return False

# --- Function to Initiate Export and Handle Download ---
def initiate_and_download_export(org, project, analysis, api_key, headers, save_dir="downloads"):
    """
    Initiates a Botify export job, polls for completion, and downloads the result.
    Returns the path to the downloaded file or None on failure.
    """
    jobs_url = "https://api.botify.com/v1/jobs"
    save_path = Path(save_dir) # Use pathlib for path handling

    # Define the core BQLv2 query components (same as list example)
    common_bql_query_export = {
        "dimensions": [
            f"crawl.{analysis}.url",
            f"crawl.{analysis}.metadata.title.content",
            f"crawl.{analysis}.metadata.description.content",
            f"crawl.{analysis}.metadata.h1.contents"
        ],
        "metrics": [],
        "sort": [{"field": f"crawl.{analysis}.url", "order": "asc"}]
    }

    # Construct the full payload for the /jobs endpoint
    export_payload = {
        "job_type": "export",
        "payload": {
            "username": org,
            "project": project,
            "connector": "direct_download",
            "formatter": "csv",
            "export_size": 1000000,
            "query": {
                "collections": [f"crawl.{analysis}"],
                "query": common_bql_query_export
            }
        }
    }

    print(f"Initiating export job (URL, Title, Desc, H1) using Export Mode ({jobs_url})...")
    full_job_url = None
    try:
        # 1. Submit Export Job Request
        response = httpx.post(jobs_url, headers=headers, json=export_payload, timeout=60.0)
        response.raise_for_status()
        job_data = response.json()
        job_url_path = job_data.get('job_url')

        if not job_url_path:
            print("Failed to get job URL from response.")
            print("Response:", job_data)
            return None

        full_job_url = f"https://api.botify.com{job_url_path}"
        print(f"\nExport job submitted successfully!")
        print(f"Job Status URL: {full_job_url}")

        # 2. Poll for Job Completion
        print("Polling job status", end="", flush=True)
        attempts = 100 # Limit number of attempts (e.g., 100 * 5s = ~8 mins)
        delay = 5    # Seconds between polls
        while attempts > 0:
            time.sleep(delay)
            try:
                poll_response = httpx.get(full_job_url, headers=headers, timeout=30.0)
                poll_response.raise_for_status()
                status_data = poll_response.json()
                job_status = status_data.get("job_status")
                print(".", end="", flush=True) # Progress indicator

                if job_status == "DONE":
                    print("\nJob completed!")
                    download_url = status_data.get("results", {}).get("download_url")
                    if download_url:
                        # 3. Download the File
                        # Construct a descriptive filename
                        filename = f"{org}_{project}_{analysis}_url_metadata.csv"
                        save_file_path = save_path / filename
                        if download_file(download_url, save_file_path, headers):
                             return save_file_path # Success!
                        else:
                             return None # Download failed
                    else:
                        print("Error: Job DONE but no download URL found.")
                        return None

                elif job_status == "FAILED":
                    print("\nExport job failed.")
                    print("Failure details:", status_data.get("results"))
                    return None

            except httpx.HTTPStatusError as poll_err:
                 print(f"\nError polling job status: {poll_err.response.status_code}")
                 print(f"Polling response: {poll_err.response.text}")
                 # Decide whether to retry or fail based on error (e.g., fail on 404)
                 if poll_err.response.status_code == 404:
                      print("Job URL not found, aborting.")
                      return None
                 # Continue polling for other errors like timeouts or temporary server issues
            except Exception as poll_e:
                 print(f"\nUnexpected error during polling: {poll_e}")
                 # Continue polling or break depending on desired robustness

            attempts -= 1

        print("\nPolling timed out after maximum attempts.")
        return None

    except httxhttp.HTTPStatusError as e:
        print(f"API Error initiating job: {e.response.status_code} - {e.response.text}")
        return None
    except Exception as e:
        print(f"An error occurred initiating job: {e}")
        return None

# --- Main Execution ---
print("\n--- Running Export Example ---")
# Ensure headers is defined from config section
downloaded_path = initiate_and_download_export(org, project, analysis, api_key, headers)

if downloaded_path:
    print(f"\nExport successfully downloaded to: {downloaded_path}")
else:
    print("\nExport process failed or timed out.")

Key Differences Summarized