Agentic Coding & Wrangling AI Assistants

By Mike Levin

Understanding Pipulate Development & AI Collaboration: Getting Started

This article provides a glimpse into the development journey of a software project called Pipulate. The author details progress on a specific feature designed to optimize website performance for search engines (SEO), particularly for users of a platform called Botify, by managing URL parameters to save ‘crawl budget’. Beyond the specific feature, the text offers a personal reflection on the practicalities of modern software development, including the challenges of working with external APIs (like Botify’s), the importance of documentation, and the evolving role of Artificial Intelligence (AI) coding assistants in the development process.

The author discusses using tools like Cursor AI and Gemini, exploring concepts like ‘agentic coding’ (where AI takes more initiative) and strategies for effectively guiding AI to understand project-specific details. A core focus is the task of consolidating project documentation—scattered across different files—into a more organized structure suitable for various audiences, including human users, developers, and the AI systems assisting them. This process highlights the interplay between human developers and AI tools in tackling complex software challenges.

Parameter Buster: A Game-Changing SEO Workflow

I have finished the first and potentially most important of the Pipulate workflows for actual day-to-day use by my coworkers. It is Parameter Buster. It is the type of thing that will mostly be useful for Botify customers as it leverages all 3 of the major data sources Botify takes advantage of:

• Web logs, which are not easy to get these days
• Crawl data, already pre-analyzed to those not in Google Search
• Search Console, all the URLs that are active in search

It uses these three data sources to create a optimization that will remove querystring parameters from the URLs it finds during a Google Crawl that would result in crawl budget waste. The end result is a copy/pastable PageWorkers optimization, perhaps one of the best tricks in the book.

Strategic Documentation and Release Planning

Today is about banking wins and banking more wins. It’s about polish, documentation and enabling my coworkers to actually use this thing. It’s a lot like letting Pipulate out in the wild generally, but really only of much use to Botify employees and customers. This is a reasonable first step. More general purpose SEO stuff comes later.

Avoid rabbit hole projects today. A single one could ruin you for another week. You have this big solid open block of Sunday, and then back to work. By the time you go back to work you will have a private YouTube video for this thing planted as a copy/pastable link-example payload to ricochet around on Slack. The idea is to quietly and self-evidently announce a total game-changer that those who can recognize such things will recognize. This is much about that story and that script. Speak quietly and deliver a big demo.

Navigating Botify API Complexities

I’m doing a few passes to improve the overall system based on the considerable learnings implementing 100_parameter_buster.py. My first stop is 50_botify_export.py to capture all the nuance and subtlety surrounding CSV downloads, of which there are many. It’s a wild maze to navigate — from BQLv1 which is easy to get examples of but which you shouldn’t be using vs. BQLv2 which you should be using but is hard to get examples of. Then there’s the /query endpoint vs. the /jobs endpoint, and really also the /export endpoint which is all over the place in terms of examples but which really has been deprecated by the /jobs endpoint… even when it’s a BQLv1 query! And then the polling… Oh, don’t get me started.

The Importance of Documentation in AI-Assisted Development

There’s vast divide of experience with the Botify API that must be mastered before everything stops being some sort of show-stopping obstacle. You really have to be an engineer. However, I am not. And so this kind of post-successful project documentation where I bank my winnings in documentation that helps the next round of AI Assistants pick apart why things went well the last time is so important. It’s part of the accelerating success effect. Success leads to success leads to success, and perhaps few places as well as in training AIs with really good and forever-improving documentation.

The Evolution of AI Code Assistants

I feel the underlying AI Code Assistants and the way they are integrated with the Cursor AI editor shifting and evolving underneath of me as I go. I believe it’s going to be too resource intensive and wasteful to train all the frontier AI models that are being used for such things, Claude, Gemini, ChatGPT, etc. to handle this process radically differently between Cursor, VSCode, Windsurf, Cline and the like. I believe we are experiencing User Interface / User Experience / Application Programming Interface (UI/UX/API) convergence here. The way “Agent” mode is different from “Manual” mode is different from “Ask” mode, while the labels of these modes may vary from AI assisted editor to editor, the spirit stays the same.

Understanding Agentic Coding

It used to be that there was one simple way you got AI-assisted coding. More or less, it was the same way we used ChatGPT through the Web UI at first, which is we send a prompt and a bunch of code together in-context, and it responded back with new code that we somehow had to figure out how to work into our existing codebase. Pedantic hair-splitting definition people are calling this former case AI assisted coding, while the later case that is springing up all over the place Agentic Coding. People think Agentic Coding is new, but as fast as the ChatGPT API was out, so was AutoGPT, CrewAI, Microsoft’s AutoGen and others. Agentic mode was there from the beginning, but relegated to us techies. Extremely recently the flurry of GooseAI, Manus, Cline and Claude Code showed us that Agentic operation was for the masses. And so now it’s popping up as “Agent” mode everywhere, including Cursor. And it all comes down to calling tools with iterative back-and-forth self-prompting.

The Evolution of Agentic Coding Implementation

Implementations may vary, but the end result is the same. The AI goes off half-cocked and loosey goosey fulfilling your request until it feels its done to your satisfaction and whatever other criteria. It’s not terribly different than the original coding assistants except that all their output is fed back into them as the new input automatically, including the output of the use of external tools that can search your codebase, use git, check the Web and whatever else.

The Rise of MCP and Standardized AI Protocols

The precise details of how they do this and made such a big leap forward so quickly is part of where this new fangled protocol MCP (model context protocol) that is becoming so popular. If all the frontier models need to know how to do this stuff we can’t fragment it into a thousand different ways because its too expensive and brittle to get all the models abiding by the schisming rules that started popping up with different tool-call protocols, differing annoyingly slightly between OpenAI, Google and Anthropic. Anthropic leading the way with new protocol proposals such as they seem to do a lot lately proposed MCP and everyone jumped on the bandwagon (including Google!) and now all the AI models know how to do this agentic dance.

The Three Modes of AI Interaction

And so we have Agent, Manual and Ask “modes” to choose from. And we have AIs going off half-cocked doing stuff to the point of downwards spiraling collapse or the narrow-path to success. Try stuff, break things, back-up, undo, try again refactoring while factoring in what we learned. Basically AIs are working a lot like humans but exploring all these potential paths at 100x the rate a human could. And so yeah, agentic behavior is big and important like all the hype, but then so is controlling that half-cocked loosey goosey behavior, steering and directing it with the kind of talent that defines where the jobs are going in the new age of AI — riding the bucking bronco — or the sandworm if you prefer and are a Dune nerd like me.

Git as the Safety Net for AI Development

And so with such a potentially disastrous (to your codebase) process, obviously they need some sort of safety-net letting them revert and branch in the background, and obviously they’re going to choose git just like humans and the rest of the world. And so equally obviously Cursor AI, and I presume all the others like Microsoft, Cline and Windsurf who must be implementing such things, are using their own bizarro parallel git server in the background for their own code assistants through some MCP tool-calling standard. Git through MCP must be just about the most delicious secret sauce in the AI code editor arms race right now.

The Human-AI Git Collaboration Dance

Cursor AI’s implementation is good. I feel free to experiment wildly. I have git reset --hard HEAD ready on my end. I know Claude has something similar on its end. When an experiment spirals out of control it’s almost like an inside joke between the human and the AI who is going to revert first and inform the other of the restore-point details. Claude doesn’t have or know my git hashes (unless I instruct it to use them through the terminal, haha), and I don’t know it’s. But what used to be downward spirals of AI code-assisted ruin are now just easy peasy experimental branches, and thus the sought-after acceleration effect is possible. Collapsing house of cards become reassemblable construction projects with instant undo/redo — from two sides! The git-using AI and the human who is learning to use git better, because AI.

The Practical Value of AI Assistants

It doesn’t matter whether these things are sentient or whether they have “true” creativity or not. Their value as a pedantic housekeeper of deep arcane detail make them infinitely useful from a pragmatic standpoint. How creative does a superpowered librarian need to be? How creative does a superpowered automechanic need to be? If something is there to immediately find the unfindable and fix the unfixable that just frees up all your creative potential. And when you learn to tap these superpowered assistants like driving a car, you can talk and drive at the same time. See? When steering AI becomes second nature like driving, your own deliberate cognitive capacity — the kind that needs to be front-and-center which you can’t throw on autopilot — gets freed up for the important stuff like adapting to changing conditions.

The Pipulate Project’s AI Integration Journey

And that’s very much where we are now on the Pipulate project. I am learning to use the AI coding assistants naturally. It’s not a struggle every time screaming at them FastHTML is not FastAPI. With a critical mass of code examples they have to look at every time when navigating responses they are effectively cornered into understanding the things they need to understand to be useful. Your local context is overwhelmingly overriding their over-training on all the full web stack enterprise redux kubernetes react stuff they’ve been brainwashed on. But sometimes you need to use a wedge like: It’s like the Electron Platform. Yeah that’s right. Web UI’s can be used for localhost apps (VSCode, Slack, Zoom, Discord, etc). And they don’t have to be written to scale like Netflix. Surprise!

Organizing Cursor Rules and Documentation

So, I’m sorting out the rules. This is part of my cleaning and polish. When you drop a .cursorrules in your local git repo, it’s tied to the repo moving around with it and part of your vendor-independent portability powers. However, the new workspace-scoped .cursor/rules/something.mdc are not. And so part of this morning’s cleanup as the day gets underway is me taking what I’ve broken up over multiple non-repo-bound .mdc files and wrapping them into one. I’m going to use Gemini 2.5 to do this, even though it’s been letting me down a lot lately by not being able to do long replies without logging me out. However, it’s also given the best responses so the strategy is just to keep it short. Let’s get the context together. First, the prompt.

The System Overview and Documentation Challenge

I have a system. The documentation of this system is spread out over a lot of places and files which I have gathered together here. I have also included parts of the codebase this documentation refers to so you can check the accuracy of the documentation against the single source of truth: the code itself, the behavior of which when running is what’s being documented.

The Anti-Pattern Solution to Modern Web Development

The system is full of counterintuitive anti-patterns as a remedy for much of what ails bloated Conway’s Law-infested modern web development. A single webmaster sitting at a single system can interact with a software product in the web browser much as one would with an Electron platform app, but instead of an opinionated bundle there is a whole normalized deterministic Linux subsystem running out of a git repo folder provided by nix flakes, solving the “not on my machine” problem, blurring the lines between the development platform and the app runtime environment. This provides full transparency to the user by moving almost all responsibilities and concerns that would be handled by a fat JavaScript client library onto the server through FastHTML and HTMX, down to and including the cookies. Think server-side cookies whose state is shown through the webserver console output which the user is free to watch while using the app. There are no mysteries concerning client-side state because all that state is shifted to the server and exposed through its server console. That’s on top of the client-side DevTools console which still also can be used to monitor the reduced and simplified minimal JavaScript that remains. So in short, there is nothing that cannot be easily known. And with this total knowledge comes enhanced control and with this enhanced control comes accelerated feedback loop of app improvement, especially AI-assisted as it can be leveraging that same said transparency for the Coding Assistant.

The Practical Reality of the System

Is the vision becoming clear? None of this is theoretical. All of this exists right now today as you can see from the code and documentation I am providing you. In fact I am leaving many things out so as to not overwhelm you with the quantity and details of the antipattern corralling examples that overcome your enterprise-concerns over-training. I’ve given you just enough examples of the DRY CRUD apps and WET Workflow examples to allay any concern that they might not exist or be as powerful as this system implies. They exist and are quite powerful. Basically, anything able to be expressed in Python and organized in the Jupyter Notebook style can be ported into this system and have a superior user interface and experience for the non-technical user who doesn’t want to and shouldn’t need to look at the Python code powering it all. So in other words, Pipulate brings the already transformative and liberating power of Notebooks to the rest of the world by taking that extra step and allowing the refinement of notebooks into code-free (as far as the user is concerned) Web Apps!

So there are 2 audience for every bit of documentation made. Can that. There are FOUR audiences for the documentation:

1. The human merely using the apps who need not even know nix but for the 2 commands to get an app installed and running
2. The human developing the apps who need be near a pulsing-brain alien to port Notebooks to FastHTML/HTMX.
3. The frontier-model AI Coding Assistant (like you) there to reduce the human’s dependency on actually being a pulsing-brain alien
4. The Ollama-powered LLM baked into the locally running web app who understands it from real-time training and is there to help the human merely using the app

The Four-Audience Documentation Ecosystem

Together these four audiences have a beautiful dance, rapidly customizing different instances of this system into endless customized variations for the SEO, Finance, Science industries and others — basally any industry or situation that could benefit from having a clearly documented semi-automated linear workflow with a local AI riding shotgun to help you make sure those parts that can’t be fully automated are at least well understood by the human while it shepherded them through whatever part of the process that still needs a human in the loop.

XML-Powered Cross-Platform Feedback Loop

The way this becomes a feedback loop independent of any particular AI platform (including Cursor, Windsurf, Cline, VSCode, etc) or even any particular API, application or even Web UI, is to bundle it all up into an XML package that can be 1-shot submitted to you and your kind. It’s so easy to do this way I am not even limited to doing it once against you nor to just you. I can do variations of this very prompt and rapidly re-test it against you and other models to compare your output. It is a wonderful way to probe for the best answers using different approaches, and even to benchmark the frontier models like you. So it is with that sort of eye I will be examining your response to this prompt.

Documentation Requirements by Audience

With that in mind, I have given you both the code that drives the system (for the most part) and some of the already existing documentation. I need to create documentation for each of the audiences discussed:

1. A human just using web apps
2. A human developing those web apps
3. A Frontier Model AI (like you) helping the developer make those web apps (port Notebooks)
4. A local model helping the user use those web apps (step through Pythonless Notebooks)

End-User Documentation (Audience 1)

For audience 1, the documentation will be:

• Introduction on Pipulate.com
• Install instructions
• Philosophy document (why the keys, chain reacting flow, etc)

Developer Documentation (Audience 2)

For audience 2, the documentation will be:

• Explanation of Nix/.venv environment
• High level Notebook-to-FastHTML/HTMX concepts
• Workflow Implementation Guide

AI Assistant Documentation (Audience 3)

For audience 3, the documentation will be:

• The mega super-complete .cusorrules file (replacing need for .mdc files)
• The mega super-complete Workflow Implementation Guide
• Recipes for every kind of Workflow Widget

Local AI Documentation (Audience 4)

For audience 4, the documentation will be:

• System prompt instilling persona and giving overview of its role
• Real-time training markdown documents per crud/workflow web app
• Tool-use instructions (probably MCP) for agentic “steps” within workflows

Consolidating Documentation Sources

I think that about covers it. That’s to give you the broad overview. But now to the actual prompt for you for right now, what I expect you to do in response to this. My priority is to reduce the reliance on .mdc files that are not bound to the repo and to instead roll all their good and knowledge into a combination of .cursorrules, perhaps the README.md (serving a lot of audiences and purposes right now), and the plugins/workflow_implementation_guide.md, which seems to be where a lot of the deep precise knowledge is ending up that’s too big to feed to the LLM on every request the way .cursorrules does.

Response Guidelines and Iterative Refinement

And so the request here is really for you to within a reasonable response size because anything larger than what appears to be about 1000 lines automatically logs me off of Gemini and loses all the good work of your reply (no way to retrieve your up-to-that output — lost forever!). So give me what you think is the right first-pass output to this request with further instructions as to how I should refine THIS PROMPT for the next iterative round because it is very easy for me to re-prompt you with one of these super-prompts in a wholly new session freeing up all your tokens and starting fresh, but with your directional advice incorporated into the prompt-at-the-end (which this is).

Is that clear? I understand it is a bit meta, but you can appreciate what I’m going for with the accelerating effect implied by this document and the effect that good documentation has towards this cause by enabling entities such as you to do even better the next time.

Please and thank you!

[mike@nixos:~/repos/pipulate/precursors]$ python context_foo.py --prompt prompt.md
Using prompt file: /home/mike/repos/pipulate/precursors/prompt.md
Using template 1: Material Analysis Mode
Output will be written to: foo.txt

=== Prompt Structure ===

--- Pre-Prompt ---
System Information:
  
You are about to review a codebase and related documentation. Please study and understand
the provided materials thoroughly before responding.
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 the architecture and patterns in the codebase
  • Note how existing patterns could be leveraged in your response
  • Consider both technical and conceptual aspects in your analysis

--- Files Included ---
• server.py (25,744 tokens)
• flake.nix (3,301 tokens)
• requirements.txt (105 tokens)
• README.md (5,959 tokens)
• .cursorrules (974 tokens)
• plugins/20_hello_workflow.py (6,757 tokens)
• plugins/60_widget_examples.py (19,775 tokens)
• plugins/70_blank_workflow.py (3,390 tokens)
• plugins/80_splice_workflow.py (6,185 tokens)
• /home/mike/repos/.cursor/rules/htmx-chain-reactions.mdc (552 tokens)
• /home/mike/repos/.cursor/rules/nix-rules.mdc (964 tokens)
• /home/mike/repos/.cursor/rules/placeholder-step-pattern.mdc (1,311 tokens)
• /home/mike/repos/.cursor/rules/wet-workflows.mdc (2,155 tokens)
• /home/mike/repos/pipulate/training/workflow_implementation_guide.md (7,471 tokens)
• /home/mike/repos/pipulate/precursors/prompt.md (4,045 tokens)

--- Post-Prompt ---

--- Token Summary ---
Total tokens: 89,170 tokens
Maximum allowed: 3,990,000 tokens
Remaining: 3,900,830 tokens

=== End Prompt Structure ===

Output written to 'foo.txt'
Output copied to clipboard

Script finished.

Gemini 2.5’s Response

Gemini: Okay, I’ve reviewed the extensive context you provided, including the codebase structure, conventions, existing documentation (.cursorrules, README.md, workflow files, .mdc rule files), and your insightful prompt detailing the project’s philosophy, target audiences, and the specific request to consolidate documentation.

This is a fascinating project with a clear, albeit unconventional, architectural vision prioritizing local-first operation, observability, and developer/AI collaboration. The “WET Workflows, DRY CRUD” approach and the explicit HTMX chain-reaction pattern are particularly noteworthy.

Here’s a breakdown addressing your request, focusing on consolidating the .mdc rule files into the primary documentation artifacts while keeping the response concise for Gemini:

Material Analysis

1. Key Concepts/Patterns:
   • Local-First/Single-Tenant: Core principle, drives simplicity. State managed server-side (DictLikeDB/JSON for workflows, MiniDataAPI/SQLite for CRUD).
   • Nix Flakes: Ensures reproducible environments across OS (Linux, macOS, WSL). Includes specific setup (nix develop, .#quiet shell).
   • FastHTML/HTMX: Server-rendered UI, minimal JS, Pythonic HTML generation.
   • Workflow Engine (Pipulate class): Manages state, provides helpers (revert_control, widget_container, state methods), enforces step progression.
   • Explicit HTMX Chain Reaction: Critical pattern (Div(id=next_step_id, hx_get=..., hx_trigger="load")) for reliable step advancement. Must not be altered.
   • WET Workflows: Explicit step-by-step logic, often ported from notebooks. Uses Step namedtuple, step_XX/step_XX_submit handlers.
   • DRY CRUD (BaseCrud): Reusable class for standard data management (e.g., Profiles).
   • Plugin Architecture: Auto-discovery of workflows/apps in /plugins.
   • Dual Identity: Workflows have filename (URL) and APP_NAME (DB key) identities.
   • Integrated Tooling: Ollama for local LLM, Jupyter for prototyping, Watchdog for live reload.
2. Interesting Aspects:
   • The deliberate rejection of common web scaling patterns (complex ORMs, client-side state, message queues) in favor of extreme simplicity and observability is unique.
   • The emphasis on AI collaboration at multiple levels (developer assistant, user assistant) and using documentation/code structure as training data is forward-thinking.
   • The Nix Flake integration for true cross-platform reproducibility is powerful.
   • The explicit HTMX chain reaction, while potentially verbose, highlights a focus on reliability over relying on potentially fragile event bubbling in complex scenarios.
3. Approach Characterization: Pragmatic, minimalist, opinionated, developer-centric (for workflow creation), user-centric (for workflow execution), and highly focused on local operation and control. It prioritizes transparency and directness over abstraction layers.

Strategic Considerations & Consolidation Plan

The goal is to consolidate the knowledge from .mdc files into repo-bound artifacts (.cursorrules, README.md, workflow_implementation_guide.md) tailored for different audiences.

1. .cursorrules (Audience: AI Dev Assistant - High Frequency):
   • Keep: Core Philosophy, What to Avoid.
   • Add: Critical HTMX Chain Reaction Pattern (from htmx-chain-reactions.mdc).
   • Add: Filename vs. APP_NAME Dual Identity Pattern (from wet-workflows.mdc).
   • Add: Brief Nix environment reminder (nix develop .#quiet).
   • Add: Pointers to README.md for setup and workflow_implementation_guide.md for detailed steps.
2. README.md (Audiences: End User, Developer - Overview):
   • Keep: Existing content (Intro, Install, Philosophy, Architecture, Tech).
   • Enhance/Add: Refine “Installation” and add a “Developer Setup” section incorporating key Nix setup commands (from nix-rules.mdc). Keep it concise, linking elsewhere for exhaustive details if needed.
   • Enhance: Briefly mention the WET/DRY and Dual Identity concepts in the Philosophy/Architecture sections.
3. workflow_implementation_guide.md (Audiences: Developer, AI Dev Assistant - Detailed How-To):
   • Add: Detailed explanation of Step Definition (Step namedtuple) (from wet-workflows.mdc).
   • Add: Detailed structure of step_XX / step_XX_submit handlers, including the full chain reaction implementation code block as the required standard (from wet-workflows.mdc, htmx-chain-reactions.mdc).
   • Add: Placeholder Step Pattern (from placeholder-step-pattern.mdc).
   • Add: Guidance on Splicing/Adding Steps (from this guide, wet-workflows.mdc).
   • Add: State Management details (pipulate helpers) (from wet-workflows.mdc).
   • Add: Widget Integration Patterns (referencing 60_widget_examples.py and its patterns like widget_container, JS triggers).
   • Add: Message Queue Usage (from wet-workflows.mdc).
   • Add: Error Handling patterns (from wet-workflows.mdc).

Concrete Response: Draft .cursorrules Consolidation

Here’s a draft incorporating the critical patterns from the .mdc files into .cursorrules.

# .cursorrules – Pipulate Local-First Development Guidelines

## Core Philosophy
1.  **Local-First, Single-Tenant Only:** Treat as a local desktop app (like Electron but with Nix). State is server-side and observable (DictLikeDB/JSON, MiniDataAPI/SQLite). NO enterprise/multi-tenant patterns.
2.  **Server-Side State Only:** NO client-side state management (React, Redux) or complex ORMs.
3.  **FastHTML/HTMX UI:** UI is server-rendered HTML via Python objects. HTMX handles dynamic updates. NO JS frameworks, NO templates (Jinja2).
4.  **WET Workflows:** Linear, explicit steps, often ported from Jupyter. State flows forward. Use `Step` namedtuple and `step_XX`/`step_XX_submit` methods.
5.  **DRY CRUD Apps:** Use `BaseCrud` for standard table management.
6.  **Local LLM (Ollama):** Integrated for chat and potential automation.
7.  **Nix Environment:** Use `nix develop .#quiet` for AI interaction. See `README.md` for setup.

## What to Avoid
* Enterprise/Multi-Tenant Architectures (heavy ORMs, session state, distributed systems).
* Over-Engineering/Unnecessary Abstractions.
* Client-Side Frameworks (React, Vue, Angular, etc.) & Build Steps.
* Implicit Event Bubbling for workflow progression (Use explicit triggers).

## Critical Patterns - DO NOT MODIFY THESE

### 1. HTMX Chain Reaction (Workflow Step Progression)
* **Purpose:** Ensures reliable, automatic advancement between workflow steps.
* **Mechanism:** Each step's completion response *must* explicitly trigger the next step.
* **CRITICAL CODE:** The following `Div` containing the next step's loader MUST be included in the return statement of **both** `step_XX` (when showing completed state) and `step_XX_submit`.

    ```python
    # CRITICAL CHAIN REACTION COMPONENT - DO NOT MODIFY Attributes
    Div(id=next_step_id, hx_get=f"/{app_name}/{next_step_id}", hx_trigger="load")
    ```
* **Full Context Example (in step_XX_submit):**
    ```python
    return Div(
        # Revert control or other completed step content
        pip.revert_control(step_id=step_id, app_name=app_name, message=..., steps=steps),

        # CRITICAL: Explicit trigger for the *next* step
        Div(id=next_step_id, hx_get=f"/{app_name}/{next_step_id}", hx_trigger="load"),

        # CRITICAL: Outer div must retain the *current* step's ID
        id=step_id
    )
    ```
* **NEVER:** Remove `hx_trigger="load"`. Relying on event bubbling is unreliable here.
* **Reference:** See `workflow_implementation_guide.md` and `80_splice_workflow.py` for details.

### 2. Workflow Dual Identity (Filename vs. APP_NAME)
* **Filename:** Determines URL endpoint (e.g., `110_abc_workflow.py` -> `/abc_workflow`). User-facing. Can be changed carefully.
* **APP_NAME Constant:** Determines database storage key (`APP_NAME = "abc"`). Internal ID. **Must remain stable** to preserve existing workflow data.
* **Best Practice:** Use different values (e.g., descriptive filename, simpler `APP_NAME`) for flexibility. Allows URL improvements without breaking data.

## Further Guidance
* For detailed workflow implementation steps (adding steps, placeholders, state management, widgets), consult **`workflow_implementation_guide.md`**.
* For Nix environment setup and usage, consult **`README.md`**.

Next Steps & Refinement

This draft .cursorrules captures the most critical, high-frequency patterns. To continue the consolidation:

1. Refine README.md: Prompt me to integrate the essential Nix setup instructions from nix-rules.mdc into the README.md, perhaps creating/enhancing “Installation” and “Developer Setup” sections.
2. Build workflow_implementation_guide.md: Prompt me section by section to generate the content for this guide, incorporating details from placeholder-step-pattern.mdc and wet-workflows.mdc (e.g., “Step Definition Structure,” “Implementing step_XX/step_XX_submit Handlers,” “Placeholder Step Pattern,” “State Management Helpers,” etc.). This allows us to stay within response limits.
3. Review & Iterate: After drafting content for each target file, we can review and refine based on your feedback and the specific needs of each audience.

This iterative approach, focusing on one document or section at a time, should be effective given the response size constraints. Let me know which section you’d like to tackle next!

AI Analysis

• Title/Headline Ideas & Filenames:
  • Title: Developing Pipulate’s Parameter Buster Workflow for Botify Users Filename: pipulate-parameter-buster-workflow-botify.md
  • Title: Using AI (Gemini, Cursor) to Consolidate Pipulate Documentation and .cursorrules Filename: ai-gemini-cursor-consolidate-pipulate-documentation-cursorrules.md
  • Title: Reflections on Agentic Coding, Botify APIs, and Streamlining Pipulate Development Filename: agentic-coding-botify-api-streamlining-pipulate-development.md
  • Title: Implementing HTMX Chain Reactions and Managing Dev Context in Pipulate Filename: pipulate-htmx-chain-reactions-managing-dev-context.md
  • Title: Wrangling AI Assistants, Botify Quirks, and Docs for the Pipulate Project Filename: wrangling-ai-assistants-botify-quirks-pipulate-docs.md
• Strengths:
  • Authenticity: Provides a transparent, real-time look into the developer’s thought process, challenges (Botify API complexity, AI limitations), and workflow.
  • Technical Detail: Offers specific insights into the Pipulate architecture, Botify integration issues, HTMX patterns, and AI interaction strategies (.cursorrules, prompting).
  • Timeliness: Discusses current trends and tools in AI-assisted development (agentic coding, specific AI models, editor integrations, MCP protocol).
  • Practical Focus: Centers on solving concrete problems (crawl budget waste, documentation management, AI context).
• Weaknesses:
  • Context Dependency: Heavily assumes reader familiarity with Pipulate, Botify, specific AI tools (Cursor, Gemini), Nix, and advanced web/SEO concepts.
  • Mixed Focus: Interweaves specific project updates (Parameter Buster), general AI reflections, API troubleshooting, and documentation strategy, potentially fragmenting the narrative.
  • Meta-Heavy: Significant portions focus on the process of documentation and AI interaction itself, which might dilute the core subject matter for readers primarily interested in Pipulate or the Parameter Buster feature.
  • Potential Obsolescence: Specific details regarding AI tool features (Cursor modes, Gemini issues) or protocols could become dated rapidly.
• AI Opinion: This text offers significant potential value, particularly as a detailed development log and a source for future, more structured documentation. It captures authentic challenges and solutions related to specific technologies (Pipulate, Botify, Nix, HTMX) and contemporary AI-assisted development practices (agentic coding, AI prompting, documentation consolidation). While its clarity is high for those deeply familiar with the project and technologies discussed, the mix of personal reflection, technical deep-dives, and meta-commentary on AI interaction makes it less accessible to a general audience. Its usefulness lies in its specific technical insights, the captured thought process, and the concrete documentation strategy outlined with the AI’s help (via the included Gemini response).