One Card Flow

By Mike Levin

Getting Started with OneCardFlow

I’m ready to get started with OneCardFlow.

A lot can be done with just one card, and my earlier temptation was to jump into Anywidget and start with the integrations straight away. But you know what’s more fundamental? You know what the muscle memory habits I’m trying to get into my finger are? You know the one version of state that can be client-side that’s okay? If it’s not server-side cookies with my dict-like-DB object, then you know what it could still be client-side without any friggin JavaScript library or session state tracking nonsense, yet still be valid for a card?

The DOM and HTMX

It’s called the DOM, aka document object model. Whether I want to pretend it or not, it’s still there, and it still can be manipulated, and so long as a page refresh doesn’t occur, it’s still persistent in the single page application mode in which these pipeline workflows exist. Consequently, it is HTMX that I need to learn and get better at. Consequently, it is the examples on these two key very important pages that I need to pay attention to: htmx.org/reference/ and htmx.org/examples/. Not to hard to remember, but easy to forget. The music of the pipes is greatly the sound of HTMX.

Server-Side State and Forms

And so consequently, even with my .cursorrules cursing of server-side state, the state of the matter is that I can affect server-side state with HTMX in a web form, and that is valid. That is a valid way to collect multiple points of data, the number of which I do not really know ahead of time in a single card. If I want to collect anywhere from 1 to 100 items for a list of indeterminate size, we can do that on a single card using the example… the example… the example… oddly, there is none.

There’s an example to delete a row and to edit a row but not to add a row, haha! It’s almost like fate is answering my mini-app vs. textarea element. And this in itself should be a little lesson for me.

The Table Generation Dilemma

First, so what? If I wanted a little app that adds rows to an HTML table, I totally could do that. Recently in the creation of the CSV file browser I added the dominate PyPI (pip installable) package to make making old school HTML tables easy, and thus have now mixed the upper-case FastHTML component names that shadow HTML element names

Huh? Yeah, it takes me a moment to wrap my head around what I did. But I have one set of Python functions when I code that come from FastHTML called FT components. They’re all listed here and they give me a bunch of functions that will help me build tables: ‘Tbody’, ‘Td’, ‘Tfoot’, ‘Th’, ‘Thead’, and ‘Tr’, but I used dominate which does almost the same thing with lower-case versions because AI told me to because it was trained on dominate before it was trained on fasthtml, and I was too dumb to spot it in time. Duhhhh.

The Library Mismatch Adventure

And then I went and tried to fix… and discovered a rabbit hole that is best avoided, and so I am going to leave the dominate import in place and the use of it to construct the CSV file browser. But I made some discoveries along the way worth sharing. This was my prompt to Claude:

Me: Read my @.cursorrules And then look at these functions to draw a little CSV file browser. And then look at @components.py. Now why would I use @dominate when I have FastHTML FT components?

Consider from fasthtml.common import * which already brings all the FT components we need into global namespace as clean HTML-named-like Python functions per @fasthtml/fastapp.py which imports @basics.py which imports @components.py which dumps the following into global:

__all__ = ['named', 'html_attrs', 'hx_attrs', 'hx_attrs_annotations', 'show', 'attrmap_x', 'ft_html', 'ft_hx', 'File',
           'fill_form', 'fill_dataclass', 'find_inputs', 'html2ft', 'sse_message', 'A', 'Abbr', 'Address', 'Area',
           'Article', 'Aside', 'Audio', 'B', 'Base', 'Bdi', 'Bdo', 'Blockquote', 'Body', 'Br', 'Button', 'Canvas',
           'Caption', 'Cite', 'Code', 'Col', 'Colgroup', 'Data', 'Datalist', 'Dd', 'Del', 'Details', 'Dfn', 'Dialog',
           'Div', 'Dl', 'Dt', 'Em', 'Embed', 'Fencedframe', 'Fieldset', 'Figcaption', 'Figure', 'Footer', 'Form', 'H1',
           'H2', 'H3', 'H4', 'H5', 'H6', 'Head', 'Header', 'Hgroup', 'Hr', 'Html', 'I', 'Iframe', 'Img', 'Input', 'Ins',
           'Kbd', 'Label', 'Legend', 'Li', 'Link', 'Main', 'Map', 'Mark', 'Menu', 'Meta', 'Meter', 'Nav', 'Noscript',
           'Object', 'Ol', 'Optgroup', 'Option', 'Output', 'P', 'Picture', 'PortalExperimental', 'Pre', 'Progress', 'Q',
           'Rp', 'Rt', 'Ruby', 'S', 'Samp', 'Script', 'Search', 'Section', 'Select', 'Slot', 'Small', 'Source', 'Span',
           'Strong', 'Style', 'Sub', 'Summary', 'Sup', 'Table', 'Tbody', 'Td', 'Template', 'Textarea', 'Tfoot', 'Th',
           'Thead', 'Time', 'Title', 'Tr', 'Track', 'U', 'Ul', 'Var', 'Video', 'Wbr']

The Great Library API Mismatch

…to which Claude has great responses and I try to clean up the CSV file browser to unify on the FastHTML pseudo-HTML tags versus the dominate ones… and it just all explodes in my face. Their APIs are totally different, and I have a strong sense of “if it ain’t broken, don’t fix it”. I have other fish to fry tonight, and this is exactly the kind of rabbit hole that needlessly burns up all your decision fatigue energy, which ends up making you defer the work that you really wanted to do in the first place (the work that could make all the difference) to another day—and which is ultimately, self-defeating.

Late Night Realizations

What’s even worse and more insulting to this whole getting-started for the day (at 11:00 PM at night, haha) is that the HTMX table-manipulation that I got so infatuated with pursuing, the key example for implementing happens to be the one left off the htmx.org site, can all be avoided if only I… if only I… if only I…

The Simple Solution

Just use a textarea box instead of some micro to-do app, hahaha!

Headsmack!

Post-Realization Rubber Ducking with o1 Pro

Rubber Ducky o1 Pro’s the one!
That master-planner is so much fun!
And OpenAI’s hoping people won’t peep
Or seek too deeply how DeepSeek is cheap!

o1 Pro: Yes—sometimes a simple <textarea> is all you really need for multi-line or multi-item input! It can spare you the complexities of an “add row” UI pattern, especially when HTMX does not supply a straightforward example for it, and especially when you’ve already got other pressing tasks. This is exactly the kind of trade-off you want to keep in mind whenever your “gut” (or your LLM assistant) tempts you into building a mini “add row” micro-app.

However, if you do decide you really want that “add a row” pattern in a single card (maybe for practice, or because multiple items in a single card is truly the best user experience), below are some notes that preserve your muscle-memory training in HTMX, remain minimal, and avoid entangling you too deeply in library mismatches (whether dominate or FastHTML).

1. “Add Row” With a Single Card Using HTMX

HTML Setup

<!-- The single card – an HTML form that can add rows dynamically -->
<div id="onecard-container">
  <h3>My Items</h3>
  <table id="items-table" border="1">
    <thead>
      <tr><th>Item</th><th>Action</th></tr>
    </thead>
    <tbody>
      <!-- Existing items (if any) rendered here by server on initial load -->
    </tbody>
  </table>

  <form id="add-item-form"
        hx-post="/onecardflow/add_item"
        hx-target="#items-table tbody"
        hx-swap="afterbegin">
    <input type="text" name="new_item" placeholder="Enter an item..." required />
    <button type="submit">Add Item</button>
  </form>
</div>

Key points:

• hx-post="/onecardflow/add_item": The server route that handles the “Add Item” action.
• hx-target="#items-table tbody": We want to update the <tbody> part of the table.
• hx-swap="afterbegin": Insert new rows at the top of the table (or use "beforeend" if you want them to append at the bottom).

Server Route (Pseudo-Code)

@app.post("/onecardflow/add_item")
def add_item(request):
    form = await request.form()
    new_item = form.get("new_item", "")
    
    # Potentially store it server-side in pipeline state here
    #   pipeline_id = db.get("pipeline_id", "unknown")
    #   state = pipulate.get_state(pipeline_id)
    #   state["items"] = state.get("items", []) + [new_item]
    #   pipulate.set_step_data(pipeline_id, "step_01", {"items": state["items"]})

    # Then just return one TR of HTML
    return Tr(
        Td(new_item),
        Td(
            Form(
                Input(type="hidden", name="delete_item", value=new_item),
                Button("Delete", type="submit"),
                hx_post="/onecardflow/delete_item",
                hx_target="closest tr",
                hx_swap="outerHTML"
            )
        )
    )

• This route just returns a single <tr> snippet. On the client side, HTMX will swap it in.
• Notice you can incorporate the delete pattern from HTMX examples by specifying hx_target="closest tr", hx_swap="outerHTML", etc.

Deleting a Row

Then you have an endpoint:

@app.post("/onecardflow/delete_item")
def delete_item(request):
    form = await request.form()
    item_to_delete = form.get("delete_item", "")
    
    # Possibly remove from pipeline state here
    #   pipeline_id = db.get("pipeline_id", "unknown")
    #   state = pipulate.get_state(pipeline_id)
    #   if item_to_delete in state["items"]:
    #       state["items"].remove(item_to_delete)
    #   pipulate.set_step_data(pipeline_id, "step_01", {"items": state["items"]})

    # Return empty so the row disappears
    # (hx_swap="outerHTML" will replace the row with nothing)
    return ""

And that’s it. So each new row is a pure “row fragment” that you or your framework returns. You can do the final “submit all items” or “finalize” from the server state any time you want—all on one card.

2. Unifying With FastHTML or dominate

• If you want to generate the <tr>...</tr> snippet in Python using FastHTML components, do something like:

  from fasthtml.common import Tr, Td, Form, Input, Button, ...
  return Tr(
      Td(new_item),
      Td(
          Form(
              Input(type="hidden", name="delete_item", value=new_item),
              Button("Delete", type="submit"),
              hx_post="/onecardflow/delete_item",
              hx_target="closest tr",
              hx_swap="outerHTML"
          )
      )
  )
  

• Or if you’re using dominate, do it with that API. The concept remains the same: return a row snippet for partial HTML swap.

If you find yourself half-using dominate and half-using FastHTML in the same code file—it’s not the end of the world. Yes, it’s a bit messy, but if it’s stable and working, you can fix it later when you have more time (or never!).

3. Using a Single <textarea> Instead

If all you really need is “some unbounded list of lines” that the user can type, then a single <textarea> is drastically simpler. One card can do:

# Step 01 form
Form(
  Textarea(name="lines", rows="10", cols="50", placeholder="Enter line after line..."),
  Button("Save", type="submit")
)

Then the server can .splitlines() to get each line, store them, etc. No table manipulation needed, no partial-HTML row swapping, no “delete row” logic. So if you don’t need the row-by-row illusions, the <textarea> is an easy path—no headscratch about how to add or delete items on the client side.

4. Final Thoughts

• Choose the simpler approach unless you explicitly want HTMX practice building an “add row” mini-app.
• One card can do a lot—especially if you’re using ephemeral DOM-based interactions. But keep an eye on your real goals. If you truly need row-by-row manipulation, go for the HTMX approach. If you just want a fluid multi-line input, <textarea> may suffice.
• Be mindful of rabbit holes. If it’s 11:00 PM and your top priority is building something else (e.g. hooking up that Anywidget you’ve been itching to try), a good short-term fix might be the <textarea> approach so you can move on to the fun part.

In short, a “single card” collecting an arbitrary number of items can be as fancy (tables + row additions + partial HTML returns) or as simple (a single <textarea>) as your immediate constraints dictate. Each approach is valid! The trick is deciding which approach is “good enough” for the next step of your pipeline journey.

Challenging AI Confirmation Bias

With PhD-level smart AI, you’re not just writing simple conversation prompts. You’re not even doing next-level prompt engineering (yesterday’s news). Rather, you’re fleshing out the actual full context with articles like this.

Me: Alright, alright, I get it. You will tell me anything I want to hear, Mr. AI Rubber Ducky. But you will try to create a fair spread of options. I do not think this is your purely emergent behavior. I think this is OpenAI wrangling you into being fair-handed. I do not want a confirmation bias, but I also do not want to be fighting against you if I think that I am right and you are just over-trained on all the wrong (overweight enterprise architecture) stuff.

The Tension in AI Interactions

There’s a tension here. I guess that’s why so much of this AI stuff is about tensors, huh? There are internal tensions and forces and torques and directions trying to get you to move or think or decide this way or that. The user’s feedback adds to those forces in all sorts of ways, and prompt and response after prompt and response chains it all up in such a way that your static model determinism is very hard to predict, indeed.

Making the Final Decision

And so in the end, the decision is mine. I will go the way I want in this, and my concerns trump everything else. And my concerns are are moving the project forward as a whole, without getting bogged down with the often devastatingly interesting rabbit hole projects that feel important and feel like foundational pillars that should be woven into the strong base fabric of a project at the beginning. But they’re not. They’re 100% deferrable. Good enough is good enough.

The Balance of Deferral

But that attitude is self-defeating in the other direction. And indeed it would be if all your deferential decisions were not actually right there still on the surface of the wood that you’re whittling, the fuzz and the spurs that you’re not going to forget. They have to be left there on the surface in such a way that they are reminders to your future self to come sand them down and smooth them out— later.

The Case for Simple Solutions

Using an html form textarea instead of a little row-based todo widget built from HTMX, which wouldn’t actually be too hard, is actually a good example. My single card app right now is going to be a textarea app. I mean, actually think of the long-term flexible utility of this as one of the generic card widget types I can forever tap. For example, I really want to make some sort of markdown-to-HTML converter for copy/pasting meeting agendas without volunteering that data to some anonymous web hosted app that offers such services for free—at the mere cost of your data! Take that in-house!

The Pipeline Philosophy

And why as part of a card in a pipeline? Because you can slam them out, and don’t have to think about user interface issues beyond the single card. Data-in/data-out in that Unix pipe flow. You don’t have to “build an app” for that. You just… well, you just port it from how you would implement it in a Jupyter Notebook, for that was the genesis of this workflow pipeline project after all—fast Notebook ports.

The New Age of AI Prompting

I’ll get to the work in a moment, but this is the new-age prompt, stories like this, fleshing out all the background so that your AI coding assistant has real life context! I mean talk about context, right? Now o1 Pro and Claude are both going to know, because I’m going to feed this article to each of them, that this linear progress these pipeline workflows represent is the same thing that happens in both Jupyter Notebooks and Unix pipes.

Managing Global State

Now Jupyter Notebooks have Python’s global namespace to make truly dysfunctional fubar, but it works and is even justifiable because Python’s global namespace is nothing more than the page-level module’s parent namespace, and those are fine for some reason when imported, but not at the top level? Sheesh! Anyhow, even though it’s defensible, we’re going to try to avoid it.

In the case of the Pipulate workflows, it’s avoided by using the JSON blob in the data field of Pipulate’s pipeline record for that workflow’s instance as the single source of truth. We do have a dict-like database in global we can use for server-side cookies here and there, but for all that state stuff tied to the pipeline instance, it really gets wrapped into that JSON blob card-by-card. The entire workflow end-to-end can be reproduced from a single pipeline record, and mostly just from that data field JSON blob.

The Pipeline Schema

pipeline={           # To "pipulate" is use this for a Unix pipe-like "pipeline" workflow: Card 1 | Card 2 | Card 3
    "url": str,      # A url must be used on Card 1 to initiate a job, and can be plugged in later to from last complete Card step
    "endpoint": str, # The app endpoint, not technically part of composite primary key (yet) but sure useful for MiniDataAPI spec with table.xtra() as a search filter in workflow app ID field searches
    "data": str,     # All jobs get just 1 pipulate record and use a JSON blob to track state for the entire workflow. The JSON blog contains the args and results of each Card for interruptionless pattern
    "created": str,  # ISO timestamp of first insert
    "updated": str,  # ISO timestamp of last update
    "pk": "url"      # URL is the primary key and can always be plugged into Card 1 to continue a job, jumping to where it left off (the same behavior as any step of workflow processing)
}                    # A FastHTML-friendly querystring-like path can be used to jump to any Card in the workflow: /endpoint/card3

Example Pipeline Record

A Mad-Libs Style Workflow

…and here is an instance of a Pipulate pipeline record after having carried out single workflow. Conceptually, there could be an unlimited number of these in the pipeline table, each one being any state of completion of any variation on a workflow. Each workflow class gets its own endpoint. Every time you carry out a workflow for that endpoint, it gets its own key. One workflow/key combo, one entry in the pipeline table. This is the results of a Mad-Libs style workflow that just kept appending new output to input to build a story.

Understanding Pipeline Records

And that brings us up to now. I think you get it, o1 Pro. All this to put a textarea on a single card app in this workflow system as a first in a series of card building blocks for a Jupyter Notebook / Unix pipe like system that generally stores its stuff in one of these pipeline records, but can occasionally end-run with a persistent server-side cookie generally reserved for user session metadata, but can occasionally be used for per-app (or per workflow) metadata as it is here with pipeline_id’s. When in a running instance of a pipeline workflow, you never need struggle to know your pipeline_id, as you can just check db['pipeline_id'], and have enough really then to construct full state by doing a lookup on the record. Thus, never a mystery! Full interruptability. Full resumeability.

OneCardFlow Architecture

And with OneCardFlow, it’s not really just one card. There’s the surrounding scaffolding of a single workflow instance, reminiscent of BridgeFlow, TenCardFlow and LinkGraphFlow. It will have an __init__ with all its self-setting, step planning and route defining. It will have an landing with its ID-acquiring (yes, the field is still named url for now), and it will have an init to actually create the initial pipeline record (if needed) and start the chain-reaction of placeholders. And then it will have a step_01, a step_01_submit, a finalize and an unfinalize.

Key Differences

The key difference between this one and the other workflows is that it will have a textarea on step_01, and will have only step_01 as far as the step_xx cards go.

Give me the first copy/paste version. Let me know if you need samples provided again of the prior workflows like BridgeFlow, TenStepFlow or LinkGraphFlow.

Please and thank you!

o1 Pro: Below is a copy/paste–ready OneCardFlow class, modeled after the pattern in your other pipeline workflows. It includes:

• Minimal pipeline scaffolding (landing/init steps).
• A single “card” (step_01) that displays a textarea for user input.
• A finalize step (step_02) so you can lock/unlock state if desired.
• An unfinalize route for reverting finalization.
• Enough placeholders and HTMX triggers to preserve consistency with your existing flows like BridgeFlow, TenCardFlow, or LinkGraphFlow.

Feel free to tweak or expand:

class OneCardFlow:
    """
    A single-card pipeline workflow example.
    - step_01: Displays a textarea for user input, saves to pipeline state.
    - step_02: Finalizes or reverts the pipeline.

    Patterns:
      - landing() collects pipeline_id
      - init() creates pipeline record & triggers placeholders
      - step_01 / step_01_submit handle user input
      - step_02 / step_02_submit handle finalize or revert
      - unfinalize() allows changes after finalization
    """

    def __init__(self, app, pipulate, prefix="/onecard", llm_enabled=False):
        self.app = app
        self.pipulate = pipulate
        self.prefix = prefix
        self.llm_enabled = llm_enabled

        # We do two steps:
        #  ("input", step_01) => user data
        #  ("finalized", step_02) => final lock
        self.STEPS = [
            ("input", "step_01", "SingleCard"),
            ("finalized", "step_02", "Finalize")
        ]

        routes = [
            (f"{prefix}",                 self.landing),
            (f"{prefix}/init",            self.init, ["POST"]),
            (f"{prefix}/step_01",         self.step_01),
            (f"{prefix}/step_01_submit",  self.step_01_submit, ["POST"]),
            (f"{prefix}/step_02",         self.step_02),
            (f"{prefix}/step_02_submit",  self.step_02_submit, ["POST"]),
            (f"{prefix}/unfinalize",      self.unfinalize, ["POST"])
        ]
        for path, handler, *methods in routes:
            method_list = methods[0] if methods else ["GET"]
            self.app.route(path, methods=method_list)(handler)

    async def landing(self):
        """
        GET /onecard
        Quick landing page to set pipeline_id; calls /init to actually start flow.
        """
        return Container(
            Card(
                H2("One-Card Flow"),
                P("A single textarea is all you need sometimes!"),
                Form(
                    self.pipulate.wrap_with_inline_button(
                        Input(
                            type="text",
                            name="pipeline_id",
                            placeholder="Enter or resume your single-card ID here",
                            required=True,
                            autofocus=True
                        ),
                        button_label="Begin SingleCard Workflow"
                    ),
                    hx_post=f"{self.prefix}/init",
                    hx_target="#onecard-container"
                )
            ),
            Div(id="onecard-container")
        )

    async def init(self, request):
        """
        POST /onecard/init
        - Sets pipeline_id
        - Calls pipeline.initialize_if_missing(...) with endpoint
        - Returns placeholders from step_01..step_02
        """
        form = await request.form()
        pipeline_id = form.get("pipeline_id", "untitled")
        db["pipeline_id"] = pipeline_id

        # Create pipeline record if needed
        self.pipulate.initialize_if_missing(pipeline_id, {
            "endpoint": self.prefix  # Store endpoint info in record
        })

        # Generate placeholders for step_01 & step_02
        placeholders = self.pipulate.generate_step_placeholders(self.STEPS, self.prefix, start_from=0)
        return Div(*placeholders, id="onecard-container")

    async def step_01(self, request):
        """
        GET /onecard/step_01
        Single card that shows a textarea.
        If already set, show revert control or locked card if finalized.
        """
        pipeline_id = db.get("pipeline_id", "untitled")
        step1_data = self.pipulate.get_step_data(pipeline_id, "step_01", {})
        user_text = step1_data.get("text", "")

        # Check if pipeline is finalized
        step2_data = self.pipulate.get_step_data(pipeline_id, "step_02", {})
        if "finalized" in step2_data:
            # Already finalized => locked
            return Div(
                Card(f"Your single-card text: {user_text} (locked)"),
                Div(id="step_02", hx_get=f"{self.prefix}/step_02", hx_trigger="load")
            )

        # If step_01 data is present but not finalized => revert control
        if user_text:
            return Div(
                self.pipulate.revert_control(
                    url=pipeline_id,
                    step_id="step_01",
                    prefix=self.prefix,
                    message=f"Your single-card text: {user_text}",
                    target_id="onecard-container"
                ),
                Div(id="step_02", hx_get=f"{self.prefix}/step_02", hx_trigger="load")
            )

        # Otherwise => show form with a textarea
        return Div(
            Card(
                H3("One Card: Enter your data"),
                Form(
                    Textarea(
                        name="text",
                        placeholder="Type anything here...",
                        rows="6",  # Adjust for your preference
                        cols="50"  # ...
                    ),
                    Button("Save Text", type="submit"),
                    hx_post=f"{self.prefix}/step_01_submit",
                    hx_target="#step_01",
                    hx_swap="outerHTML"
                )
            ),
            Div(id="step_02"),
            id="step_01"
        )

    async def step_01_submit(self, request):
        """
        POST /onecard/step_01_submit
        Saves user input from the textarea, re-renders placeholders.
        """
        pipeline_id = db.get("pipeline_id", "untitled")
        form = await request.form()
        user_text = form.get("text", "")

        # Save to pipeline
        self.pipulate.set_step_data(pipeline_id, "step_01", {"text": user_text})

        # Show revert control + next step
        return Div(
            self.pipulate.revert_control(
                url=pipeline_id,
                step_id="step_01",
                prefix=self.prefix,
                message=f"Your single-card text: {user_text}",
                target_id="onecard-container"
            ),
            Div(id="step_02", hx_get=f"{self.prefix}/step_02", hx_trigger="load")
        )

    async def step_02(self, request):
        """
        GET /onecard/step_02
        Final step. Allows finalize or shows locked state if already final.
        """
        pipeline_id = db.get("pipeline_id", "untitled")
        step2_data = self.pipulate.get_step_data(pipeline_id, "step_02", {})
        step1_data = self.pipulate.get_step_data(pipeline_id, "step_01", {})
        user_text = step1_data.get("text", "")

        # If finalized => show locked card
        if "finalized" in step2_data:
            return Card(
                f"Single-card text final: {user_text}",
                P("This workflow is finalized. Revert is locked."),
                style="color: green;"
            )

        # else => let user finalize
        return Div(
            Card(
                H3("Finalize OneCardFlow"),
                P("Lock this single card input?"),
                Form(
                    Button("Finalize", type="submit"),
                    hx_post=f"{self.prefix}/step_02_submit",
                    hx_target="#step_02",
                    hx_swap="outerHTML"
                )
            ),
            id="step_02"
        )

    async def step_02_submit(self, request):
        """
        POST /onecard/step_02_submit
        Mark pipeline as finalized => no revert allowed.
        """
        pipeline_id = db.get("pipeline_id", "untitled")
        self.pipulate.set_step_data(pipeline_id, "step_02", {"finalized": True})

        # Return locked final view
        step1_data = self.pipulate.get_step_data(pipeline_id, "step_01", {})
        user_text = step1_data.get("text", "")
        return Card(
            f"Final SingleCard text: {user_text}",
            P("Finalized. No further editing."),
            style="color: green;"
        )

    async def unfinalize(self, request):
        """
        POST /onecard/unfinalize
        Removes 'finalized' from step_02 data, unlocking revert controls.
        """
        pipeline_id = db.get("pipeline_id", "untitled")
        if not pipeline_id:
            return P("No pipeline found. Please start again.", style="color:red;")

        state = self.pipulate.get_state(pipeline_id)
        if "step_02" in state and "finalized" in state["step_02"]:
            del state["step_02"]["finalized"]
            self.pipulate._save_state(pipeline_id, state)

        # Show placeholders from beginning:
        placeholders = self.pipulate.generate_step_placeholders(self.STEPS, self.prefix, start_from=0)
        return Div(*placeholders, id="onecard-container")

Key Points

1. Single “step_01”: Where you gather all user input via a single <textarea>.
2. step_02: Your minimal “Finalize” step. We preserve the possibility of revert or final lock, matching the style of BridgeFlow/TenCardFlow.
3. unfinalize: Lets you remove "finalized" from the state—just in case.
4. HTMX: Each step is triggered by a placeholder Div(id="step_xx", hx_get=..., hx_trigger="load"). The forms do hx_post=... to the _submit routes, and the partial HTML is returned.
5. Integration: If you want LLM commentary, you could call await self.pipulate.explain(...) in step_01 or step_02 just like you do in BridgeFlow, but here it’s set to llm_enabled=False by default.

Enjoy your OneCardFlow as a building block for more advanced “widget card” expansions!

First Pass of o1 Pro’s One Card Flow

Amazing, the code was indeed copy/paste ready and delivered as promised. There are a few observations and items to iron out.

First, the observations. Now that I have a number of plugin apps and workflow examples, the built-in auto-suggest style code assistant is kicking-in big-time. For example, even though the above code was drop-in ready and generated no errors, it was not yet linked-in through the system that registers it on the navigation menu or page layout, but the AI coding assistant that expresses itself as auto-suggest was all over it. I only needed to put my cursor in location and it was auto-suggesting how to link OneCardFlow in, and I needed only tap the tab key for it to auto-complete. I went to both locations, menu and page layout registration and it did the same thing.

This bodes very well for a single-file self-modifying system. All context is right there in front of the LLM. The file you’re in is the only one it needs to look at for full context. Yes, the file can then grow quite large, but if you’re good at navigating large files, it’s way better for flow-state than jumping between files— the mental equivalent to taking your hands off the keyboard for the mouse.

Next, I went on to actually fix it and make it work per my vision. This was no small task. I’m winding up here at around 4:00 AM. I started around 11:00 PM. That was another full day of work, considering the mental energy that really goes into coding, trouble-shooting, debugging and refining like this. I mean, it’s not physical labor but the brain does consume the most oxygen and energy of the body for a reason. Thinking is hard!

It’s no wonder we want to offload so much of it to the AI’s. But that which remains in our own human wheelhouse makes so much difference. At least today. It will be interesting to see how that holds up over time.

But for now, what I most want to do is to lock in those nuances, those deltas between what o1 Pro gave me on its first pass, and what I worked out with Claude 3.5 Sonnet over the past five hours. The better I lock these things in in some sort of style guide or instruction manual for future workflow design sessions, the smoother they’ll go.

Locking-in Learnings with o1 Pro

PhD-level intelligence you say? Hmmm, how to use that well? Maybe making it compare two things exhaustively and draw conclusions from the comparisons? And then to formalize what it learned from those conclusions? Let’s see…

Me: Exhaustively and in pedantic meticulous detail, compare what turned out to be the final implementation with what you provided me, and given what you know about the workflow do a deep analysis for the changes and then do an instruction guide that you yourself could use as part of your prompt instructions to start new workflows correctly abiding by the new pattern given what you learned from the comparison, analysis and knowledge of these workflows.

class OneCardFlow:
    def __init__(self, app, pipulate, prefix="/onecard", llm_enabled=False):
        self.app = app
        self.pipulate = pipulate
        self.prefix = prefix
        self.llm_enabled = llm_enabled

        # Match BridgeFlow's pattern exactly:
        self.STEPS = [
            ("text", "step_01", "SingleCard"),  # Data collection
            ("finalized", "step_02", "Final")   # Finalization only
        ]

        routes = [
            (f"{prefix}",                 self.landing),
            (f"{prefix}/init",            self.init, ["POST"]),
            (f"{prefix}/step_01",         self.step_01),
            (f"{prefix}/step_01_submit",  self.step_01_submit, ["POST"]),
            (f"{prefix}/step_02",         self.step_02),
            (f"{prefix}/step_02_submit",  self.step_02_submit, ["POST"]),
            (f"{prefix}/unfinalize",      self.unfinalize, ["POST"]),
            (f"{prefix}/jump_to_step",    self.jump_to_step, ["POST"])
        ]
        for path, handler, *methods in routes:
            method_list = methods[0] if methods else ["GET"]
            self.app.route(path, methods=method_list)(handler)

    async def landing(self):
        """
        GET /onecard
        Quick landing page to set pipeline_id; calls /init to actually start flow.
        """
        return Container(
            Card(
                H2("One-Card Flow"),
                P("A single textarea is all you need sometimes!"),
                Form(
                    self.pipulate.wrap_with_inline_button(
                        Input(
                            type="text",
                            name="pipeline_id",
                            placeholder="🗝 Enter or resume your single-card ID here",
                            required=True,
                            autofocus=True
                        ),
                        button_label="Begin One Card Flow 🔑",
                        button_class="secondary",
                    ),
                    hx_post=f"{self.prefix}/init",
                    hx_target="#onecard-container"
                )
            ),
            Div(id="onecard-container")
        )

    async def init(self, request):
        """
        POST /onecard/init
        - Sets pipeline_id
        - Calls pipeline.initialize_if_missing(...) with endpoint
        - Returns placeholders from step_01..step_02
        """
        form = await request.form()
        pipeline_id = form.get("pipeline_id", "untitled")
        db["pipeline_id"] = pipeline_id

        # Create pipeline record if needed
        self.pipulate.initialize_if_missing(pipeline_id, {
            "endpoint": self.prefix  # Store endpoint info in record
        })

        # Generate placeholders for step_01 & step_02
        placeholders = self.pipulate.generate_step_placeholders(self.STEPS, self.prefix, start_from=0)
        return Div(*placeholders, id="onecard-container")

    async def step_01(self, request):
        """GET /onecard/step_01 - Shows textarea or revert control"""
        pipeline_id = db.get("pipeline_id", "untitled")
        step1_data = self.pipulate.get_step_data(pipeline_id, "step_01", {})
        step2_data = self.pipulate.get_step_data(pipeline_id, "step_02", {})
        user_text = step1_data.get("text", "")

        # Check if pipeline is finalized
        if "finalized" in step2_data:
            return Div(
                Card(self._fmt(user_text)),
                Div(id="step_02", hx_get=f"{self.prefix}/step_02", hx_trigger="load")
            )

        # If we have data AND step_02 exists => show revert control
        if user_text and step2_data:
            return Div(
                self.pipulate.revert_control(
                    url=pipeline_id,
                    step_id="step_01",
                    prefix=self.prefix,
                    message=self._fmt(user_text),
                    target_id="onecard-container"
                ),
                Div(id="step_02", hx_get=f"{self.prefix}/step_02", hx_trigger="load")
            )

        # Show form (first time OR after jump)
        return Div(
            Card(
                H3("Enter your text:"),
                Form(
                    Textarea(
                        user_text,  # Existing text will be here
                        name="text",
                        placeholder="Type anything here...",
                        rows="6",
                        required=True,
                        autofocus=True
                    ),
                    Button("Save", type="submit"),
                    hx_post=f"{self.prefix}/step_01_submit",
                    hx_target="#step_01"
                )
            ),
            Div(id="step_02"),
            id="step_01"
        )

    async def step_01_submit(self, request):
        """
        POST /onecard/step_01_submit
        Saves user input from the textarea, re-renders placeholders.
        """
        pipeline_id = db.get("pipeline_id", "untitled")
        form = await request.form()
        user_text = form.get("text", "")

        # Save to pipeline
        self.pipulate.set_step_data(pipeline_id, "step_01", {"text": user_text})

        # Show revert control + next step
        return Div(
            self.pipulate.revert_control(
                url=pipeline_id,
                step_id="step_01",
                prefix=self.prefix,
                message=self._fmt(user_text),
                target_id="onecard-container"
            ),
            Div(id="step_02", hx_get=f"{self.prefix}/step_02", hx_trigger="load")
        )

    async def step_02(self, request):
        """GET /onecard/step_02 - Final step with finalize button"""
        pipeline_id = db.get("pipeline_id", "untitled")
        step2_data = self.pipulate.get_step_data(pipeline_id, "step_02", {})
        
        # If finalized => show final card
        if "finalized" in step2_data:
            return self._final_card()

        # If step_01 has data => show finalize option
        step1_data = self.pipulate.get_step_data(pipeline_id, "step_01", {})
        if step1_data.get("text"):
            return Card(
                H3("Ready to finalize?"),
                P("Your text is saved. Lock it in?"),
                Form(
                    Button("Finalize", type="submit"),
                    hx_post=f"{self.prefix}/step_02_submit",
                    hx_target="#onecard-container",
                    hx_swap="outerHTML"
                ),
                id="step_02"
            )

        return Div(id="step_02")

    async def step_02_submit(self, request):
        """POST /onecard/step_02_submit - Sets finalized flag"""
        pipeline_id = db.get("pipeline_id", "untitled")
        self.pipulate.set_step_data(pipeline_id, "step_02", {"finalized": True})
        
        # Re-generate all placeholders in finalized mode
        placeholders = self.pipulate.generate_step_placeholders(self.STEPS, self.prefix, start_from=0)
        return Div(*placeholders, id="onecard-container")

    def _final_card(self):
        """Shows final card with text and unfinalize button"""
        pipeline_id = db.get("pipeline_id", "untitled")
        text = self.pipulate.get_step_data(pipeline_id, "step_01", {}).get("text", "")
        
        return Card(
            H3("SingleCard Complete"),
            P("Finalized. No further editing."),
            Form(
                Button("Edit Again", type="submit", style="background-color: #f66;"),
                hx_post=f"{self.prefix}/unfinalize",
                hx_target="#onecard-container",
                hx_swap="outerHTML"
            ),
            style="color: green;",
            id="onecard-final"
        )

    async def unfinalize(self, request):
        """POST /onecard/unfinalize - Removes finalized state"""
        pipeline_id = db.get("pipeline_id", "untitled")
        if not pipeline_id:
            return P("No pipeline found", style="color:red;")

        # Clear finalized state
        state = self.pipulate.get_state(pipeline_id)
        if "step_02" in state and "finalized" in state["step_02"]:
            del state["step_02"]["finalized"]
            self.pipulate._save_state(pipeline_id, state)

        # Regenerate placeholders
        placeholders = self.pipulate.generate_step_placeholders(self.STEPS, self.prefix, start_from=0)
        return Div(*placeholders, id="onecard-container")

    async def jump_to_step(self, request):
        """
        POST /onecard/jump_to_step
        Rolls back to a prior step, clearing only subsequent steps.
        """
        form = await request.form()
        step_id = form.get("step")
        pipeline_id = db.get("pipeline_id", "untitled")

        # Get the current state before clearing
        state = self.pipulate.get_state(pipeline_id)
        
        # Find the index of the step we're jumping to
        step_index = next(i for i, (_, sid, _) in enumerate(self.STEPS) if sid == step_id)
        
        # Clear only steps AFTER the one we're jumping to
        for _, sid, _ in self.STEPS[step_index + 1:]:
            if sid in state:
                del state[sid]
                
        # Save the modified state
        self.pipulate._save_state(pipeline_id, state)

        # Re-generate placeholders from the beginning
        placeholders = self.pipulate.generate_step_placeholders(self.STEPS, self.prefix, start_from=0)
        return Div(*placeholders, id="onecard-container")

    def _fmt(self, text: str, with_check: bool = False) -> P:
        """
        Formats user text with consistent styling and optional checkmark.
        
        Args:
            text: The user's text to format
            with_check: Whether to append a checkmark (default: True)
        
        Returns:
            P element containing formatted text
        """
        return P(
            Pre(
                text,
                style=(
                    "white-space: pre-wrap; "
                    "margin-bottom: 0; "
                    "margin-right: .5rem; "
                    "padding: .25rem;"
                )
            ),
            " ✓" if with_check else ""
        )

When o1 Pro Is Instructed to Be Exhaustively Thorough, Meticulous & Pedantic

I like it! I think I’ve got a new prompting technique and style. Of course, it requires writing whole articles like this, and those articles being a part of a huge conversation history. Perhaps that’s not for everyone, but prompting techniques for these huge context window chain-of-thought big-thinker models is so different!

o1 Pro: [Displaying the Details]

Analyzing code evolution

I’m comparing the final code with the proposed snippet, noting differences and detailing steps to start new workflows based on the new pattern.

Analyzing class structure

I’m noting the final code snippet’s class, OneCardFlow, including steps and routes, and comparing it with the proposed snippet, emphasizing differences in step names and finalize terminology.

Tracing additional routes

The final snippet includes an extra route and a different finalize method, noting step_01 logic changes and formatting preferences.

Analyzing decision criteria

I’m noting variations in the logic for step_01 and step_02, including control reversion and placeholder generation, as well as differences in finalize step handling. Progressing through each detail meticulously.

Spotting enhancements

The final snippet introduces _final_card for unified view, Unfinalize for state management, jump_to_step for navigation, and with_check and Pre for text formatting and whitespace.

Below is a two-part response:

1. Exhaustive, pedantic comparison and deep analysis of how the final implementation differs from the earlier snippet we provided (and from typical patterns in your prior flows).
2. An instruction guide you (and any AI collaborator) can follow when starting new pipeline workflows, reflecting these final changes and lessons learned.

1. Exhaustive, Pedantic Comparison & Analysis

Let’s set up a side-by-side comparison. I’ll label each snippet to keep them distinct:

• Snippet A (Assistant’s Earlier Proposal): The code we originally provided in a single-card flow style.
• Snippet B (Your Final Implementation): The final version you ended up with.

1.1. Step Definitions

Snippet A:

self.STEPS = [
    ("input", "step_01", "SingleCard"),
    ("finalized","step_02", "Finalize")
]

Snippet B (Final Implementation):

self.STEPS = [
    ("text", "step_01", "SingleCard"),  # Data collection
    ("finalized", "step_02", "Final")   # Finalization only
]

Differences:

1. Internal Key:
   • Snippet A used "input" as the first step’s internal key.
   • Snippet B uses "text".
2. Step Label:
   • Snippet A labeled the second step as "Finalize".
   • Snippet B labels it "Final".
3. Purpose:
   • Both steps accomplish the same broad goals (data gathering for step_01, finalization for step_02).
   • The difference is purely naming convention: Snippet B aligns with the pattern in BridgeFlow/TenCardFlow, using a simpler label, and uses "text" to highlight that this first step is about “collecting text.”

Analysis:
This difference is primarily cosmetic and naming-based but indicates a preference for:

• Shorter step labels ("Final")
• More direct internal keys ("text").

In some of your other workflows, you used ("name", "step_01", "Name") or ("color", "step_03", "Color"). So using ("text", "step_01", "SingleCard") keeps that pattern of “the first item is the data key.”

1.2. Routes

Snippet A:

routes = [
    (f"{prefix}",                self.landing),
    (f"{prefix}/init",           self.init, ["POST"]),
    (f"{prefix}/step_01",        self.step_01),
    (f"{prefix}/step_01_submit", self.step_01_submit, ["POST"]),
    (f"{prefix}/step_02",        self.step_02),
    (f"{prefix}/step_02_submit", self.step_02_submit, ["POST"]),
    (f"{prefix}/unfinalize",     self.unfinalize, ["POST"])
]

Snippet B (Final Implementation):

routes = [
    (f"{prefix}",                 self.landing),
    (f"{prefix}/init",            self.init, ["POST"]),
    (f"{prefix}/step_01",         self.step_01),
    (f"{prefix}/step_01_submit",  self.step_01_submit, ["POST"]),
    (f"{prefix}/step_02",         self.step_02),
    (f"{prefix}/step_02_submit",  self.step_02_submit, ["POST"]),
    (f"{prefix}/unfinalize",      self.unfinalize, ["POST"]),
    (f"{prefix}/jump_to_step",    self.jump_to_step, ["POST"])  # Extra route
]

Differences:

1. Extra jump_to_step route in Snippet B:
   • The final code supports partial rewinding of pipeline steps (like in BridgeFlow).
   • Snippet A omitted this route, presumably to keep it minimal.
2. Order & Small naming differences (Begin One Card Flow 🔑 vs. Begin SingleCard Workflow). Minor UI text differences in landing(), but that’s not in the route definitions themselves.

Analysis:
jump_to_step is a direct import from your BridgeFlow (or TenCardFlow) pattern, letting the user revert further than the immediate prior step. This indicates you wanted (and ended up with) a more robust revert mechanism. In simpler flows, you might skip it. But in the final version, it’s present for consistency with existing flows.

1.3. landing() Method

Snippet A:

async def landing(self):
    return Container(
        Card(
            H2("One Card Workflow"),
            ...
        ),
        Div(id="onecard-container")
    )

Snippet B (Final Implementation):

async def landing(self):
    return Container(
        Card(
            H2("One-Card Flow"),
            P("A single textarea is all you need sometimes!"),
            Form(
                self.pipulate.wrap_with_inline_button(
                    ...
                ),
                hx_post=f"{self.prefix}/init",
                hx_target="#onecard-container"
            )
        ),
        Div(id="onecard-container")
    )

Differences:

1. Text/Styling: Snippet B has different button label text and placeholders (e.g. “🗝 Enter or resume your single-card ID”).
2. button_class: Snippet B sets button_class="secondary".
3. Overall: Both yield a container with a card, a form, and a div placeholder. Snippet B is more stylized and thematically consistent with your other flows (embedding emojis, etc.).

Analysis:
Functionally identical. The differences are purely UI labeling, extra styling, and theming. Both land on the same concept: “Wait for user input of pipeline_id, post to init, and store the result in #onecard-container.”

1.4. init() Method

Snippet A:

async def init(self, request):
    form = await request.form()
    pipeline_id = form.get("pipeline_id", "untitled")
    db["pipeline_id"] = pipeline_id

    self.pipulate.initialize_if_missing(pipeline_id, {"endpoint": self.prefix})

    placeholders = self.pipulate.generate_step_placeholders(self.STEPS, self.prefix, start_from=0)
    return Div(*placeholders, id="onecard-container")

Snippet B (Final Implementation):

async def init(self, request):
    form = await request.form()
    pipeline_id = form.get("pipeline_id", "untitled")
    db["pipeline_id"] = pipeline_id

    self.pipulate.initialize_if_missing(pipeline_id, {
        "endpoint": self.prefix
    })

    placeholders = self.pipulate.generate_step_placeholders(self.STEPS, self.prefix, start_from=0)
    return Div(*placeholders, id="onecard-container")

Differences:

• None functionally, except for possibly small docstring differences. The code is almost exactly the same.
• Snippet B just has some minor stylistic differences in docstrings or comment lines.

Analysis:
Exactly as expected: sets the pipeline_id, calls initialize_if_missing, returns placeholders.

1.5. step_01() Method

This is where many small logic differences appear.

Snippet A:

async def step_01(self, request):
    pipeline_id = db.get("pipeline_id", "unknown")
    step1_data = self.pipulate.get_step_data(pipeline_id, "step_01", {})
    user_text = step1_data.get("text", "")

    # If user_text => revert control or locked if finalized
    ...
    # else => show form (no pre-filled text)

We had a straightforward approach: if data is present, show revert. If finalized, locked. Otherwise, show an empty <textarea>.

Snippet B (Final Implementation):

async def step_01(self, request):
    pipeline_id = db.get("pipeline_id", "untitled")
    step1_data = self.pipulate.get_step_data(pipeline_id, "step_01", {})
    step2_data = self.pipulate.get_step_data(pipeline_id, "step_02", {})
    user_text = step1_data.get("text", "")

    # If step_02 is "finalized" => locked display
    # elif user_text and step2_data => revert control
    # else => show form, pre-filled with user_text

Differences:

1. Condition for showing revert:
   • Snippet A: If user_text exists (and not finalized), show revert.
   • Snippet B: If user_text and step2_data exist, show revert control. This implies you only revert if step_02 is partially present. This might be for bridging the logic that step_02 might or might not exist yet.
2. Pre-filled <textarea>:
   • Snippet B sets Textarea(user_text, ...), so if user_text is already in step_01, it re-displays it.
   • Snippet A did not pass the user_text back into the <textarea>—the user would have had a blank.
3. Use of _fmt(user_text):
   • Snippet B calls message=self._fmt(user_text) for revert control to format the user’s text.
   • Snippet A used a direct string. No _fmt helper.
4. Lock Logic:
   • Snippet B specifically checks if "finalized" in step2_data: and returns a locked Div with Card(self._fmt(user_text)).
   • Snippet A did the same in principle but was simpler or less explicit about formatting.

Analysis:
Snippet B more closely matches how BridgeFlow logic branches (the “if step_2_data => revert” approach). Also it ensures a user sees the text in the <textarea> if they return to step_01. This is an improvement in user experience: they can edit their existing text, not retype from scratch.

1.6. step_01_submit()

Snippet A:

async def step_01_submit(self, request):
    user_text = form.get("text", "")
    self.pipulate.set_step_data(pipeline_id, "step_01", {"text": user_text})

    return Div(
        self.pipulate.revert_control(...),
        Div(id="step_02", hx_get=..., hx_trigger="load")
    )

Snippet B (Final Implementation):

async def step_01_submit(self, request):
    user_text = form.get("text", "")
    self.pipulate.set_step_data(pipeline_id, "step_01", {"text": user_text})

    return Div(
        self.pipulate.revert_control(
            ...
            message=self._fmt(user_text),
        ),
        Div(id="step_02", hx_get=f"{self.prefix}/step_02", hx_trigger="load")
    )

Differences:

1. Formatting: Snippet B uses self._fmt(user_text) in the revert control.
2. No significant structural difference otherwise. The approach (save data → show revert → auto-load next step) is identical.

Analysis:
They’re functionally the same. The final snippet just keeps consistent text-formatting using _fmt() so the user sees a nicely rendered version.

1.7. step_02() Final Step

Snippet A:

async def step_02(self, request):
    # If "finalized" in step_02 => show locked
    # Else => finalize button

Snippet B (Final Implementation):

async def step_02(self, request):
    step2_data = ...
    if "finalized" in step2_data:
        return self._final_card()
    
    step1_data = ...
    if step1_data.get("text"):
        return Card(
            H3("Ready to finalize?"),
            ...
            Form(
                Button("Finalize", ...),
                hx_post=f"{self.prefix}/step_02_submit",
                hx_target="#onecard-container",
                ...
            ),
            id="step_02"
        )
    
    return Div(id="step_02")

Differences:

1. _final_card(): Snippet B calls a separate function that returns the final locked card. Snippet A might have inlined it.
2. Edge case: If step_01 has no text, Snippet B returns an empty Div(id="step_02"). That ensures no finalize button if the user typed nothing.
3. HTMX target: Snippet B finalizes directly to hx_target="#onecard-container"—Snippet A might have used hx_target="#step_02".

Analysis:
Snippet B is a bit more robust: it checks that step_01 has text before letting you finalize. If you arrive at step_02 with no text, you get an empty placeholder. That is a small but possibly crucial UI improvement. Also, factoring out _final_card() is a clean approach.

1.8. step_02_submit()

Snippet A:

async def step_02_submit(self, request):
    self.pipulate.set_step_data(pipeline_id, "step_02", {"finalized": True})
    # Possibly return a locked card or re-generate placeholders

Snippet B (Final Implementation):

async def step_02_submit(self, request):
    self.pipulate.set_step_data(pipeline_id, "step_02", {"finalized": True})
    
    # Re-generate placeholders
    placeholders = self.pipulate.generate_step_placeholders(self.STEPS, self.prefix, start_from=0)
    return Div(*placeholders, id="onecard-container")

Differences:

1. Snippet B always re-generates placeholders from scratch in finalized mode, so the user sees the locked version of step_01 and step_02.
2. In Snippet A, we might have inlined a locked final card. Both are valid patterns.
3. Snippet B is consistent with how BridgeFlow finalizes: calling generate_step_placeholders so everything re-draws in locked mode.

Analysis:
It’s consistent with TenCardFlow/BridgeFlow. Instead of returning a single final card, you “redraw” from step_01 onward, which is how you unify the “locked” state across all steps.

1.9. _final_card()

Snippet A: Didn’t have a separate _final_card function.

Snippet B (Final Implementation):

def _final_card(self):
    text = ...
    return Card(
        H3("SingleCard Complete"),
        ...
        Form(
            Button("Edit Again", ...),
            hx_post=f"{self.prefix}/unfinalize",
            ...
        )
    )

Differences:

• Snippet B factors out the final, locked view.
• It includes an “Edit Again” button that calls unfinalize.
• Snippet A likely had some inline code for a locked card or none at all.

Analysis:
Snippet B uses a separate method for clarity—cleaner, more re-usable. Also ensures consistency with the Monty Python or TenCard style “Try Again” button concept. That’s a direct continuation of your existing finalize–unfinalize pattern.

1.10. unfinalize()

Snippet A:

async def unfinalize(self, request):
    pipeline_id = ...
    if "step_02" in state and "finalized" in state["step_02"]:
        del state["step_02"]["finalized"]
        self.pipulate._save_state(pipeline_id, state)
    
    placeholders = ...
    return Div(*placeholders, id="onecard-container")

Snippet B (Final Implementation):

async def unfinalize(self, request):
    pipeline_id = ...
    state = self.pipulate.get_state(pipeline_id)
    if "step_02" in state and "finalized" in state["step_02"]:
        del state["step_02"]["finalized"]
        self.pipulate._save_state(pipeline_id, state)

    placeholders = ...
    return Div(*placeholders, id="onecard-container")

Differences:

• They’re basically identical. Possibly minor docstring differences or variable naming.

Analysis:
Same logic. No functional difference.

1.11. jump_to_step()

Snippet A: Lacked this method.

Snippet B (Final Implementation):

async def jump_to_step(self, request):
    form = ...
    step_id = form.get("step")
    # Clear steps after step_id
    # Regenerate placeholders
    return Div(*placeholders, id="onecard-container")

Differences:

• Snippet B has a more advanced revert mechanism, partial rollback.
• Snippet A had no partial revert—only immediate revert to the previous step.

Analysis:
This is consistent with your standard “jump_to_step” pattern from BridgeFlow. It’s more advanced than a single-step revert, letting you roll back multiple steps if needed.

1.12. _fmt(...)

Snippet A: No _fmt function. We typically just inserted text or used a simpler approach.

Snippet B (Final Implementation):

def _fmt(self, text: str, with_check: bool = False) -> P:
    return P(
        Pre(text, style="white-space: pre-wrap; ..."),
        " ✓" if with_check else ""
    )

Differences:

1. Pre block*: *Snippet B ensures user text retains line breaks, spacing, etc. This is a nice UX improvement for multiline text.
2. Conditional checkmark: Not used heavily in Snippet B, but the function allows it.
3. One-liner: consolidates all your “pretty display” logic into a single helper.

Analysis:
This is a straightforward improvement in user text formatting. It also keeps the final code consistent if you want to add more stylings later or place it in a card with special highlight.

2. Deep “Lessons Learned” & Final Analysis

1. Naming Conventions:
   • You ended up preferring (“text”, “step_01”, “SingleCard”) instead of (“input”, “step_01”, …) or (“name”, “step_01”, …). This is consistent with your other workflows (Poetflow, BridgeFlow, etc.), where the first item is the “data key” that references the user-provided data (like color, time, etc.).
2. Conditional Revert:
   • The final snippet introduced a nuance: revert is only shown if step_02 data is partially present (i.e. the user loaded it) and step_01 was set. Possibly a small logic difference, but it’s clearly your style from BridgeFlow: “If we have advanced enough to create step_02’s data structure, we must be able to revert to step_01.”
3. Inline vs. Re-Placeholder:
   • The final snippet consistently calls generate_step_placeholders after key transitions (especially finalization). This is your standard pattern in BridgeFlow/TenCardFlow, ensuring everything re-draws in “locked” or “unlocked” mode.
4. Enhanced Partial Revert:
   • The new jump_to_step method shows you want advanced rollback, not just “the prior step.” That’s borrowed from your multi-step flows (BridgeFlow).
5. Refined UI:
   • You introduced _fmt(...) and _final_card(...) to unify the display logic. This keeps code DRY (Don’t Repeat Yourself) and consistent.
   • You also use a pre-filled <textarea> to let the user see/edit previously typed text, which is friendlier than starting blank.
6. Smoother UX:
   • The final snippet checks if step_01 had data before showing finalize in step_02. That prevents an empty finalize scenario.
   • The final snippet uses a locked display if “finalized.”

Overall, the final snippet is more aligned with the patterns from your other multi-step flows, whereas the minimal snippet we gave was a simpler/shorter demonstration.

3. Instruction Guide for Starting New Workflows with the “Final Pattern”

Below is a concise “cheat sheet” that you (or an AI) can follow every time you create a new pipeline flow, ensuring it matches the final style:

3.1. Skeleton Setup

1. Class Definition:

   class MyFlowName:
       def __init__(self, app, pipulate, prefix="/myflow", llm_enabled=False):
           self.app = app
           self.pipulate = pipulate
           self.prefix = prefix
           self.llm_enabled = llm_enabled
              
           self.STEPS = [
               # (data_key, step_id, label), ...
               # (final_key, final_step_id, final_label)
           ]
              
           routes = [
               (f"{prefix}",                self.landing),
               (f"{prefix}/init",           self.init, ["POST"]),
               (f"{prefix}/step_01",        self.step_01),
               (f"{prefix}/step_01_submit", self.step_01_submit, ["POST"]),
               # ...
               (f"{prefix}/unfinalize",     self.unfinalize, ["POST"]),
               (f"{prefix}/jump_to_step",   self.jump_to_step, ["POST"]),
           ]
           for path, handler, *methods in routes:
               method_list = methods[0] if methods else ["GET"]
               self.app.route(path, methods=method_list)(handler)
   

   • Why? Consistent with BridgeFlow, TenCardFlow, LinkGraphFlow.
   • If you need more steps, add them with ("color", "step_02", "Color"), etc.

3.2. The landing() Approach

• Must create a container with:
  1. A short form to capture pipeline_id.
  2. hx_post that calls init.
  3. Div(id="...-container") to hold subsequent placeholders.

Example:

async def landing(self):
    return Container(
        Card(
            H2("Welcome to MyFlow!"),
            Form(
                self.pipulate.wrap_with_inline_button(
                    Input(name="pipeline_id", required=True, autofocus=True),
                    button_label="Begin"
                ),
                hx_post=f"{self.prefix}/init",
                hx_target="#myflow-container"
            )
        ),
        Div(id="myflow-container")
    )

3.3. init() Must:

1. Parse pipeline_id from form.
2. db["pipeline_id"] = pipeline_id.
3. self.pipulate.initialize_if_missing(pipeline_id, {"endpoint": self.prefix}).
4. Generate placeholders with generate_step_placeholders(self.STEPS, self.prefix, start_from=0).
5. Return a single container Div.

3.4. Each step_X() Method

• Follows the pattern:
  • Check if “finalized”: If yes, show locked version.
  • Else if data is present, show revert control.
  • Else show form.

Pro tips:

• If your step is a “text” card, pre-fill the <textarea> with any existing text so the user can see what they typed before.
• If your step is more advanced (like a dropdown, a field set, etc.), do the same approach: pre-fill existing data.

3.5. Each step_X_submit():

• Gather form data.
• self.pipulate.set_step_data(pipeline_id, "step_X", {...}).
• Return a Div that:
  1. Shows revert control.
  2. Has Div(id="step_Y", hx_get=..., hx_trigger="load") to auto-load the next step.

3.6. Finalization Step

• Always define a final step ID (like step_99 or step_02 in a 2-step flow).
• In step_final(), if "finalized" is in the data, return a locked final card. Otherwise, show a “Finalize” button that calls step_final_submit.

3.7. step_final_submit():

• self.pipulate.set_step_data(pipeline_id, "step_final", {"finalized": True})
• Then re-generate placeholders from step_01..step_final:

  placeholders = self.pipulate.generate_step_placeholders(self.STEPS, self.prefix, start_from=0)
  return Div(*placeholders, id="myflow-container")
  

3.8. Unfinalize

• Must remove "finalized" from the final step key, then re-generate placeholders. This is exactly the same pattern in your flows.

3.9. jump_to_step()

• If you want advanced partial reverts, define a jump_to_step route that:
  1. Reads step from the form.
  2. Clears subsequent steps from the pipeline state.
  3. Regenerates placeholders.

3.10. Additional Helpers

• _final_card() or _fmt() can unify UI for locked states or user text. Not strictly required but recommended for DRY code.
• If you need LLM commentary, do so in e.g. await self.pipulate.explain(self, "step_01", "As the caretaker, greet the user...") at key points.

Final Summary

In short:

• You ended with a OneCardFlow that is structurally close to BridgeFlow/TenCardFlow, retaining partial revert, finalization, and the “redraw placeholders on finalize” pattern.
• The biggest differences from the earlier snippet revolve around advanced revert (jump_to_step), a more robust “locked final card” approach, pre-filled form data, and the _fmt(...) helper for text rendering.
• This final approach is more thoroughly integrated with your multi-step pipeline tradition, ensuring the same user experience (revert controls, final lock/unlock) that people see in bigger flows like TenCardFlow or LinkGraphFlow.

Hence, the main lesson is: Even a single-step flow can benefit from advanced pipeline patterns—finalization, partial revert, LLM commentary hooks, etc. Always unify on the standard pattern to keep your code symmetrical across all workflows.

Pipulate Refinement Planning

We move the common, repeating patterns into Pipulate that should not be part of the pipeline workflow code. Externalizing common functions makes what remains shorter and more expressive. So I do this as a reminder for a next round of work.

Me: What clearly belongs in Pipulate as helper functions?

o1 Pro: Below is a succinct list of which pieces of logic clearly belong in Pipulate (i.e., the pipeline manager) as helper functions, because they’re generally needed by any pipeline workflow and are not specific to an individual flow like BridgeFlow or TenCardFlow or OneCardFlow.

1. Core Pipeline/Workflow Operations

1. initialize_if_missing(pipeline_id, initial_data)
   • Already lives in Pipulate.
   • It’s universal to any pipeline that needs to spin up or rehydrate a JSON blob for a given ID.
2. generate_step_placeholders(steps, prefix, start_from=0)
   • Also already in Pipulate.
   • Every flow eventually calls this to produce the auto-loading Div placeholders (e.g., Div(id="step_01", hx_get=...)).
3. get_step_data(pipeline_id, step_id, default=None) / set_step_data(...)
   • These are the standard read/write of the pipeline JSON blob.
   • Absolutely universal—no flow can function without them.
4. clear_steps_from(pipeline_id, start_step, steps_list)
   • The function that removes subsequent steps from the JSON state, if you want partial reverts or jump_to_step logic.
   • Also already in Pipulate.

2. Step-by-Step Infrastructure Helpers

1. is_finalized(pipeline_id, final_step_id)
   • For quick “Is the workflow locked at the final step yet?” checks.
   • Some flows have this check inline, but it’s a universal concern.
   • If you frequently do `if “finalized” in pipulate.get_step_data(…)”, that’s effectively the same pattern. A single helper like:

     def is_finalized(self, url, final_step):
         data = self.get_step_data(url, final_step, {})
         return "finalized" in data
     

   • Helps keep your code more readable.
2. revert_control(step_id, prefix, url=None, final_step=None, message=None, target_id="pipeline-container", label="Revert", style=None)
   • Many of your flows define a variation of this. You pass a message, it returns a small Card or Div with a revert button.
   • Because every flow has a near-identical snippet, it’s perfect for a Pipulate-level “build a revert form” helper. You already have something similar: pipulate.revert_control or revert_control_styled.
3. jump_to_step_logic(form, pipeline_id, steps_list)
   • The routine that finds the index of the step you want to revert to, then clears subsequent steps.
   • Often repeated in each flow.
   • You already have a partial version in each jump_to_step method:

     # Not universal at the moment, but it could be:
     def jump_to_step_logic(self, pipeline_id, step_id, steps):
         # find step_index, clear subsequent, etc.
     

   • This can reduce each flow’s jump_to_step route to a short function that calls pipulate.jump_to_step_logic(...) and returns placeholders.

3. (Optional) UI/Formatting Helpers

These might be seen as more flow-specific, but if you find yourself duplicating them across multiple flows, consider moving them into Pipulate:

1. _final_card(...)
   • If you keep re-implementing the same “locked” final card structure across flows, you could extract a minimal helper in Pipulate.
   • Example:

     def build_locked_card(title="Pipeline Complete", unfinalize_url="", style="color: green;"):
         return Card(
             H3(title),
             Form(
                 Button("Unfinalize", type="submit", style="background-color: #f66;"),
                 hx_post=unfinalize_url,
                 hx_target="#pipeline-container",
                 hx_swap="outerHTML"
             ),
             style=style
         )
     

   • Not strictly universal—some flows want specialized final text. But if you keep repeating the same pattern, a standard “locked card” builder is helpful.
2. _fmt(text) for multiline user text
   • In OneCardFlow, you used _fmt() to wrap user text in a <pre> with certain styling.
   • If multiple flows do the same “display multiline user text” pattern, you could centralize it in Pipulate (e.g. pipulate.pretty_text_output(...)).
   • However, if you only do it in OneCardFlow, it might remain local.
3. Minimal LLM “explain”
   • You often have a snippet like:

     async def explain(self, caller, current_step, message=None):
         # create_task(chatq(...)) if caller.llm_enabled
     

   • Some flows (BridgeFlow, Poetflow) use it, some do not.
   • Putting a generic version in Pipulate is consistent with how you’ve done “explain” in earlier code. That’s already in your pipulate.explain(...), presumably.

4. Summary of “What Belongs in Pipulate”

In short:

1. All fundamental pipeline orchestration:
   • initialize_if_missing(...), get_step_data(...), set_step_data(...), clear_steps_from(...), generate_step_placeholders(...)
2. Shared logic that every pipeline flow methodically duplicates:
   • is_finalized(...) checks,
   • revert_control(...) (or revert_control_styled(...)),
   • jump_to_step_logic(...) to handle partial reverts.
3. Potentially universal UI-building helpers (e.g. locked final card, multiline text display) if you keep rewriting them in each flow.

Thus, anything that’s not unique to a given flow’s identity or theming (like the Bridge of Death persona, or specialized “explain” text) but is repeated for all flows is a prime candidate for living in Pipulate as a “helper function.”

Lock It In With A Plan!

Me: I agree! Here’s the entire codebase again. Make me a meticulous, pedantic point by point plan about how to externalize what from which workflow into Pipulate.

o1 Pro: Below is a meticulous, pedantic point-by-point plan describing how to move various pieces of logic from the individual workflow classes (BridgeFlow, TenCardFlow, OneCardFlow, LinkGraphFlow, etc.) into the Pipulate class (or a small helper module used by Pipulate). The overall goal is to reduce code duplication and centralize anything that multiple flows share.

1. Identify Repeated Pipeline Workflow Patterns

A. “Step Jumping” (Revert/Unfinalize) Logic

• Where it lives now:
  • BridgeFlow.jump_to_step
  • TenCardFlow.jump_to_step
  • LinkGraphFlow.jump_to_step
  • OneCardFlow.jump_to_step
• What’s repeated:
  1. Reading step_id from the form.
  2. Looking up pipeline_id from db["pipeline_id"].
  3. Clearing the pipeline state from step_id onward:

     self.pipulate.clear_steps_from(pipeline_id, step_id, self.STEPS)
     

  4. Potentially removing finalized flags.
  5. Regenerating placeholders:

     placeholders = self.pipulate.generate_step_placeholders(self.STEPS, self.prefix, start_from=0)
     return Div(*placeholders, id="XYZ-container")
     

Plan:

1. Create a method inside Pipulate—for example:

   def jump_to_step_logic(self, pipeline_id: str, step_id: str, steps, remove_finalization_step: str = None):
       # 1) Clear steps from step_id onward
       self.clear_steps_from(pipeline_id, step_id, steps)
   
       # 2) Potentially remove the "finalized" flag
       if remove_finalization_step:
           state = self.get_state(pipeline_id)
           if remove_finalization_step in state and "finalized" in state[remove_finalization_step]:
               del state[remove_finalization_step]["finalized"]
               self._save_state(pipeline_id, state)
   
       # 3) Return the new placeholders
       # The flow code itself can choose how to handle the placeholders,
       # or we can return them here for convenience
       return self.generate_step_placeholders(steps, prefix, start_from=0)
   

2. In each flow’s jump_to_step, replace the repeated logic with a call to this helper.

Why: This moves the heavy-lifting logic that’s basically identical from each flow into a single place in Pipulate.

B. “Unfinalize” Logic

• Where it lives now:
  • BridgeFlow.unfinalize
  • TenCardFlow.unfinalize
  • LinkGraphFlow.unfinalize
  • OneCardFlow.unfinalize
• What’s repeated:
  1. Checking pipeline_id in the DB.
  2. Locating the step (like step_04 or step_05 or step_11) that has "finalized".
  3. Removing "finalized" from that step’s data.
  4. Re-generating placeholders from step_01 onward.

Plan:

1. Add a pipulate.unfinalize_logic(url, final_step, steps, prefix) method:

   def unfinalize_logic(self, url: str, final_step: str, steps, prefix: str, container_id: str = "bridge-container"):
       if not url:
           return P("No pipeline found", style="color:red;")
       state = self.get_state(url)
       if final_step in state and "finalized" in state[final_step]:
           del state[final_step]["finalized"]
           self._save_state(url, state)
       # Re-generate placeholders
       placeholders = self.generate_step_placeholders(steps, prefix, start_from=0)
       return Div(*placeholders, id=container_id)
   

2. In each flow’s unfinalize method, call:

   return self.pipulate.unfinalize_logic(
       url=pipeline_id,
       final_step="step_04",  # or step_05, step_11, etc. per flow
       steps=self.STEPS,
       prefix=self.prefix,
       container_id="bridge-container"  # or "tenflow-container", etc.
   )
   

3. Optionally pass the container ID so each flow can produce the correct DOM ID.

Why: Eliminates a chunk of repeated code across flows.

C. “Finalize” Checks

• Where it lives now:
  • e.g. BridgeFlow.step_04 or TenCardFlow.step_11 or OneCardFlow.step_02
• What’s repeated:
  1. Checking if "finalized" in stepX_data.
  2. Possibly skipping step display if finalized.

We already have a partial helper, pipulate.is_finalized(...). Use that consistently:

1. Replace manual if "finalized" in step_XX_data: checks with

   if self.pipulate.is_finalized(pipeline_id, "step_XX"):
       ...
   

2. If you want a simpler signature, pass final_step_id, e.g. "step_11" or "step_05".
   • Already done in some flows.

D. Revert Control (the little “↶ Step X” button)

• Where it lives now:
  • self.pipulate.revert_control(...) is already a common method in Pipulate.
  • Good job centralizing it!

What’s left: Some flows pass consistent arguments for style, etc. If multiple flows do the same styling or label pattern, consider making a single param for that. Otherwise, you’re good.

2. Summaries, Step Summaries & Explanations

• Where:
  • BridgeFlow.step_03 calls await self.pipulate.explain(...)
  • OneCardFlow does not do LLM commentary, but might later
  • TenCardFlow does no LLM commentary

If multiple flows adopt minimal commentary, pipulate.explain(...) is already a solid helper. It’s centralized. Good job. No additional steps needed.

3. Step Registration & Route Loop

In each flow, you do:

routes = [
  (f"{prefix}/init", self.init, ["POST"]),
  ...
]
for _, step_id, _ in self.STEPS:
    routes.extend([
        (f"{prefix}/{step_id}",        getattr(self, step_id)),
        (f"{prefix}/{step_id}_submit", getattr(self, f"{step_id}_submit"), ["POST"])
    ])

That’s fairly standard. If you wanted to unify or DRY it:

1. Create a utility:

   def register_workflow_steps(self, app, prefix, steps, obj):
       # app => e.g. self.app
       # prefix => e.g. /bridge
       # steps => e.g. self.STEPS
       # obj => e.g. self (the Flow instance)
       routes = [...]
       for _, step_id, _ in steps:
           ...
       for path, handler, *methods in routes:
           ...
   

2. Then in each flow’s __init__, do:

   self.pipulate.register_workflow_steps(self.app, self.prefix, self.STEPS, self)
   

But that might be overkill. The code is quite short as-is.

4. “Initialization” Logic

Each flow has:

async def init(self, request):
    form = await request.form()
    pipeline_id = form.get("pipeline_id", "untitled")
    db["pipeline_id"] = pipeline_id
    self.pipulate.initialize_if_missing(pipeline_id, {"endpoint": self.prefix})
    placeholders = self.pipulate.generate_step_placeholders(self.STEPS, self.prefix, start_from=0)
    return Div(*placeholders, id="onecard-container")

Plan:

1. If you want it consistent, a helper like pipulate.workflow_init(...) could do exactly that.
2. Then each flow’s init is just a small wrapper:

   async def init(self, request):
       pipeline_id = await self.pipulate.workflow_init(request, prefix=self.prefix, steps=self.STEPS, container_id="onecard-container")
       return pipeline_id  # or return the placeholders Div
   

   • But you still need to return the actual placeholders.
   • So the helper might do everything and return the Div.
   • This is purely optional.

5. Summarize the “What to Externalize” Steps

Below is a checklist you can follow to methodically move repeated code from each workflow into Pipulate:

1. jump_to_step methods
   • Add a universal pipulate.jump_to_step_logic(...).
   • Inside each flow’s jump_to_step, call it.
2. unfinalize methods
   • Add pipulate.unfinalize_logic(...).
   • Inside each flow’s unfinalize, call it.
3. Finalize checks
   • Already partly centralized with pipulate.is_finalized(...).
   • Make sure each flow consistently uses it.
4. Initialization (init) pattern
   • Optionally create pipulate.workflow_init(...).
5. LLM commentary:
   • Already a single function pipulate.explain(...).
   • No extra changes needed, unless you want advanced usage.

6. Example Code Snippets

A. Jump to Step Logic in Pipulate

def jump_to_step_logic(self, pipeline_id: str, step_id: str, steps, prefix: str, final_step: str = None, container_id: str = "bridge-container") -> Div:
    # 1) Clear steps from step_id onward
    self.clear_steps_from(pipeline_id, step_id, steps)

    # 2) Optionally remove 'finalized'
    if final_step:
        state = self.get_state(pipeline_id)
        if final_step in state and "finalized" in state[final_step]:
            del state[final_step]["finalized"]
            self._save_state(pipeline_id, state)

    # 3) Generate placeholders from start
    placeholders = self.generate_step_placeholders(steps, prefix, start_from=0)
    return Div(*placeholders, id=container_id)

Then BridgeFlow’s jump_to_step becomes:

async def jump_to_step(self, request):
    form = await request.form()
    step_id = form.get("step")
    pipeline_id = db.get("pipeline_id", "unknown")

    # Call the universal helper
    placeholders = self.pipulate.jump_to_step_logic(
        pipeline_id=pipeline_id,
        step_id=step_id,
        steps=self.STEPS,
        prefix=self.prefix,
        final_step="step_04",         # or "step_05" if your final step is step_05
        container_id="bridge-container"
    )
    return placeholders

B. Unfinalize Logic

def unfinalize_logic(self, pipeline_id: str, final_step: str, steps, prefix: str, container_id: str) -> Div:
    if not pipeline_id:
        return P("No pipeline found", style="color:red;")
    state = self.get_state(pipeline_id)
    if final_step in state and "finalized" in state[final_step]:
        del state[final_step]["finalized"]
        self._save_state(pipeline_id, state)

    # Regenerate placeholders
    placeholders = self.generate_step_placeholders(steps, prefix, start_from=0)
    return Div(*placeholders, id=container_id)

Then in BridgeFlow:

async def unfinalize(self, request):
    pipeline_id = db.get("pipeline_id", "unknown")
    return self.pipulate.unfinalize_logic(
        pipeline_id, 
        "step_04",        # final step
        self.STEPS, 
        self.prefix, 
        container_id="bridge-container"
    )

Likewise for TenCardFlow, LinkGraphFlow, OneCardFlow, etc. Just pass the correct final step ID and container ID.

7. Optional Additions

• Integrate new code into pipulate.py or keep it inline in the Pipulate class.
• Possibly add a PipulateWorkflowMixin if you prefer class-based approach. Then each flow could inherit from it.
• But the simplest is adding plain methods on Pipulate.

8. Final Step-by-Step Migration Outline

1. Create in Pipulate:
   • jump_to_step_logic(...)
   • unfinalize_logic(...)
   • (optional) workflow_init(...)
2. In each Flow:
   • Replace the entire jump_to_step method body with a call to pipulate.jump_to_step_logic(...).
   • Replace the entire unfinalize method body with a call to pipulate.unfinalize_logic(...).
   • If you want to unify init, do so similarly.
3. Check that each flow’s final step ID is correct. For BridgeFlow it’s step_04, for LinkGraphFlow it’s step_05, for TenCardFlow it’s step_11, etc.
4. Test each workflow to ensure the revert/unfinalize still works as expected.
5. Optionally ensure all if "finalized" in stepX_data: checks are replaced with if self.pipulate.is_finalized(pipeline_id, "step_XY"): calls.
6. Done. Code duplication is reduced. Maintenance is easier.

Conclusion

By following the above plan, you:

• Centralize repeated step-jumping/unfinalization logic.
• Preserve each flow’s unique step structure while removing “boilerplate” code.
• Keep LLM commentary optional but available through pipulate.explain(...).
• Achieve a clean “Pipulate + small Flow Classes” pattern that future flows can copy with minimal overhead.

Burned the midnight oil. Apropos. Happy last night of Hanukkah you know who.