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Feature Doc

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huytieu
feature-doc

Closed-loop feature documentation for your product. Give it a feature (issue numbers or an id) and it reads the source, drives your live app to capture real screenshots and a screen recording, writes a docs article (Docusaurus or any markdown docs site) with both how-to steps and use-case content, then verifies the article with a fresh-context read-only verifier before reporting. Use when you say "write the doc for <feature>", "document issue #123", or "/feature-doc".

Overview

Publisherhuytieu
RepositoryCOG-second-brain
Skill namefeature-doc
Stars
1.3K
Forks
162
Bundled files
4
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by huytieu on GitHub. Read the source before you install it.

Installation

Install the Feature Doc AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/huytieu/COG-second-brain/tree/main/.claude/skills/feature-doc
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/huytieu/COG-second-brain.git /tmp/COG-second-brain
mkdir -p .claude/skills
cp -r /tmp/COG-second-brain/.claude/skills/feature-doc .claude/skills/feature-doc
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Feature Doc in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Feature Doc on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Feature Doc is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

feature-doc: closed-loop feature documentation

Produces one evidence-backed feature article end to end. Capture needs no manual gesture: a bundled recorder grabs frames from the browser through the DevTools protocol. Publishing the docs PR stays a gate for you at CP-6.

Configuration

Set these once (a .env, your kit.toml, or pass them as arguments). Nothing below is hardcoded.

SettingMeaningExample
APP_URLStart page of the live app to drivehttps://app.example.com/projects/1
ISSUE_REPOIssue tracker holding the feature issuesyour-org/your-product
DOCS_REPOLocal path of the docs repo../your-docs
DOCS_DIRFolder inside the docs repo for the articledocs/features/
EVIDENCE_DIRWhere screenshots and video land./evidence/<feature>/

Keep the docs repo in a folder your tools can always read. Some operating systems block background reads of the Documents folder mid-session, which breaks the verifier.

When to use

  • A shipped or shipping feature has no article, or an outdated one.
  • You want a doc that works as a user manual and as a guide to what you can do with the feature, with real screenshots and a video.

Skip it for backend features with no user surface (write a short "no doc required" record instead).

Closed loop

CP-1 spec (issues/repo)  -> CP-2 plan (AC-n)  -> CP-3 build (capture + write)
   -> CP-3v component verify (task-verifier, read-only)  -> fix-agent (max 2)
   -> CP-5 post-condition  -> CP-6 ship gate (you review, then open the docs PR)

The writer never grades its own work. The verifier is a fresh-context, read-only subagent that gets paths and AC-n only, never the writer's narration. See the closed-loop skill for the checkpoint mechanics.

Inputs

  • Feature id plus seed issue numbers in ISSUE_REPO, or a spec path.
  • A browser already logged in to the app, reachable by a browser automation tool (Playwright, CDP, or your browser harness). If the app redirects to a login page, stop and ask the user to sign in. Never type credentials.
  • The docs repo at DOCS_REPO. One article per feature: a new feature gets a new article, a changed one updates the existing article.

Pipeline

1. Enrich (CP-1)

Read each seed issue (gh issue view <n> --repo $ISSUE_REPO --json title,body,state,labels,milestone, or your tracker's CLI) and any matching spec files. Establish the concrete user surface, the ship status per issue (an OPEN issue is not a shipped surface, so do not claim it), and the exact flow to demo. Never invent UI. Where a detail is unknown, mark it TODO and flag it.

2. Plan (CP-2)

Map the article's sections to AC-n (see Post-conditions). Grep the docs repo for existing coverage to decide between a new article and an update.

3. Capture (CP-3)

Drive the live flow and save real evidence into EVIDENCE_DIR.

  • Screenshots of each meaningful state, using your browser tool's screenshot call.

  • A real video of the end-to-end flow with the bundled recorder (no gesture, no ffmpeg needed for the capture):

    bash
    SR_OUT=$EVIDENCE_DIR/<feature>-demo \
    SR_URL=$APP_URL \
    SR_FPS=8 \
    SR_STEPS='[{"at":0.5,"do":"click_testid","testid":"..."},{"at":2.0,"do":"click_text","text":"..."}]' \
    browser-harness < .claude/skills/feature-doc/tools/screenrec.py

    screenrec.py runs inside a CDP-based Python harness that exposes the page (browser-harness is one such tool; adapt the launcher to Playwright or your own). It writes frames, <out>.manifest.json (fps, viewport, per-action events with cursor coordinates and captions) and a quick <out>.raw.mp4 preview. Step actions are click_xy|click_testid|click_text|type|key|scroll, each with at (seconds) and an optional caption.

    The deliverable is a clean, complete recording of the real workflow: set up, configure, use, get the result. Finish it with tools/recompose.py, which places the real viewport in a simple browser window on a gradient background and speeds up long waits instead of cutting them. No captions, no intro or outro cards, no synthetic cursor.

    bash
    RC_MANIFEST=<base>.manifest.json RC_OUT=<feature>-walkthrough.mp4 \
    RC_SEGMENTS='[[0,9,1],[9,58,8],[58,70,1]]'  # [from_s,to_s,speed]; ramp the waits \
    RC_ADDR="app.example.com" python3 .claude/skills/feature-doc/tools/recompose.py

    recompose.py needs ffmpeg and Pillow. Embed the result with a plain video tag: <video controls preload="metadata" style="width:100%;border-radius:8px;"><source src="..." type="video/mp4" /></video>.

    • Length follows the task: long enough to teach setup and usage.
    • The usage payoff must work on camera. If the payoff step is broken, do not record a stub. Use screenshots for the working part and hold the video until the full flow works.
    • A clip of a few seconds is worse than a screenshot. Use a screenshot instead.
    • tools/videokit.py makes a branded, captioned teaser with intro and outro cards. Use it only for marketing teasers, never for walkthroughs.
  • Optional polish: an interactive demo (Arcade or similar). Browsers require a trusted user gesture to start such recorders, so tools/arcade-record.py drives the flow while you press the start and stop shortcut yourself.

  • Look at every capture. Open the images and the video, name any UI discrepancy, fix the flow, and re-capture. Never pass on "the element exists".

  • If a flow is gated (a broken backend path, an unshipped surface), capture what works, file the blocker in your tracker, and mark the rest pending. Do not fake a working demo.

  • Mask any customer data or personal data visible in a capture before it leaves your machine.

4. Write (CP-3)

Write the article as markdown (MDX-safe when the docs site is Docusaurus) with both layers:

  • How-to: overview, why it matters, step-by-step usage.
  • Use-case layer: at least 3 use cases with sample workflows (each a concrete prompt or step list plus the expected result), prompting techniques where the feature takes input, best practices, limitations.
  • Embed the captured media (![alt](path) for images, a video tag for the recording). Cross-link to related articles.
  • Run the draft through the no-ai-slop skill before verification.

5. Verify (CP-3v then CP-5)

Spawn a task-verifier with only the article path, the evidence directory and the AC-n list. It re-opens the files and media itself and returns one row per AC: EVIDENCE AC-n | CP-3v | PASS|FAIL | <one line + path/observation>. On FAIL:fixable, spawn fix-agent (max 2 attempts) with just the failed AC-n, then re-verify. After 2 attempts, escalate with the evidence.

6. Report and ship gate (CP-6)

Summarize the article path, the media (with accessible URLs once uploaded), the AC-n PASS table, and open TODOs or gated items. Opening the docs PR waits for the user.

Post-conditions

  • AC-1 The article exists at the planned path under DOCS_DIR with valid frontmatter for your docs site.
  • AC-2 It contains how-to steps and the use-case layer (at least 3 use cases, prompting where relevant, best practices).
  • AC-3 Every UI or behavior claim is grounded in a captured screenshot or an issue body. Unknowns are TODO, not invented. Surfaces from OPEN issues are marked gated, not shipped.
  • AC-4 Either (a) a clean, complete walkthrough recording exists (real setup, usage and result, no decoration, valid mp4 by ffprobe) and the article embeds it, or (b) screenshots cover the working parts and the article notes the video is pending. A short decorated clip does not satisfy this.
  • AC-5 Evidence that ships is accessible (uploaded to the PR or issue, not a local path).
  • AC-6 A limitations section states any gated, blocked or unshipped parts.

Rules

  • No fabrication. Follow your house writing rules (no em dashes, TL;DR first, scannable).
  • Visual verification is mandatory for UI docs: read the pixels, fix what is wrong, re-capture.
  • Use relative paths or config values in all output, never machine-specific absolute paths.

Escape hatches

  • Auth wall: stop and ask the user to sign in. Never enter credentials.
  • CDP will not connect: point your harness at the browser's remote-debugging URL (for example http://localhost:9222) and reload it. If the browser prompts for permission, the user clicks Allow.
  • Flow gated by a bug: file it, capture the working portion, mark the rest pending.
  • ffmpeg absent: the recorder falls back to a GIF preview. Install ffmpeg for the mp4.

Tools

  • tools/screenrec.py: flow recorder (CDP frame-grab loop to frames, manifest and raw preview).
  • tools/recompose.py: walkthrough finisher. Manifest to a clean 1920x1080 h264 mp4 with a browser window, gradient and speed ramps. Options: RC_FIT=1 fits a phone or tall capture, RC_CROP=x0,y0,x1,y1 crops the source, RC_NOBAR=1 drops the browser bar, RC_SPEED_BADGE=1 labels sped-up segments, RC_REDACT pixelates boxes such as a person's name.
  • tools/videokit.py: branded teaser cut with captions and cards (teaser only).
  • tools/arcade-record.py: hybrid interactive-demo recorder. Set APP_URL and add flows to its FLOWS table.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Feature Doc AI skill do?

Closed-loop feature documentation for your product. Give it a feature (issue numbers or an id) and it reads the source, drives your live app to capture real screenshots and a screen recording, writes a docs article (Docusaurus or any markdown docs site) with both how-to steps and use-case content, then verifies the article with a fresh-context read-only verifier before reporting. Use when you say "write the doc for <feature>", "document issue #123", or "/feature-doc".

Why use Feature Doc on TypingMind?

Because you install it once and use it with any model. Feature Doc is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Feature Doc in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/huytieu/COG-second-brain/tree/main/.claude/skills/feature-doc. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Feature Doc?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Feature Doc?

As many as you like. As long as a model supports skills, you can use Feature Doc with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Feature Doc AI skill free?

Yes. It is published on GitHub by huytieu under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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