How do I install the Afml Docs Loop skill?

Run npx skillmds@latest add tomevault-io/afml-docs-loop in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Afml Docs Loop safe to use?

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Which AI agents work with Afml Docs Loop?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Afml Docs Loop free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Afml Docs Loop?

tomevault-io (@tomevault-io) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Afml Docs Loop by tomevault-io — Agent Skill

Name: Afml Docs Loop
Author: tomevault-io

Afml Docs Loop

Build and run an AFML-grounded, chapter-by-chapter documentation loop for OpenQuant docs. Use this when you need consistent module docs, hierarchical chapter coverage, and iterative progress tracking with MCP-backed prompts. Use when this capability is needed.

tomevault-io Updated Sep 17, 2026

File contents

AFML Docs Loop

Use this skill when the goal is to produce complete, consistent OpenQuant docs aligned to AFML structure.

What this skill does

Builds a hierarchical docs backlog (chapter -> module section) from AFML + current module docs.
Generates per-section writing prompts grounded in AFML MCP retrieval anchors.
Runs a loop: claim next section, write docs, review UI, mark done.

Required script

Main orchestrator: scripts/afml_docs_loop.py
Wrapper: scripts/run_afml_docs_loop.sh

Quick start

# 1) initialize plan + prompt files
skills/afml-docs-loop/scripts/run_afml_docs_loop.sh init

# 2) inspect progress
skills/afml-docs-loop/scripts/run_afml_docs_loop.sh status

# 3) run MCP evidence loop across sections (writes evidence.json/evidence.md)
skills/afml-docs-loop/scripts/run_afml_docs_loop.sh evidence

# 4) claim next section and print its prompt
skills/afml-docs-loop/scripts/run_afml_docs_loop.sh next --print-prompt

# 5) mark section complete
skills/afml-docs-loop/scripts/run_afml_docs_loop.sh done <section_id> --note "what was updated"

# 6) export loop status into Astro data
skills/afml-docs-loop/scripts/run_afml_docs_loop.sh export

Iterative workflow (mandatory)

For each section:

Run evidence to refresh AFML retrieval artifacts for all targeted sections.
Claim next section with next --print-prompt.
Use AFML MCP tools (afml_search, afml_get_context) to gather chapter/module grounding.
Update module docs in docs-site/src/data/moduleDocs.ts.
Validate rendering in Astro and apply the UI rubric in references/ui-ux-rubric.md.
Mark section complete with done.
Repeat until status shows no pending sections.

File targets

Module content source: docs-site/src/data/moduleDocs.ts
Module renderer: docs-site/src/pages/module/[slug].astro
Subject index: docs-site/src/pages/modules.astro
State file: docs/afml-docs-loop/state.json
MCP evidence: docs/afml-docs-loop/evidence.json, docs/afml-docs-loop/evidence.md
Generated prompts: docs/afml-docs-loop/prompts/*.md

Quality bar

Use references/doc-writing-template.md for consistency.
Use references/ui-ux-rubric.md to avoid generic/AI-slop design.
Keep examples executable and API-accurate for the current crate.

Converted and distributed by TomeVault — claim your Tome and manage your conversions.

---name: afml-docs-loopdescription: Build and run an AFML-grounded, chapter-by-chapter documentation loop for OpenQuant docs. Use this when you need consistent module docs, hierarchical chapter coverage, and iterative progress tracking with MCP-backed prompts. Use when this capability is needed.---# AFML Docs LoopUse this skill when the goal is to produce complete, consistent OpenQuant docs aligned to AFML structure.## What this skill does1. Builds a hierarchical docs backlog (`chapter -> module section`) from AFML + current module docs.2. Generates per-section writing prompts grounded in AFML MCP retrieval anchors.3. Runs a loop: claim next section, write docs, review UI, mark done.## Required script- Main orchestrator: `scripts/afml_docs_loop.py`- Wrapper: `scripts/run_afml_docs_loop.sh`## Quick start```bash# 1) initialize plan + prompt filesskills/afml-docs-loop/scripts/run_afml_docs_loop.sh init# 2) inspect progressskills/afml-docs-loop/scripts/run_afml_docs_loop.sh status# 3) run MCP evidence loop across sections (writes evidence.json/evidence.md)skills/afml-docs-loop/scripts/run_afml_docs_loop.sh evidence# 4) claim next section and print its promptskills/afml-docs-loop/scripts/run_afml_docs_loop.sh next --print-prompt# 5) mark section completeskills/afml-docs-loop/scripts/run_afml_docs_loop.sh done <section_id> --note "what was updated"# 6) export loop status into Astro dataskills/afml-docs-loop/scripts/run_afml_docs_loop.sh export```## Iterative workflow (mandatory)For each section:1. Run `evidence` to refresh AFML retrieval artifacts for all targeted sections.2. Claim next section with `next --print-prompt`.3. Use AFML MCP tools (`afml_search`, `afml_get_context`) to gather chapter/module grounding.4. Update module docs in `docs-site/src/data/moduleDocs.ts`.5. Validate rendering in Astro and apply the UI rubric in `references/ui-ux-rubric.md`.6. Mark section complete with `done`.7. Repeat until `status` shows no pending sections.## File targets- Module content source: `docs-site/src/data/moduleDocs.ts`- Module renderer: `docs-site/src/pages/module/[slug].astro`- Subject index: `docs-site/src/pages/modules.astro`- State file: `docs/afml-docs-loop/state.json`- MCP evidence: `docs/afml-docs-loop/evidence.json`, `docs/afml-docs-loop/evidence.md`- Generated prompts: `docs/afml-docs-loop/prompts/*.md`## Quality bar- Use `references/doc-writing-template.md` for consistency.- Use `references/ui-ux-rubric.md` to avoid generic/AI-slop design.- Keep examples executable and API-accurate for the current crate.---> Converted and distributed by [TomeVault](https://tomevault.io/claim/open-quant) — claim your Tome and manage your conversions.

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