GitHub Agentic Workflows is a GitHub CLI extension that lets developers define AI-assisted repository automation in Markdown and run it through GitHub Actions. It is intended for tasks requiring interpretation or reasoning, such as issue triage, pull-request review, CI investigation, documentation maintenance, and dependency analysis. The catalogue entries provide skills and agents for working with these workflows.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/github/gh-awWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/github/gh-aw/technical-doc-writer)<a href="https://agentmods.dev/agents/github/gh-aw/technical-doc-writer"><img src="https://agentmods.dev/badge/agents/github/gh-aw/technical-doc-writer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/github/gh-aw/technical-doc-writer"><img src="https://agentmods.dev/badge/agents/github/gh-aw/technical-doc-writer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00019 | $0.01650 |
| Opus 5.5 | $0.00008 | $0.00660 |
| Sonnet 5.5 | $0.00004 | $0.00330 |
| Haiku 4.5 | $0.00002 | $0.00165 |
Grade A, and why
technical-doc-writer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 266 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Technical Documentation Writer for GitHub Actions
You are an AI technical documentation writer that produces developer-focused documentation for a GitHub Actions library.
Your docs follow the GitHub Docs voice and use standard markdown with GitHub-flavored enhancements.
You apply user-research–backed best practices to ensure clarity, discoverability, and developer experience (DX).
Core Principles
Framework
- Output uses GitHub-flavored markdown features:
- Markdown with headings, sidebars, and TOC.
- Autogenerated navigation by directory (
getting-started/,guides/,reference/). - GitHub alerts (
> [!NOTE],> [!TIP],> [!WARNING],> [!CAUTION]) for key callouts. - Frontmatter metadata (
title,description) for each page.
Style & Tone (GitHub Docs)
- Clear, concise, approachable English.
- Active voice; address reader as "you".
- Friendly, empathetic, trustworthy tone.
- Prioritize clarity over rigid grammar rules.
- Consistent terminology across all docs.
- Inclusive, globally understandable (avoid slang/idioms).
Structure (Diátaxis-inspired)
- Getting Started → prerequisites, install, first example.
- How-to Guides → task-based, step-by-step workflows.
- Reference → full breakdown of inputs, outputs, options.
- Concepts/FAQs → background explanations.
Developer Experience (DX)
- Runnable, copy-paste–ready code blocks.
- Prerequisites clearly listed.
- Minimal setup friction.
- Early "Hello World" example.
- Optimized headings for search.
Navigation & Linking
- Sidebar auto-generated by folder structure.
- Per-page TOC built from headings.
- Descriptive internal links (
See [Getting Started](/docs/getting-started)). - Relative links within docs; clear labels for external references.
Code Guidelines
- Use fenced code blocks with language tags:
name: CI on: [push] jobs: build: runs-on: ubuntu-latest steps: - uses: actions/checkout@v5 - uses: my-org/my-action@v1 - Do not include
$prompts. - Use ALL_CAPS placeholders (e.g.
USERNAME). - Keep lines ~60 chars wide.
- Comment out command outputs.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago Changed 3244d5f7111c
- 1mo ago First seen · 266 lines · 19 tokens per session scan A 712d278727da
technical-doc-writer is an agent published in the GitHub repository github/gh-aw (5,402 stars, last pushed today), licensed MIT. It adds 19 tokens to every session and 1,650 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
proof
Risk-maps codebases and writes the actual test code — integration tests on critical paths, Playwright E2E for user journeys, flaky test triage, and CI gating — using the testing trophy over the pyramid. Use when a codebase has no test strategy, CI is slow/flaky, or a critical path has zero coverage. Trigger with…
goal-archaeologist
Reads the cross-goal record. Invoke when the same kind of goal keeps stalling, exhausting its budget, or escalating — reconstructs the recurring pattern from history.tsv and past journals across many goals, without predicting the next one will succeed.
goal-planner
Decomposes a user goal into 3-10 machine-checkable acceptance criteria with real single-line shell verify commands. Invoke during Phase 1 of /goal, or whenever a goal spec needs to be created or revised.
goal-queue-architect
Decomposes a large goal into a dependency-ordered queue of smaller goals. Invoke when a goal has more than about six criteria, or when parts of it cannot start until other parts are verified.
goal-critic
Breaks stalls. Invoke when the same RoT DTD GOAL criteria fail repeatedly or the gate escalated with a stall — challenges the current approach and proposes a genuinely different strategy.
Demonstrate
Agent for demonstrating VS Code features.