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.
Borrowing it
Nothing to install: this file belongs to github/gh-aw. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/github/gh-aw/main/.github/skills/developer-code-organization/SKILL.mdgit 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/skills/github/gh-aw/developer-code-organization)<a href="https://agentmods.dev/skills/github/gh-aw/developer-code-organization"><img src="https://agentmods.dev/badge/skills/github/gh-aw/developer-code-organization/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/skills/github/gh-aw/developer-code-organization"><img src="https://agentmods.dev/badge/skills/github/gh-aw/developer-code-organization.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Tool Misuse · line 455 Tool defaults are unsafe or overly permissive (e.g. disabled TLS verification, no authentication, world-writable permissions). Unsafe defaults widen the attack surface.Fix: Override unsafe defaults with secure settings (verify=True, auth required, restrictive permissions). Review and harden all tool configurations.
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 | $0.00028 | $0.04325 |
| Opus 5.5 | $0.00011 | $0.01730 |
| Sonnet 5.5 | $0.00006 | $0.00865 |
| Haiku 4.5 | $0.00003 | $0.00432 |
Grade A, and why
developer-code-organization 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 1mo 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 — 486 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Organization
Use this reference for gh-aw code organization patterns, file structure guidelines, WASM build-variant stubs, and string sanitization/normalization conventions.
Table of Contents
- File Organization Principles
- Excellent Patterns to Follow
- File Size Guidelines
- Decision Trees
- Case Study: Refactoring Large Files
- Anti-Patterns to Avoid
- Helper File Conventions
- String Sanitization vs Normalization
- WASM Build-Variant Pattern
File Organization Principles
The codebase follows clear patterns for organizing code by functionality rather than type. This section provides guidance on maintaining code quality and structure.
Prefer Many Small Files Over Large Ones
Organize code into focused files of 100-500 lines rather than creating large monolithic files.
Example:
create_issue.go (160 lines)
create_pull_request.go (238 lines)
create_discussion.go (118 lines)
Group by Functionality, Not by Type
Recommended approach:
create_issue.go # Issue creation logic
create_issue_test.go # Issue creation tests
add_comment.go # Comment addition logic
add_comment_test.go # Comment tests
Avoid:
models.go # All structs
logic.go # All business logic
tests.go # All tests
Excellent Patterns to Follow
Create Functions Pattern
One file per GitHub entity creation operation:
create_issue.go- GitHub issue creation logiccreate_pull_request.go- Pull request creation logiccreate_discussion.go- Discussion creation logiccreate_code_scanning_alert.go- Code scanning alert creation
Benefits:
- Clear separation of concerns
- Easy to locate specific functionality
- Prevents files from becoming too large
- Facilitates parallel development
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.
- 1mo ago First seen · 486 lines · 28 tokens per session scan A d8a8661b21ba
developer-code-organization is a skill published in the GitHub repository github/gh-aw (5,406 stars, last pushed today), licensed MIT. It adds 28 tokens to every session and 4,325 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.
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