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/ssl/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/ssl)<a href="https://agentmods.dev/skills/github/gh-aw/ssl"><img src="https://agentmods.dev/badge/skills/github/gh-aw/ssl.svg" alt="Measured on agentmods" 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 | $0.00030 | $0.02251 |
| Opus 5 | $0.00015 | $0.01125 |
| Sonnet 5 | $0.00006 | $0.00450 |
| Haiku 4.5 | $0.00003 | $0.00225 |
Grade A, and why
ssl-skill-normalizer 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 3d 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 — 289 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SSL Skill Normalizer
Purpose
This skill converts markdown-based skill artifacts into a structured Scheduling-Structural-Logical (SSL) representation as introduced in:
Liang et al., "From Skill Text to Skill Structure: The Scheduling-Structural-Logical Representation for Agent Skills", arXiv:2604.24026 (2026).
SSL addresses the core limitation of free-form skill text: it is human-readable but hard for agents to reason over, discover, and audit. By mapping each skill into three complementary layers, SSL makes skills searchable (improved MRR 0.573 → 0.707 in the paper) and risk-assessable (improved macro F1 0.744 → 0.787).
The Three SSL Layers
The representation is grounded in Schank & Abelson's theories of Memory Organization Packets (MOPs), Script Theory, and Conceptual Dependency. Each layer captures a different dimension of skill knowledge:
Layer 1 — Scheduling (When / Who)
Answers: When should this skill be invoked? By whom, given which inputs and outputs?
Fields extracted:
id— stable lowercase identifiername— human-readable skill namegoal— one-sentence purposeintent_signature— typed function signature (fn($input) -> $output)inputs—$-prefixed named input bindingsoutputs—$-prefixed named output bindingsdependencies— explicit runtime tool or library requirementscontrol_flow_features— e.g.sequential,conditional,loopentry_scene— ID of the first scene to executesubscene_refs— IDs of any nested/delegated scenes
Layer 2 — Structural (How / Order)
Answers: What are the macro-level execution stages and how do they connect?
Each scene is a named execution stage with:
id— unique within the skilltype— one of the restricted scene-type enum (see below)goal— what the scene accomplishesentry_condition— precondition for entering the sceneexit_condition— postcondition that must hold on exitnext_scene_rules— conditional transitions to the next scene ID,END_SUCCESS, orEND_FAILinputs/outputs—$-prefixed bindings consumed and producedentry_logic_step— ID of the first logic step in this scene
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 3d ago First seen · 289 lines · 30 tokens per session scan A dffc2c929e02
ssl-skill-normalizer is a skill published in the GitHub repository github/gh-aw (5,109 stars, last pushed today), licensed MIT. It adds 30 tokens to every session and 2,251 once invoked, about $0.0002 per session on Opus 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 2026-09-03.
Other skills, from other repositories
c-github
Interact with GitHub using the gh CLI and jq. Manage PRs, issues, repositories, and Actions workflows. Make raw API calls with gh api for anything not covered by built-in commands.
watch-pr
Watch a GitHub pull request for CI status, reviews, comments, merge conflicts, and terminal states using the gh-watch extension. Use when the user wants to monitor a PR, wait for CI, or track PR progress.
watch-tag
Watch a GitHub repository for new tags using the gh-watch extension. Use when the user wants to be notified when a tag is created, when a release is cut, or when a tag that includes a specific commit appears (e.g. "tell me when my merge ships in a release").
watch-branch
Watch a GitHub branch for new commits using the gh-watch extension. Use when the user wants to be notified when new commits are pushed to a branch, monitor main for merges, or track branch activity.
watch-commit
Watch a GitHub commit for CI status changes using the gh-watch extension. Use when the user wants to monitor a commit's CI checks, wait for a build to finish, or track CI progress on a specific SHA.
update-architecture-docs
Generate or update the architecture documentation in docs/content/architecture/. Use on "update architecture docs", "generate architecture documentation", "regenerate architecture docs", or after any structural change to the codebase.