github-agentic-workflows

Guidance for creating, reviewing, installing, and troubleshooting GitHub Agentic Workflows. These are Markdown-based GitHub automations that use an agent, with safety settings and compiled workflow files.

In plain words
What is it for?
Use it to set up or edit agentic workflows, compile and run them with gh aw, review safety controls, install workflows, and debug failures.
Why use it?
It provides a consistent way to manage agent-based GitHub automation and avoid configuration or security mistakes. It also distinguishes these workflows from ordinary fixed-step GitHub Actions.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/webmaxru/is-ai-native/github-agentic-workflows
Any agent
npx skills add webmaxru/is-ai-native --skill github-agentic-workflows
Clone the repo
git clone --depth 1 https://github.com/webmaxru/is-ai-native

Made for: Claude Code, Codex.

Per session 81 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,334 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00081 $0.02334
Opus 5 $0.00041 $0.01167
Sonnet 5 $0.00016 $0.00467
Haiku 4.5 $0.00008 $0.00233

Measured 2d ago against content hash fdd02e24f189, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

github-agentic-workflows 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 2d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/find-gh-aw-targets.mjs), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

.agents/skills/github-agentic-workflows/SKILL.md · 91 lines

How it starts

The opening of the file, as written. The whole thing — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.

GitHub Agentic Workflows

Procedures

Step 1: Identify the repository state

  1. Inspect the workspace for .github/workflows/, .github/agents/, existing .lock.yml files, and any gh aw usage.
  2. Execute node skills/github-agentic-workflows/scripts/find-gh-aw-targets.mjs . when a Node runtime is available.
  3. Run gh aw version before making compiler-sensitive decisions so the workflow authoring path matches the installed CLI behavior.
  4. If the repository contains multiple candidate workflows, prefer the workflow the user named or the one closest to the active issue, pull request, or automation surface.
  5. If the repository has no GH-AW setup and the task is to create or maintain agentic workflows, read references/authoring.md before editing.
  6. If the task is limited to standard deterministic GitHub Actions YAML without agentic markdown workflows, stop and explain that this skill does not apply.

Step 2: Choose the working mode

  1. Classify the task as one of: repository setup, new workflow authoring, workflow revision, workflow installation from another repository, security review, or failure debugging.
  2. Read references/examples.md when the task needs a starting pattern for scheduled reports, issue or PR triage, orchestration, or agent handoff.
  3. Read references/security-and-operations.md when the workflow needs safe outputs, network policy, authentication, lockdown, threat detection, or run observability.
  4. Read references/troubleshooting.md when the workflow fails to compile, install, authenticate, execute safe outputs, or access tools.

Step 3: Author or revise the workflow source

  1. Keep the workflow source of truth in .github/workflows/<workflow-name>.md.
  2. Use assets/workflow.template.md as the base shape when creating a new workflow.
  3. Choose the smallest viable trigger surface and repository role scope that satisfies the task.
  4. Keep permissions: read-only unless the workflow truly needs broader GitHub Actions permissions outside the agentic section.
  5. Prefer safe-outputs: for comments, issues, labels, PRs, agent assignment, and orchestration instead of granting direct write access to the agent.
  6. In safe-output workflows, instruct the agent to call noop when no action is required.
  7. Keep tools: and toolsets: minimal and specific to the task.
  8. Default to engine: copilot unless the task explicitly requires another engine and the repository is already prepared for that engine's authentication model.
  9. Configure network: with least privilege. Prefer ecosystem identifiers such as node, python, or github over individual registry domains when the compiler supports them.
  10. If strict mode and the installed CLI reject custom domains that the workflow still needs, prefetch external sources in deterministic setup steps and pass local files into the agent instead of broadly relaxing the firewall.
  11. Do not rely on ${{ steps.<id>.outputs.* }} placeholders reaching the agent-visible markdown body in real runs. If prompt instructions depend on runtime values, write them into a deterministic local file during setup and tell the agent to read that file.
  12. Use imported or reusable workflows only when the repository genuinely benefits from shared logic or orchestration.
  13. For recurring work across a dynamic set of inputs, prefer a reusable GH-AW worker plus a deterministic YAML wrapper for discovery and matrix fan-out.
  14. When a reusable GH-AW worker is called from a matrix, do not leave it on the default shared workflow-level concurrency group. Set an explicit concurrency group keyed by the matrix input or prompt identity so parallel legs are not cancelled by GitHub's one-running-one-pending concurrency behavior.
  15. Recompile the workflow after frontmatter, imports, or other compile-time configuration changes.
  16. If only the markdown body changed and the workflow is edited directly on GitHub.com, do not recompile solely for body text changes.
  17. Treat .github/aw/ as transient GH-AW runtime and compiler scratch space during local compile, validate, or trial flows unless the workflow intentionally uses checked-in files from that path.

Read the full file on GitHub · 91 lines

Files

What ships with it

6 files 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.

Changes

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.

  1. 2d ago First seen · 91 lines · 81 tokens per session scan A fdd02e24f189

Subscribe to this mod's changes

github-agentic-workflows is a skill published in the GitHub repository webmaxru/is-ai-native (5 stars, last pushed 10d ago), licensed MIT. It adds 81 tokens to every session and 2,334 once invoked, about $0.0004 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 2026-08-31.