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.
npx agentmods add skills/github/gh-aw/optimize-agentic-workflownpx skills add github/gh-aw --skill optimize-agentic-workflowgit clone --depth 1 https://github.com/github/gh-awWhat 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 | $0.00029 | $0.01064 |
| Opus 5 | $0.00015 | $0.00532 |
| Sonnet 5 | $0.00006 | $0.00213 |
| Haiku 4.5 | $0.00003 | $0.00106 |
Grade A, and why
optimize-agentic-workflow 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.
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 — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agentic Workflow Token Optimizer
Help users reduce the AI token usage and cost of GitHub Agentic Workflows in this repository.
Load These References First
Load these files from github/gh-aw (they are not available locally).
.github/aw/github-agentic-workflows.md.github/aw/token-optimization.md.github/aw/workflow-editing.md.github/aw/syntax.md
Load these only when relevant:
.github/aw/experiments.md.github/aw/safe-outputs.md
Available Commands
gh aw audit <run-id> --json
gh aw audit <base-run-id> <optimized-run-id>
gh aw logs <workflow-name> --json
gh aw compile <workflow-name>
gh aw status
Start the Conversation
Ask for one of these inputs:
- a workflow run URL (or run ID) to analyze
- a workflow name to review the source
- the guardrail that was exceeded (max-ai-credits, max-daily-ai-credits, max-tool-denials, max-turns / timeout)
Fast Path: Run URL Provided
If the user gives a GitHub Actions run URL:
- Extract the run ID
- Run
gh aw audit <run-id> --json - Inspect
agent_usage.aic,agent_usage.input_tokens,agent_usage.output_tokens,agent_usage.cache_read_tokens - Identify the most expensive phases before asking additional questions
Guardrail-Specific Entry Points
max-ai-credits exceeded
The workflow was stopped because it consumed more AI Credits than the configured per-run budget.
Priority checks:
- Which tool calls dominated token usage? (
token-usage.jsonl) - Is the prompt front-loading large payloads that could be fetched on demand?
- Are there repetitive extraction steps that sub-agents could handle cheaply?
- Does the frontier model handle tasks that a small model could do?
max-daily-ai-credits exceeded
The workflow is being blocked because its 24-hour AI Credits budget is exhausted.
Priority checks:
- What is the run cadence? (scheduled too frequently?)
- Does the workflow use cheap triage before escalating to the frontier model?
- Is batching or caching applicable to reduce run frequency?
- Are there noop early-exits for events that do not require agent action?
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.
- 2d ago First seen · 115 lines · 29 tokens per session scan A 1b345577056b
optimize-agentic-workflow is a skill published in the GitHub repository github/gh-aw (5,050 stars, last pushed yesterday), licensed MIT. It adds 29 tokens to every session and 1,064 once invoked, about $0.0001 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 2026-08-30.
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.