What is optimize-agentic-workflow?

A guide for measuring and reducing token use in GitHub Agentic Workflows, which are GitHub Actions workflows that use AI agents. It covers audits, logs, compilation, status checks, and workflow editing.

Use it to audit a workflow run, compare an original with an optimized run, inspect agent token metrics, review workflow source, or check workflow status.

It helps find workflows that exceed limits on AI credits, turns, tool denials, or execution time and identifies where their token use can be reduced.

optimize-agentic-workflow — Skill for Claude Code and Codex by github

About the project

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.

github/gh-aw · 5,579 stars · on GitHub · gh.io

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.

Any agent

npx skills add github/gh-aw --skill optimize-agentic-workflow

Clone the repo

git clone --depth 1 https://github.com/github/gh-aw

Made for: Claude Code, Codex.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/github/gh-aw/optimize-agentic-workflow/github.svg)](https://agentmods.dev/skills/github/gh-aw/optimize-agentic-workflow)

Your own site

<a href="https://agentmods.dev/skills/github/gh-aw/optimize-agentic-workflow"><img src="https://agentmods.dev/badge/skills/github/gh-aw/optimize-agentic-workflow/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/github/gh-aw/optimize-agentic-workflow"><img src="https://agentmods.dev/badge/skills/github/gh-aw/optimize-agentic-workflow.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 29 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,064 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector pass 7 Sept 2026

How audits are shown

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.

Model	Per session	Once invoked
Fable 5.1	$0.00029	$0.01064
Opus 5.5	$0.00012	$0.00426
Sonnet 5.5	$0.00006	$0.00213
Haiku 4.5	$0.00003	$0.00106

Measured 2mo ago against content hash 1b345577056b, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 2mo 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.

.github/skills/optimize-agentic-workflow/SKILL.md · 115 lines

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?

Read the full file on GitHub · 115 lines

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.

2mo ago First seen · 115 lines · 29 tokens per session scan A 1b345577056b

Subscribe to this mod's changes

optimize-agentic-workflow is a skill published in the GitHub repository github/gh-aw (5,579 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 5.5. 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.

Analytics and metrics Building agents Claude Code actions cai ci codex gh-extension

optimize-agentic-workflow next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
optimize-agentic-workflowgithub/gh-aw	Skill	5.6k	yesterday	MIT	A	29	Claude Code, Codex
watch-prjustincampbell/gh-watch	Skill	4	7d ago	MIT	A	48	Claude Code
watch-tagjustincampbell/gh-watch	Skill	4	7d ago	MIT	A	62	Claude Code
watch-branchjustincampbell/gh-watch	Skill	4	7d ago	MIT	A	45	Claude Code
watch-commitjustincampbell/gh-watch	Skill	4	7d ago	MIT	A	48	Claude Code
c-githubdaxaur/openpaw	Skill	177	5mo ago	MIT	A	48	Claude Code, Codex
update-architecture-docsAlexSkrypnyk/scaffold	Skill	11	yesterday	GPL-3.0	A	46	Claude Code