Skip to content
cccskills
FreePublished on GitHub

github-actions-efficiency

Audit GitHub Actions workflows and run history to identify wasted automated checks and runner time. Get up to three evidence-based improvements aimed at reducing runtime or cost while preserving required validation.

OriginalShow the original description

Audit GitHub Actions workflow efficiency and recommend fixes to reduce CI minutes and costs.

Works withGitHub
See how to install

Good for

  • Investigate long GitHub Actions runs
  • Review CI costs and unnecessary workflow runs
  • Improve caching and cancellation of overlapping runs
  • Create an initial GitHub Actions workflow

Files included (5)

  • SKILL.md4.5 KB
  • references/actions.md3.1 KB
  • references/patterns.md1.3 KB
  • references/reporting.md1.5 KB
  • references/review-rubric.md465 B

SKILL.md (original)

Review the instructions the agent will receive before you install.

GitHub Actions Efficiency

Use this skill as a lean entrypoint for GitHub Actions efficiency work. Inspect the repo, identify the waste source, and load only the reference material needed for the current task.

If no workflows exist yet, load references/actions.md and define a baseline before proceeding with the steps below.

If shell or gh CLI access is unavailable: ask the user to paste .github/workflows/ contents and gh run list --limit 10 output. If only partial files are provided, note it: "Audit based on provided files only; some insights may be incomplete." Begin responses from files alone with: "Static-only analysis (not confirmed with live runs)."

Use This Skill When

  • The user wants to reduce GitHub Actions runtime, CI cost, or wasted workflow runs.
  • The repo has existing workflows in .github/workflows/ or explicit GitHub Actions configuration questions.
  • The user asks for caching, concurrency, path filters, matrix reduction, job optimization, or workflow-specific fixes.
  • The user needs help creating a new GitHub Actions workflow or CI baseline from scratch.

Load Only What You Need

Core Workflow

1. Measure first

rg -n "on:|concurrency:|paths:|paths-ignore:|strategy:|matrix:|cache:" .github/workflows
gh run list --limit 10
run_id=$(gh run list --limit 1 --json databaseId --jq '.[0].databaseId')
gh run view "$run_id" --log-failed

Look for: missing dependency caches, missing concurrency cancellation, over-broad triggers, duplicate workflow coverage, and expensive jobs that run on every change regardless of scope.

2. Apply guardrails

Check each proposed fix against these rules before recommending it:

  1. Does not hide required validation — drop any fix that removes release, schema, migration, or shared-library checks.
  2. Does not reduce parallelism without justification — drop unless the user prioritised cost over latency and the new critical path stays within 1.25× the original.
  3. Preserves only documented matrix legs — drop matrix legs with no explicit version or platform commitment.
  4. Write-back jobs use opt-in triggers — flag (do not drop) formatter or bot jobs that run automatically; recommend an opt-in trigger instead.
  5. Repo changes stay separate from org settings — split any fix that mixes repo-editable YAML with org-level or GitHub-account settings into two distinct recommendations.

3. Select the top 3 fixes

From the six candidates below, keep only those supported by audit evidence from step 1 and passing all guardrails from step 2. Rank survivors by estimated daily CI minutes saved (per-run savings × runs per day). Select all candidates that meet both criteria, up to a maximum of 3.

  1. Add dependency caching with lockfile-based keys
  2. Add or correct concurrency cancellation
  3. Remove duplicate workflow coverage before merging jobs
  4. Narrow workflow or job triggers safely
  5. Reduce matrix breadth to match risk and event type
  6. Parallelize independent jobs on the critical path

4. Verify

  • If gh CLI access is available, validate path-gating and concurrency cancellation with a live test push on a non-protected branch.
  • If live validation is not possible, state that explicitly in the output.
  • Treat unexpected live behavior as a real bug even when the YAML looks correct.

Required Output

  1. Waste sources — top cost or latency drivers found in step 1
  2. Proposed fixes — top 3 (or all remaining) with supporting audit evidence
  3. Validation — what was proven live, what was checked locally only, and any remaining risk
  4. Impact — expected savings vs. measured savings; separate PR wall-clock time from total runner time

References

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

Map an existing codebase into seven documents covering its technology, structure, architecture, conventions, integrations, tests, and concerns. Link findings to file evidence, mark unknowns, and highlight differences between stated plans and the actual implementation.

  • Get oriented in an unfamiliar codebase
  • Prepare documentation for a project handover
  • Compare documented architecture with implementation
github/awesome-copilot40.2KUpdated Oct 17, 2026

Assess a repository’s readiness for AI agents with AgentRC and generate a standalone HTML dashboard. Review scores across nine areas, see prioritized improvements, and optionally apply scoring policies tailored to an organization.

  • Assessing a repository’s readiness for AI agents
  • Prioritizing improvements from assessment results
  • Reviewing scores under an organization’s own policy
github/awesome-copilot40.2KUpdated Oct 17, 2026

Generate coding agent instruction files from a project's actual code using AgentRC. Capture detected languages, frameworks, and conventions in one shared file or split rules by topic and project area, with a preview before writing.

  • Create project instructions for VS Code Copilot
  • Refresh agent rules after project changes
  • Split monorepo instructions by project area
github/awesome-copilot40.2KUpdated Oct 17, 2026

Customize AgentRC readiness assessments to match team or organization standards. Choose or create policies that adjust check importance, disable irrelevant checks, set pass thresholds, and define requirements for automated checks in CI workflows.

  • Set shared readiness standards across an organization
  • Focus assessments on AI tooling
  • Adjust the importance of individual checks
github/awesome-copilot40.2KUpdated Oct 17, 2026

Analyze supplied ad performance data to identify wasted spend, strong campaigns, and next tests. Compare channels, assess whether test differences are meaningful, and produce budget reallocation recommendations with a prioritized action plan.

  • Identify ads and keywords to pause
  • Find campaigns and audiences worth scaling
  • Assess whether A/B test results are meaningful
github/awesome-copilot40.2KUpdated Oct 17, 2026

Turn code files into learning resources by adding comments that explain syntax, programming concepts, and design choices. Adjust explanation depth and repetition to the reader’s knowledge while preserving file structure, encoding, and syntax.

  • Add beginner-friendly explanations to existing code
  • Document the reasoning behind syntax and design choices
  • Adjust comment depth for readers with different experience
github/awesome-copilot40.2KUpdated Oct 17, 2026

See all skills by github

Report a problem with this skill