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github-actions-efficiency

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

Install / Use

npx skills add github/awesome-copilot --skill github-actions-efficiency

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

89/100

Category

Automation

Supported Platforms

Universal

Our assessment of github-actions-efficiency

github-actions-efficiency scores 89/100 on our quality scale, 613th of 1,335 Automation skills we index (top 46%).

Its SKILL.md is 4.4 KB long, well organised into 10 sections with 1 code example: a solid amount of guidance for an agent.

With 39,348 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
26/30
Structure
17/20
Description
12/15
Adoption
20/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 2 days ago, so github-actions-efficiency is actively maintained.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

Safety scan

No issues found

Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands.

Automated pattern scan on 2026-09-26. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.

github-actions-efficiency compared with similar skills

All 4 of these similar skills score higher than github-actions-efficiency; compare them before choosing.

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github-actions-efficiency (this skill)by github8939.3k2d agoSKILL.md
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Frequently asked questions

How do I install github-actions-efficiency?
Run npx skills add github/awesome-copilot --skill github-actions-efficiency. The install tabs above show the steps for each supported agent.
Which AI agents does github-actions-efficiency work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is github-actions-efficiency safe to use?
Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is github-actions-efficiency still maintained?
The repository was last updated 2 days ago, so github-actions-efficiency is actively maintained.

name: github-actions-efficiency description: 'Audit GitHub Actions workflow efficiency and recommend fixes to reduce CI minutes and costs.'

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

Related Skills

View on GitHub
GitHub Stars39.3k
CategoryAutomation
Updated2d ago
Forks5.0k

Languages

JavaScript

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions