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Github Actions Optimizer

面向 GitHub Actions 的 CI/CD 优化分析 Skill。读取 .github/workflows 下的工作流文件并结合运行时长记录,从依赖缓存、任务并行、Runner 选择、触发条件、超时与并发、密钥与权限等方面给出速度、成本、安全和可靠性改进建议,最终输出含节省估算的 Markdown 优化报告。

charlie-morrison
charlie-morrison
热度 41.49
最近更新 2026-05-11 17:46:45
分类:开发依赖:代码执行 / 读写文件 / 第三方 API应用场景:DevOps / 代码审查 / 报告撰写 / 工作流

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add github-actions-optimizer
Metadata
name
github-actions-optimizer
description
Optimize GitHub Actions workflows for speed, cost, security, and reliability — analyze run times, cache strategies, job parallelism, and runner selection.
metadata
{"tags":["github-actions","ci-cd","optimization","devops","automation"]}

GitHub Actions Optimizer

Analyze and optimize GitHub Actions workflows for faster builds, lower costs, better security, and higher reliability. Reviews workflow files, run history, cache usage, and runner configurations. Use when CI is slow, expensive, or unreliable.

Usage

"Optimize my GitHub Actions workflows"
"Why are my CI builds so slow?"
"Audit my workflows for security issues"
"Reduce GitHub Actions costs"
"Find flaky steps in my CI pipeline"

How It Works

1. Workflow Discovery

# Find all workflow files
find .github/workflows -name "*.yml" -o -name "*.yaml" 2>/dev/null

# Check recent run durations
gh run list --limit 20 --json name,status,conclusion,startedAt,updatedAt,databaseId | python3 -c "
import json, sys
from datetime import datetime
runs = json.load(sys.stdin)
for r in runs:
    start = datetime.fromisoformat(r['startedAt'].rstrip('Z'))
    end = datetime.fromisoformat(r['updatedAt'].rstrip('Z'))
    duration = (end - start).total_seconds() / 60
    print(f'{r[\"name\"]:30s} {r[\"conclusion\"]:10s} {duration:.1f}min')
"

2. Speed Optimization

Caching analysis:

  • Are dependencies cached? (actions/cache or actions/setup-node with cache)
  • Cache hit rate from recent runs
  • Missing cache keys for build artifacts, Docker layers, compiled assets
  • Cache size approaching 10GB limit?
  • Stale cache keys never cleaned up

Job parallelism:

  • Sequential jobs that could run in parallel
  • Large matrix builds that could be split
  • Test suites that could be sharded
  • Independent steps within a single job

Runner optimization:

  • Self-hosted vs GitHub-hosted: cost/speed tradeoff
  • Larger runners available? (ubuntu-latest-xl, ubuntu-latest-16-cores)
  • ARM runners for compatible workloads (30% cheaper)
  • Container jobs vs VM jobs

Build optimization:

  • Unnecessary checkout of full git history (fetch-depth: 0)
  • Redundant install steps across jobs
  • Tests running on every push instead of just PRs
  • Docker builds without layer caching
  • Missing path filters (trigger on irrelevant file changes)

3. Cost Reduction

Minute savings:

  • Identify most expensive workflows (minutes × frequency)
  • Timeout missing on long-running jobs (default: 6 hours!)
  • Concurrency groups to cancel redundant runs
  • Path filtering to skip irrelevant triggers
  • PR-only vs push+PR triggers

Storage savings:

  • Artifact retention too long (default: 90 days)
  • Large artifacts uploaded unnecessarily
  • Cache entries never evicted

4. Security Audit

  • Pinned actions: Using @v3 instead of SHA pinning
  • Secrets exposure: Secrets passed to untrusted steps
  • GITHUB_TOKEN permissions: Overly broad default permissions
  • Pull request target: Workflow runs on pull_request_target with checkout of PR head
  • Script injection: Untrusted input in run: blocks (${{ github.event.issue.title }})
  • Third-party actions: Unverified marketplace actions with broad permissions
  • Environment protection: Missing required reviewers on production deployments

5. Reliability

  • Retry strategy: Flaky steps without retry configuration
  • Timeout values: Missing or too generous timeouts
  • Error handling: continue-on-error hiding real failures
  • Status checks: Required checks that aren't actually running
  • Concurrency: Race conditions between parallel workflow runs

6. Modern Patterns

Recommend modern GitHub Actions features:

  • Reusable workflows for DRY CI
  • Composite actions for shared steps
  • Environments with deployment protection rules
  • OIDC for cloud authentication (no long-lived secrets)
  • Merge queues for safe main branch

Output

## GitHub Actions Optimization Report

**Workflows:** 5 | **Avg monthly minutes:** 12,400 | **Monthly cost:** ~$99

### ⚡ Speed Improvements

1. **Add dependency caching** — ci.yml
   Current: `npm ci` runs fresh every time (2m 15s)
   Fix: Add `cache: 'npm'` to `actions/setup-node`
   Savings: ~1m 45s per run × 180 runs/mo = 315 min/mo

2. **Parallelize test suites** — ci.yml
   Current: Unit + integration + e2e run sequentially (18 min)
   Fix: Split into 3 parallel jobs
   Savings: ~12 min per run (runs in 6 min instead of 18)

3. **Add path filters** — ci.yml
   Current: Triggers on all pushes including docs changes
   Fix: `paths-ignore: ['docs/**', '*.md', 'LICENSE']`
   Savings: ~40 unnecessary runs/mo × 18 min = 720 min/mo

### 🔐 Security Issues

4. **Unpinned action** — deploy.yml:12
   `uses: actions/checkout@v4` → pin to SHA
   
5. **Script injection risk** — pr-comment.yml:8
   `run: echo "${{ github.event.comment.body }}"` 
   → Use environment variable instead

6. **Broad GITHUB_TOKEN** — all workflows
   No `permissions:` block = read-write to everything
   → Add explicit `permissions: { contents: read }`

### 💰 Cost Savings
| Optimization | Minutes Saved/mo | $ Saved/mo |
|-------------|------------------|------------|
| Dependency cache | 315 | $2.52 |
| Path filters | 720 | $5.76 |
| Concurrency cancel | 200 | $1.60 |
| Timeout (6h → 30m) | ~0 (prevents surprise) | — |
| **Total** | **1,235** | **$9.88** |

Projected monthly: 12,400 → 11,165 min (-10%)