Shapes turns action-first: action leads, steps numbered, state restated. Use for ADHD-friendly output. Do not use to trim tokens; use response-compression.
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Configures GitHub Actions CI/CD workflows for testing, linting, and deployment. Use when setting up automation for a Python, Rust, or TypeScript project.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Set up GitHub Actions workflows for continuous integration and deployment.
/attune:upgrade-project instead for updating existing workflowsls -la .github/workflows/
Verification: Run the command with --help flag to verify availability.
from project_detector import ProjectDetector
detector = ProjectDetector(Path.cwd())
language = detector.detect_language()
required_workflows = {
"python": ["test.yml", "lint.yml", "typecheck.yml"],
"rust": ["ci.yml"],
"typescript": ["test.yml", "lint.yml", "build.yml"],
}
missing = detector.get_missing_configurations(language)
Verification: Run pytest -v to verify tests pass.
workflows_dir = Path(".github/workflows")
workflows_dir.mkdir(parents=True, exist_ok=True)
for workflow in required_workflows[language]:
template = templates_dir / language / "workflows" / f"{workflow}.template"
output = workflows_dir / workflow
engine.render_file(template, output)
print(f"✓ Created: {output}")
Verification: Run the command with --help flag to verify availability.
# Syntax check (requires act or gh CLI)
gh workflow list
# Or manually check YAML syntax
python3 -c "import yaml; yaml.safe_load(open('.github/workflows/test.yml'))"
Verification: Run pytest -v to verify tests pass.
# Good - pinned to major version
- uses: actions/checkout@v4
- uses: actions/setup-python@v5
# Avoid - unpinned or outdated
- uses: actions/checkout@v2
- uses: actions/setup-python@latest
Verification: Run pytest -v to verify tests pass.
strategy:
matrix:
python-version: ["3.10", "3.11", "3.12"]
os: [ubuntu-latest, macos-latest, windows-latest]
Verification: Run pytest -v to verify tests pass.
- uses: actions/setup-python@v5
with:
python-version: '3.10'
cache: 'pip' # Cache pip dependencies
Verification: Run python --version to verify Python environment.
When writing inline shell scripts in workflows, ensure proper exit code handling:
# BAD - pipeline masks exit code
- run: |
make typecheck 2>&1 | grep -v "^make\["
echo "Typecheck passed" # Runs even if make failed!
# GOOD - use pipefail
- run: |
set -eo pipefail
make typecheck 2>&1 | grep -v "^make\["
# GOOD - capture exit code explicitly
- run: |
output=$(make typecheck 2>&1) || exit_code=$?
echo "$output" | grep -v "^make\[" || true
exit ${exit_code:-0}
For complex wrapper scripts, run /pensive:shell-review before integrating.
To update workflows to latest versions:
/attune:upgrade-project --component workflows
Verification: Run the command with --help flag to verify availability.
Skill(attune:project-init) - Full project initializationSkill(sanctum:pr-prep) - PR preparation with CI checks.github/workflows/
(Python: test.yml + lint.yml + typecheck.yml; Rust: ci.yml; TypeScript: test.yml + lint.yml +
build.yml) and contain valid YAML syntax verified by
python3 -c "import yaml; yaml.safe_load(open('...'))".gh workflow list returns each created workflow file as an entry, confirming GitHub
recognizes the workflow definitions.set -eo pipefail or explicit exit-code
capture; pipeline-masked failures (cmd | grep) without pipefail are flagged as errors.아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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Shapes turns action-first: action leads, steps numbered, state restated. Use for ADHD-friendly output. Do not use to trim tokens; use response-compression.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Inverts burden of proof for code additions. Use when reviewing PRs, planning refactors, or running unbloat to challenge every addition's necessity.
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Tracks per-agent token usage and flags waste in parallel dispatch. Use when evaluating parallel agent efficiency or after a multi-agent run.
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Coordinates Claude agent teams via filesystem protocol. Use when orchestrating parallel agents with task dependencies. Do not use for single-agent tasks.
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Evaluates API surface design, consistency, and exemplar alignment. Use when reviewing public API changes or before releasing a new API surface.
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Selects architecture paradigm via research before scaffolding. Use when architecture is undecided and the choice needs justification and documentation.
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