Inspect project files to identify languages and build tools, then generate starter GitHub Actions or GitLab CI workflows. Set up automated checks, caching, and deployment stages that reflect the project’s commands and target environments.
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Generate pragmatic CI/CD pipelines from detected project stack signals — fast baseline generation, repeatable checks, environment-aware deployment stages. Use when setting up CI for a new project, refactoring existing pipelines, or standardizing deployment workflows across multiple repos.
安装前可以查看交给 AI 代理的指令内容。
Tier: POWERFUL
Category: Engineering
Domain: DevOps / Automation
Use this skill to generate pragmatic CI/CD pipelines from detected project stack signals, not guesswork. It focuses on fast baseline generation, repeatable checks, and environment-aware deployment stages.
lint, test, build, deploy)python3 scripts/stack_detector.py --repo . --format text
python3 scripts/stack_detector.py --repo . --format json > detected-stack.json
Supports input via stdin or --input file for offline analysis payloads.
python3 scripts/pipeline_generator.py \
--input detected-stack.json \
--platform github \
--output .github/workflows/ci.yml \
--format text
Or end-to-end from repo directly:
python3 scripts/pipeline_generator.py --repo . --platform gitlab --output .gitlab-ci.yml
test, lint, build).lint/test/build).python3 scripts/stack_detector.py --help
--inputpython3 scripts/pipeline_generator.py --help
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Audit web applications for accessibility issues against WCAG 2.2 Level A and AA, the web accessibility standards. Get framework-specific code fixes, contrast checks, verification steps, and reports, with options for automated checks in development pipelines.
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Plan A/B tests that compare page variations, from headlines to pricing layouts. Define a hypothesis, choose success metrics, calculate sample size and duration, and prepare launch checks and results reports to support decisions.
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Create paid ad headlines and body copy tailored to each platform's character limits. Use existing performance data to develop variations, explore new messaging angles, and check copy for length, quality, and potential rejection risks.
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Review code changes from three critical perspectives: production failures, maintainability, and security. Get a consolidated report that ranks findings by severity and provides merge guidance, helping examine assumptions that an ordinary self-review may overlook.
中文简介正在准备中。当前显示英语简介。
Audit and improve content intended for citation in AI answers. Review evidence, author information, and structure, generate revised content and structured metadata, and keep a local record of observed citations across AI services.
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Create signed records for consequential agent actions such as deployments, deletions, and payments. Decide which actions need evidence, record the agent, action, target, and policy, then verify the resulting certificate offline to detect later changes.
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