Use when long-lived agents, project memories, system prompts, or playbooks are repeatedly summarized, rewritten, or optimized and risk losing domain detail, provenance, or execution learnings.
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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.
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实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
Attribution: Sourced from alirezarezvani/claude-skills by Alireza Rezvani, licensed MIT. Modified for this collection.
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
--outputThe stack detector prioritizes deterministic file signals over heuristics:
Start with a minimal, reliable pipeline:
Then layer advanced behavior (matrix builds, security scans, deploy gates).
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Use when long-lived agents, project memories, system prompts, or playbooks are repeatedly summarized, rewritten, or optimized and risk losing domain detail, provenance, or execution learnings.
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Use this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.
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Adds educational comments to a code file so it becomes a learning resource, asking for the file (with a list of close matches) if none is given. Use when preparing code for learners, onboarding, or teaching.
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Write, debug, and optimize Adobe Illustrator automation scripts using ExtendScript (JavaScript/JSX). Use when creating or modifying scripts that manipulate documents, layers, paths, text frames, colors, symbols, artboards, or any Illustrator DOM objects. Covers the complete JavaScript object model, coordinate system, measurement units, export workflows, and scripting best practices.
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Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)
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Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"
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