Loading...
Loading...
Found 532 Skills
This skill should be used when the user asks to "review code", "review my changes", "check effect patterns", "run effect review", "effect review", "review for effect best practices", or wants a comprehensive code review against Effect-TS conventions, branded types, observability, error handling, test coverage, and UI quality.
Use when creating a new skill with maximum quality. Launches 3 parallel competing approaches (skill-creator, superpowers writing-skills, and manual), compares results on 5 dimensions, then synthesizes the best elements into a final skill. Triggers on "build a skill", "create a skill", "new skill".
Ecosystem self-evolution orchestrator. Detects project lifecycle phases, evaluates agent relevance, synthesizes cross-agent knowledge, and proposes evolution actions (health checks, fitness scoring, evolution proposals).
执行完整的 7 阶段深度研究流程。接收结构化研究任务，自动部署多个并行研究智能体，生成带完整引用的综合研究报告。当用户有结构化的研究提示词时使用此技能。
Automatic agent selection and intelligent task routing. Analyzes user requests and automatically selects the best specialist agent(s) without requiring explicit user mentions.
After the task execution is completed, prompt the user to open a new Agent to review the uncommitted git code. Athletes should not act as referees; proceed with the wrap-up only after the review is approved.
Comprehensive security audit of codebase using multiple security-auditor agents. Use before production deployments or after major features.
Documentation consistency reviewer that checks alignment between code implementation and documentation. Use when user requests reviewing documentation vs code consistency, checking if README/docs are outdated, verifying API documentation accuracy. Applicable for (1) reviewing README vs implementation consistency (2) checking if docs/ directory content is outdated (3) verifying API/config documentation accuracy (4) generating documentation consistency reports. Trigger words include doc review, documentation consistency, check outdated docs, verify docs.
Deep web research with parallel investigators, multi-wave exploration, and structured synthesis. Spawns multiple web-researcher agents to explore different facets of a topic simultaneously, launches additional waves when gaps are identified, then synthesizes findings. Use when asked to research, investigate, compare options, find best practices, or gather comprehensive information from the web.\n\nThoroughness: quick for factual lookups | medium for focused topics | thorough for comparisons/evaluations (waves continue while critical gaps remain) | very-thorough for comprehensive research (waves continue until satisficed). Auto-selects if not specified.
Fan out a prompt to multiple AI coding agents in parallel and synthesize their responses.
Azure cloud operations orchestration and Microsoft Agent Framework integration hub.
Run any question, idea, or decision through a council of 5 AI advisors who independently analyze it, peer-review each other anonymously, and synthesize a final verdict. Based on Karpathy's LLM Council methodology. MANDATORY TRIGGERS: 'council this', 'run the council', 'war room this', 'pressure-test this', 'stress-test this', 'debate this'. STRONG TRIGGERS (use when combined with a real decision or tradeoff): 'should I X or Y', 'which option', 'what would you do', 'is this the right move', 'validate this', 'get multiple perspectives', 'I can't decide', 'I'm torn between'. Do NOT trigger on simple yes/no questions, factual lookups, or casual 'should I' without a meaningful tradeoff (e.g. 'should I use markdown' is not a council question). DO trigger when the user presents a genuine decision with stakes, multiple options, and context that suggests they want it pressure-tested from multiple angles.