183 skills · sorted by relevance
Use when executing implementation plans with independent tasks in the current session or facing 3+ independent issues that can be investigated without shared state or dependencies - dispatches fresh subagent for each task with code review between tasks, enabling fast iteration with quality gates
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Use when tackling complex reasoning tasks requiring step-by-step logic, multi-step arithmetic, commonsense reasoning, symbolic manipulation, or problems where simple prompting fails - provides comprehensive guide to Chain-of-Thought and related prompting techniques (Zero-shot CoT, Self-Consistency, Tree of Thoughts, Least-to-Most, ReAct, PAL, Reflexion) with templates, decision matrices, and research-backed patterns
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Use when implementing any feature or bugfix, before writing implementation code - write the test first, watch it fail, write minimal code to pass; ensures tests actually verify behavior by requiring failure first
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Update and maintain project documentation for local code changes using multi-agent workflow with tech-writer agents. Covers docs/, READMEs, JSDoc, and API documentation.
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Display the current state of the FPF knowledge base
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This skill should be used when need prioritize what changed code in repository human must review.
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Guide for setup Context7 MCP server to load documentation for specific technologies.
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Execute tasks through systematic exploration, pruning, and expansion using Tree of Thoughts methodology with meta-judge evaluation specifications and multi-agent evaluation
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Execute complete FPF cycle from hypothesis generation to decision
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Iterative PDCA cycle for systematic experimentation and continuous improvement
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Search the FPF knowledge base and display hypothesis details with assurance information
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Comprehensive multi-perspective review using specialized judges with debate and consensus building
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Load all open issues from GitHub and save them as markdown files
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Systematically fix all failing tests after business logic changes or refactoring
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Comprehensive guide for skill development based on Anthropic's official best practices - use for complex skills requiring detailed structure
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Curates insights from reflections and critiques into CLAUDE.md using Agentic Context Engineering
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Design multi-agent architectures for complex tasks. Use when single-agent context limits are exceeded, when tasks decompose naturally into subtasks, or when specializing agents improves quality.
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Create a workflow command that orchestrates multi-step execution through sub-agents with file-based task prompts
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Execute a task with sub-agent implementation and LLM-as-a-judge verification with automatic retry loop
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Reflect on previus response and output, based on Self-refinement framework for iterative improvement with complexity triage and verification
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Execute tasks through competitive multi-agent generation, meta-judge evaluation specification, multi-judge evaluation, and evidence-based synthesis
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creates draft task file in .specs/tasks/draft/ with original user intent
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Launch an intelligent sub-agent with automatic model selection based on task complexity, specialized agent matching, Zero-shot CoT reasoning, and mandatory self-critique verification
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Reset the FPF reasoning cycle to start fresh
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