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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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Use when creating or editing skills, before deployment, to verify they work under pressure and resist rationalization - applies RED-GREEN-REFACTOR cycle to process documentation by running baseline without skill, writing to address failures, iterating to close loopholes
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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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Apply writing rules to any documentation that humans will read. Makes your writing clearer, stronger, and more professional.
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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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This skill should be used when need prioritize what changed code in repository human must review.
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Guide for setup Codemap CLI for intelligent codebase visualization and navigation
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Display the current state of the FPF knowledge base
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Iterative PDCA cycle for systematic experimentation and continuous improvement
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Use when adding metadata to commits without changing history, tracking review status, test results, code quality annotations, or supplementing commit messages post-hoc - provides git notes commands and patterns for attaching non-invasive metadata to Git objects.
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Load all open issues from GitHub and save them as markdown files
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Use this skill when you writing commands, hooks, skills for Agent, or prompts for sub agents or any other LLM interaction, including optimizing prompts, improving LLM outputs, or designing production prompt templates.
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Execute complete FPF cycle from hypothesis generation to decision
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Implement a task step by step with automated LLM-as-Judge verification at the end of each phase
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Verify what PR review comments have been addressed (committed/pushed OR uncommitted local changes) and resolve the threads that are genuinely fixed or no longer relevant.
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Review your local uncommitted working-tree changes (git diff plus untracked files) and return actionable improvement suggestions. Use before committing, when nothing has been pushed yet.
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Systematically fix all failing tests after business logic changes or refactoring
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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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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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Search the FPF knowledge base and display hypothesis details with assurance information
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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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Reset the FPF reasoning cycle to start fresh
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Review an existing GitHub pull request and post inline review comments on its diff. Use when the changes are on an opened PR rather than your local working tree.
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