issue-decomposition
Use when the user says 'create issues for...', 'break this into tasks', or 'decompose this feature', or when converting a project description into GitHub issues - produces 5-15 INVEST-quality issues with user stories, acceptance criteria, dependencies, labels, and T-shirt estimates
research-topic-summarize
Use when asked to research a technology, library, or concept via web search, compare options (X vs Y), or gather background for a decision - synthesizes findings into a cited summary with comparison tables, screenshots of key visuals, and a recommendation
requirement-elicitation
Use when gathering project requirements, or when a user says 'help me define requirements' or 'figure out what to build' - adaptive wizard covering functional, nonfunctional, constraints, and edge cases with domain-specific questions for web apps, APIs, CLIs, mobile, and data pipelines
project-planning
Use when the user says 'build a project plan', 'help me plan X', 'break down this project', or 'scope out this feature' - orchestrates issue-decomposition, architecture-diagramming, dependency-mapping, and timeline-planning to produce GitHub issues, diagrams, and a saved plan document
project-analysis
Use when starting work on an unfamiliar codebase or asked to 'analyze this project' - maps structure, project type, architecture pattern, key files, and dependencies into a standard report
context-aware-questions
Use when asked 'what am I missing?', reviewing an issue or draft before submission, or checking documentation completeness - detects requirement, spec, and doc gaps and generates prioritized actionable questions
idea-to-design
Use when asked to turn an idea note into a design document, plan an idea, or make an idea real - autonomously researches the idea and produces Design.md, Decisions.md, and Research.md in an Ideas/<Idea Name>/ folder, asking zero questions
build-faq-from-issues
Use when building or updating an FAQ from closed GitHub issues, documenting recurring support questions, or reducing repeat questions - extracts common questions from resolved issues and generates a categorized FAQ document with synthesized answers and source links
consolidate-notes-summary
Use when knowledge on a topic is scattered across multiple project notes and someone asks to consolidate, synthesize, or summarize what has been documented - searches notes by topic and produces a single summary with key points, timeline, cross-references, and documentation gaps
at-risk-detection
Use when asked about project health, stale or blocked issues, deadline risk, or scope creep - scans GitHub issues and PRs with heuristics (staleness, blocked labels, milestone dates, size, unassigned high-priority) and produces a severity-ranked at-risk report
architecture-diagramming
Use when the user asks for an architecture diagram, says 'show me the system architecture' or 'diagram the components', or during project planning - generates GitHub-compatible Mermaid flowcharts showing components, layers, subgraph boundaries, and data flows
dependency-mapping
Use when asked to map issue dependencies, show what blocks what, or find the critical path through project work - reads GitHub issues for blocked-by/depends-on signals and generates Mermaid flowcharts of blocking chains
daily-planning-ritual
Interactive daily planning ritual that guides the user through a reflective conversation across all life dimensions (work, fitness, relationship, social, adventure) and produces a holistic day plan. Use when the user requests to plan their day with phrases like "plan the day", "plan my day", "plan today", or similar variations.
mermaid-diagrams
Use when creating any Mermaid diagram (flowchart, sequence, class, state, ER, Gantt) in markdown, or when a diagram fails to render on GitHub - covers the syntax rules that break GitHub rendering (parentheses in labels, mismatched brackets) plus per-type examples
stakeholder-updates
Use when writing status updates, communicating delays or blockers, announcing launches, or requesting decisions and resources from stakeholders - structures the message by audience level (exec/management/peer/team) with lead-with-the-punchline framing
summarize-conversation-thread
Use when catching up on a long GitHub issue or PR discussion thread, or asked to summarize a thread, extract key decisions, or list action items - produces a structured summary with TL;DR, decisions, action items, open questions, and next steps
triage-new-issues
Use when processing new or untriaged GitHub issues - assesses urgency from keyword and label signals, assigns P0-P3 priority, suggests labels and assignees, and flags issues needing escalation or clarification
timeline-planning
Use when asked for a project timeline, schedule, sprint plan, or Gantt chart, or after breaking work into issues - converts issue estimates and dependencies into a Mermaid Gantt chart with phases, milestones, and critical path
Git Commit with Auto-Changelog
Automatically documents code changes in a searchable changelog when committing to git. Creates structured entries with what/why/issues. Use when user asks to commit changes to git.
coderabbit-fix-flow
This skill should be used when CodeRabbit code review feedback needs to be processed and fixed systematically. Use after running `coderabbit --plain` to automatically save feedback, analyze issues using MCP tools, and implement minimal code fixes with proper planning.
Collision-Zone Thinking
Force unrelated concepts together to discover emergent properties - "What if we treated X like Y?"
AgentDB Performance Optimization
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
using-superpowers
Use when starting any conversation - establishes mandatory workflows for finding and using skills, including using Read tool before announcing usage, following brainstorming before coding, and creating TodoWrite todos for checklists
agentic-jujutsu
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
AgentDB Memory Patterns
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
AgentDB Vector Search
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
AgentDB Advanced Features
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
verification-before-completion
Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always
Verification & Quality Assurance
Comprehensive truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability.
testing-skills-with-subagents
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
writing-plans
Use when design is complete and you need detailed implementation tasks for engineers with zero codebase context - creates comprehensive implementation plans with exact file paths, complete code examples, and verification steps assuming engineer has minimal domain knowledge
When Stuck - Problem-Solving Dispatch
Dispatch to the right problem-solving technique based on how you're stuck
writing-skills
Use when creating new skills, editing existing skills, or verifying skills work before deployment - applies TDD to process documentation by testing with subagents before writing, iterating until bulletproof against rationalization
Scale Game
Test at extremes (1000x bigger/smaller, instant/year-long) to expose fundamental truths hidden at normal scales
Hooks Automation
Automated coordination, formatting, and learning from Claude Code operations using intelligent hooks with MCP integration. Includes pre/post task hooks, session management, Git integration, memory coordination, and neural pattern training for enhanced development workflows.
hive-mind-advanced
Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory
Simplification Cascades
Find one insight that eliminates multiple components - "if this is true, we don't need X, Y, or Z"
root-cause-tracing
Use when errors occur deep in execution and you need to trace back to find the original trigger - systematically traces bugs backward through call stack, adding instrumentation when needed, to identify source of invalid data or incorrect behavior
requesting-code-review
Use when completing tasks, implementing major features, or before merging to verify work meets requirements - dispatches code-reviewer subagent to review implementation against plan or requirements before proceeding
receiving-code-review
Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation
ReasoningBank Intelligence
Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems.
Meta-Pattern Recognition
Spot patterns appearing in 3+ domains to find universal principles
github-workflow-automation
Advanced GitHub Actions workflow automation with AI swarm coordination, intelligent CI/CD pipelines, and comprehensive repository management
brainstorming
Use when creating or developing anything, before writing code or implementation plans - refines rough ideas into fully-formed designs through structured Socratic questioning, alternative exploration, and incremental validation
defense-in-depth
Use when invalid data causes failures deep in execution, requiring validation at multiple system layers - validates at every layer data passes through to make bugs structurally impossible
condition-based-waiting
Use when tests have race conditions, timing dependencies, or inconsistent pass/fail behavior - replaces arbitrary timeouts with condition polling to wait for actual state changes, eliminating flaky tests from timing guesses
Inversion Exercise
Flip core assumptions to reveal hidden constraints and alternative approaches - "what if the opposite were true?"
finishing-a-development-branch
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup
AgentDB Learning Plugins
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
executing-plans
Use when partner provides a complete implementation plan to execute in controlled batches with review checkpoints - loads plan, reviews critically, executes tasks in batches, reports for review between batches
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