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Found 48 Skills
Conduct exhaustive, citation-rich research on any topic using all available tools: web search, browser automation, documentation APIs, and codebase exploration. Use when asked to "research X", "find out about Y", "investigate Z", "deep dive into...", "what's the current state of...", "compare options for...", "fact-check this...", or any request requiring comprehensive, accurate information from multiple sources. Prioritizes accuracy over speed, cross-references claims across sources, identifies conflicts, and provides full citations. Outputs structured findings with confidence levels and source quality assessments.
Guide users step-by-step through manually testing whatever is currently being worked on. Use when asked to "test this", "verify it works", "let's test", "manual testing", "QA this", "check if it works", or after implementing a feature that needs verification before proceeding.
Answer questions about the AI SDK and help build AI-powered features. Use when developers: (1) Ask about AI SDK functions like generateText, streamText, ToolLoopAgent, or tools, (2) Want to build AI agents, chatbots, or text generation features, (3) Have questions about AI providers (OpenAI, Anthropic, etc.), streaming, tool calling, or structured output.
Critique UI/UX designs for clarity, hierarchy, interaction, accessibility, and craft. Use for design reviews, PR feedback on UI changes, evaluating mockups, checking if a component is ship-ready, or when honest feedback is needed on whether something meets a high bar.
Manual testing workflow for Claude HUD to verify core functionality. Use when asked to "test the app", "verify the app works", "run manual tests", "test after changes", or after implementing significant features. Performs full reset, launches app, and guides through verification checklist.
Identify non-obvious signals, hidden patterns, and clever correlations in datasets using investigative data analysis techniques. Use when analyzing social media exports, user data, behavioral datasets, or any structured data where deeper insights are desired. Pairs with personality-profiler for enhanced signal extraction. Triggers on requests like "what patterns do you see", "find hidden signals", "correlate these datasets", "what am I missing in this data", "analyze across datasets", "find non-obvious insights", or when users want to go beyond surface-level analysis. Also use proactively when you notice interesting anomalies or correlations during any data analysis task.
Analyze recent conversation context and capture learnings to project knowledge files (for project-specific insights) or skills/commands/subagents (for cross-project patterns). Use when the user asks to "capture this learning", "update the docs with this", "remember this for next time", "document this issue", "add this to CLAUDE.md", "save this knowledge", or "update project knowledge". Also triggers after resolving build/setup issues, discovering non-obvious patterns, or completing debugging sessions with valuable insights.
Compare OpenAI Codex GPT-5.3 and code-searcher responses for comprehensive dual-AI code analysis. Use when you need multiple AI perspectives on code questions.
Generate comprehensive implementation tutorial documents with deep background, context, rationale, and step-by-step milestones. Use when the user wants to learn by building—creating detailed guides instead of making direct code changes. Triggers on requests like "create a tutorial for", "implementation guide", "teach me how to implement", or explicit /tutorial invocation.
Apply startup execution wisdom to product, strategy, and business decisions. Use for feature prioritization, build-vs-buy decisions, go-to-market planning, pricing, hiring, scope/timeline reality checks, or when evaluating whether an idea has product-market fit potential.
Expertise in architecting, implementing, reviewing, and debugging hierarchical matching systems. Use when working with: (1) Two-sided matching (Gale-Shapley, hospital-resident, student-school), (2) Assignment/optimization problems (Hungarian algorithm, bipartite matching), (3) Multi-level hierarchy matching (org charts, taxonomies, nested categories), (4) Entity resolution and record linkage across hierarchies. Triggers: debugging match quality issues, reviewing matching algorithms, translating business requirements into constraints, validating match correctness, architecting new matching systems, fixing unstable matches, resolving constraint violations, diagnosing preference misalignment.
Build a retrieval-optimized knowledge layer over agent documentation in dotfiles (.claude, .codex, .cursor, .aider). Use when asked to "optimize docs", "improve agent knowledge", "make docs more efficient", or when documentation has accumulated and retrieval feels inefficient. Generates a manifest mapping task-contexts to knowledge chunks, optimizes information density, and creates compiled artifacts for efficient agent consumption.