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Found 594 Skills
PGA Tour, LPGA, and DP World Tour golf data via ESPN public endpoints — tournament leaderboards, scorecards, season schedules, golfer profiles/overviews, and news. Zero config, no API keys. Use when: user asks about golf scores, tournament leaderboards, scorecards, PGA Tour schedule, golfer profiles, golfer season stats, LPGA results, or golf news. Don't use when: user asks about other sports.
Comprehensive prompt and context engineering for any AI system. Four modes: (1) Craft new prompts from scratch, (2) Analyze existing prompts with diagnostic scoring and optional improvement, (3) Convert prompts between model families (Claude/GPT/Gemini/Llama), (4) Evaluate prompts with test suites and rubrics. Adapts all recommendations to model class (instruction-following vs reasoning). Validates findings against current documentation. Use for system prompts, agent prompts, RAG pipelines, tool definitions, or any LLM context design. NOT for running prompts, generating content, or building agents.
React useEffect anti-pattern detection and correction guide. Use this skill whenever writing, reviewing, or modifying any React component that contains useEffect, or when about to add a useEffect hook. Also trigger when you see patterns like "setState inside useEffect", "effect chains", "derived state in effect", or "notify parent in effect". Covers 12 specific scenarios where Effects are unnecessary or misused, with correct alternatives. Even if the useEffect looks reasonable at first glance, consult this skill to verify it's truly needed.
Use when cognee is a Python AI memory engine that transforms documents into knowledge graphs with vector and graph storage for semantic search and reasoning. Use this skill when writing code that calls cognee's Python API (add, cognify, search, memify, config, datasets, prune, session) or integrating cognee-mcp. Covers the full public API, SearchType modes, DataPoint custom models, pipeline tasks, and configuration for LLM/embedding/vector/graph providers. Do NOT use for general knowledge graph theory or unrelated Python libraries.
LoRA, full fine-tuning, DPO preference tuning, VLM training, function-calling tuning, reasoning tuning, and BYOM uploads on Together AI. Reach for it whenever the user wants to adapt a model on custom data rather than only run inference, evaluate outputs, or host an existing model.
Business logic vulnerability playbook. Use when reasoning about workflows, race conditions, price manipulation, coupon abuse, state machines, and multi-step authorization gaps.
Apply exponential smoothing methods for time series forecasting with weighted moving averages. Use this skill when the user needs simple, robust forecasts, implement Holt-Winters for seasonal data, or build lightweight forecasting without complex models — even if they say 'simple forecast', 'moving average prediction', or 'smoothing method'.
Meta skill for the EvanFlow system. Loads the shared vocabulary (deep modules, deletion test, vertical slice, grill, mockup quick-mode, no-auto-commit) and describes when to invoke each evanflow-* skill. Use when starting a new task and unsure which evanflow skill applies, or when you need to ground reasoning in the shared vocabulary.
shadcn/ui AI chat components for conversational interfaces. Use for streaming chat, tool/function displays, reasoning visualization, or encountering Next.js App Router setup, Tailwind v4 integration, AI SDK v5 migration errors.
Decompose the elements of retrieved legal provisions and conduct element-by-element subsumption: civil claim basis inspection and criminal three-tier review, output subsumption tables, conformity conclusions, and evidence gap lists. Element decomposition must be based on the original text of the provisions or judgment reasons; creating elements out of thin air or supplementing facts through speculation is prohibited.
Use this skill before any creative or constructive work (features, components, architecture, behavior changes, or functionality). This skill transforms vague ideas into validated designs through disciplined, incremental reasoning and collaboration.
4-tier autonomous self-healing system for OpenClaw Gateway with persistent learning, reasoning logs, and multi-channel alerts. Features Claude Code as Level 3 emergency doctor for AI-powered diagnosis and repair.