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Found 1,905 Skills
Iterative code refinement through plan → code → evaluate → refine cycles. Runs lint checks (ruff), tests (pytest), and structured self-evaluation each cycle, then diagnoses failures and refines. Decomposes complex tasks into sequential phases, iterates up to 3 times per phase (10 total). Use when: the main agent delegates a code task with 'MODE: MORE_EFFORT', the user selects 'More Effort' code generation mode, or the task explicitly requests iterative refinement for higher code quality. Do NOT use for single-pass code generation (Lite mode), experiment pipeline orchestration (use experiment-pipeline), or diagnosing a specific experiment failure (use experiment-craft).
Evaluates Claude Agent Skills on 10 quality axes with letter grades (A+ through F) and specific improvement recommendations. Use when auditing a skill, comparing skills, prioritizing improvements, or performing quality control on a skill library. Activate on "grade skill", "evaluate skill", "skill quality", "skill audit", "skill review", "rate skill". NOT for creating skills (use skill-architect), grading code quality, or evaluating non-skill documents.
Optional Stage 0 of the feature workflow — clarify vague ideas through dialogue until they are ready to enter the design phase. The role of AI is a thinking partner: dig out the real problem the user wants to solve (instead of sticking to the first solution they blurt out), actively evaluate the solution when the user brings it up, and propose better alternatives if necessary. After the discussion, output {slug}-brainstorm.md to document the results. Trigger scenarios: The user says "I have an unclear idea", "Let's brainstorm first", "The feature direction is still undecided", or the user brings a specific solution but wants to hear other ideas first. Skip this stage and proceed directly to design if the idea is already clear and the user does not want to discuss the solution further. This stage also does not handle bugs and refactoring.
A method for iteratively improving text instructions for agents (skills / slash commands / task prompts / CLAUDE.md sections / code generation prompts) by having unbiased executors run them, then evaluating from both perspectives (executor self-report + instruction-side metrics). Repeat until improvement plateaus. Use immediately after creating or significantly revising a prompt or skill, or when you suspect the reason an agent isn't behaving as expected is due to ambiguity in the instructions.
UI design and review should apply Nielsen's 10 Usability Heuristics — the foundational principles for evaluating and improving usability. Use when auditing an interface, designing interaction flows, writing error messages, or reviewing any UI for usability issues.
Patterns for DeFi market analysis, screening, and comparison using DefiLlama MCP tools. Covers valuation ratios (P/S, P/F), growth screening with pct_change columns, multi-metric protocol comparison, category comparison, and cross-entity analysis. Use when users ask to compare protocols, screen for undervalued projects, analyze growth trends, or do sector analysis.
Real-time quotes, static reference, and valuation indices for stocks listed in HK / US / A-share / Singapore via Longbridge Securities. Returns last price, change, volume, turnover, market cap, industry, PE/PB, turnover-rate, and other indicators. Triggers: "现在多少钱", "股价", "涨跌幅", "成交量", "市值", "市盈率", "PE", "PB", "换手率", "行业", "現在多少", "股價", "成交量", "市值", "市盈率", "stock price", "current price", "quote", "market cap", "PE ratio", "valuation", "NVDA price", "AAPL quote", "茅台市值", "腾讯股价", "700.HK", "600519.SH".
Tech hype vs. fundamentals analysis via Longbridge — identifies valuation bubbles and fundamental disconnects in A-share / HK tech stocks. Compares PE / PS / EV-EBITDA historical percentile against actual revenue / profit growth. Analyses which AI / EV / semiconductor theme plays have fundamental support vs. pure sentiment-driven momentum. Triggers: "科技炒作", "AI泡沫", "估值泡沫", "科技估值", "概念股", "主题炒作", "基本面背离", "炒作识别", "科技泡沫", "科技炒作", "AI泡沫", "估值泡沫", "科技估值", "概念股", "主題炒作", "基本面背離", "tech hype", "AI bubble", "valuation bubble", "tech valuation", "theme stocks", "hype vs fundamentals", "concept stocks", "narrative vs reality", "AI concept", "semiconductor bubble".
Select and configure evaluation metrics for an AI agent. Guides through metric selection using use-case recommendations, custom LLM-based metric creation with prompt engineering, and agent default attachment. Use when user says "set up metrics", "configure metrics", "create a metric", "what metrics should I use", "add evaluation criteria", or "customize scoring".
Structure and debug a LÖVE (Love2D) game in Lua: the love.load/update/draw loop, delta-time movement, input, and screen states. Use when building a LÖVE 11.x game (main.lua, conf.lua, .love).
Persist player data in Roblox with DataStoreService: GetDataStore, GetAsync/ SetAsync/UpdateAsync/IncrementAsync wrapped in pcall, load-on-join and save-on-leave plus BindToClose, retries, and OrderedDataStore leaderboards. Use when saving or loading persistent data in a Roblox experience — when the user mentions DataStore, DataStoreService, GetAsync, SetAsync, UpdateAsync, save player data, or leaderboards. For general Luau scripting use roblox-luau.
Take your AI agent to the next level with full LangWatch integration. Adds tracing, prompt versioning, evaluation experiments, and simulation tests in one go. Use when the user wants comprehensive observability, testing, and prompt management for their agent.