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Found 1,904 Skills
Use when creating or improving golden datasets for AI evaluation. Defines quality criteria, curation workflows, and multi-agent analysis patterns for test data.
Write high-performance JIT compilers and interpreters in the style of Mike Pall, creator of LuaJIT. Emphasizes trace-based compilation, aggressive optimization, and understanding CPU microarchitecture. Use when building JITs, interpreters, or any code where every cycle counts.
Evaluate text completeness based on criteria.
Automatically collect hot topics in the AI field or complete AI technical article writing in the writing style of 'Second Brother' according to specified topics. It focuses on actual tests of AI Coding tools (Claude Code, Qoder, Cursor, TRAE, etc.), engineering implementation of large models (SpringAI, LangChain, RAG, etc.), AI Agent and workflow orchestration, evaluation of domestic large models (GLM, Tongyi Qianwen, DeepSeek, MiniMax, Kimi, etc.), and evaluation of various AI tools and Agent tools. Trigger keywords: write an AI article, AI technical article, large model evaluation, AI tool actual test, GLM, Claude Code, Qoder, Cursor, TRAE, SpringAI, RAG, Agent, workflow, domestic large model, collect AI hot topics, AI topic, etc.
Benchmark compensation against market data. Trigger with "what should we pay", "comp benchmark", "market rate for", "salary range for", "is this offer competitive", or when the user needs help evaluating or setting compensation levels.
Model free cash flow to evaluate project or business value. Use for investment decisions, valuation, and understanding cash dynamics.
A/B test agent variants measuring quality and total session token cost across simple and complex benchmarks. Use when creating compact agent versions, validating agent changes, comparing internal vs external agents, or deciding between variants for production. Use for "compare agents", "A/B test", "benchmark agents", or "test agent efficiency". Do NOT use for evaluating single agents, testing skills, or optimizing prompts without variant comparison.
Evaluate and manage suppliers using weighted scorecards across quality, delivery, price, and service dimensions. Use this skill when the user needs to assess supplier performance, compare vendors for selection, design a supplier rating system, or manage supplier development — even if they say 'which supplier should we choose', 'rate our vendors', 'this supplier keeps delivering late', or 'build a vendor evaluation system'.
Objective task quality evaluation framework using quantitative KPIs. KPIs are automatically calculated by a hook when task files are modified and saved to TASK-XXX--kpi.json. Use when: reading KPI data for task evaluation, understanding quality metrics, deciding whether to iterate or approve based on data.
Evaluate the quality of CAW (Cobo Agentic Wallet) Agent in local Claude Code, and generate scoring data and analysis reports. Use when: Users want to run CAW evaluation, conduct evaluation, test Skill, assess Agent quality, generate evaluation reports, or say "run evaluation", "evaluate CAW", "eval", "score". For weak model / openclaw evaluation, please use caw-eval-openclaw (only installed on openclaw servers).
Analyzes and compares existing skills from any source (skills.sh, GitHub, Claude marketplace, or local files) against a target skill or requirement. Fetches skill content, evaluates it across 10 dimensions, produces a structured comparison table, identifies gaps, and recommends whether to adopt, adapt, or build from scratch. Trigger when: analyze this skill, compare skills, is this skill good enough, what does this skill do, skill evaluation, should I use this skill, skill gap analysis, paste a skills.sh URL, GitHub skill URL, or upload a SKILL.md file for review.
Provides guidance for automatically evolving and optimizing AI agents across any domain using LLM-driven evolution algorithms. Use when building self-improving agents, optimizing agent prompts and skills against benchmarks, or implementing automated agent evaluation loops.