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Found 1,948 Skills
Evaluate text completeness based on criteria.
This skill should be used when the user asks to review, proofread, check, or evaluate content. It provides comprehensive text review (grammar, logic, compliance) and version evaluation (A/B testing, comparison analysis). Text review automatically adds AI disclaimer at the end.
Advisory fee billing: fee calculation (tiered, flat, breakpoint), billing cycles, AUM valuation, direct-debit/invoice collection, GAAP revenue recognition, ADV Part 2A/Reg BI/ERISA 408(b)(2) disclosure, billing exceptions, system migration, revenue forecasting, concentration analysis.
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.
Designs production-grade RAG pipelines with chunking optimization, retrieval evaluation, and pipeline architecture. Use when building a RAG system, selecting a chunking strategy, choosing a vector database, optimizing retrieval quality, designing embedding pipelines, or evaluating RAG performance with RAGAS metrics.
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.
Command-line interface for CloudAnalyzer — Agent-friendly harness for CloudAnalyzer, a QA platform for mapping, localization, and perception outputs. Supports 27 commands across 8 groups: point cloud evaluation, trajectory evaluation, ground segmentation QA, config-driven quality gates, baseline evolution, processing, visualization, and interactive REPL.