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Found 1,900 Skills
Production-ready financial analyst skill with ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction. 4 Python tools (all stdlib-only). Works with Claude Code, Codex CLI, and OpenClaw.
Content quality and E-E-A-T assessment for AI citability — evaluate experience, expertise, authoritativeness, trustworthiness, and content structure
· Batch-improve skill collections with evaluation loops, lint checks, behavioral tests, peer review. Triggers: 'skill refiner', 'improve skills', 'quality sweep', 'batch improve', 'skill loop'. Not for one skill.
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, update or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or iterate on skill quality. Triggers: "create a skill", "make a new skill", "build a skill for", "write a skill that", "skill for doing X", "I want a skill to", "new skill", "design a skill", "scaffold a skill", "improve this skill", "optimize this skill", "this skill isn't working well", "evaluate this skill", "score this skill", "how good is this skill", "run evals on", "benchmark this skill", "test this skill's quality", "skill quality", "skill performance". Also triggers when a user describes a repeatable workflow they want to automate, says "I keep doing X manually", "can you remember how to do X", or "turn this into a skill".
Run cross-framework agent comparisons using evaluatorq from orqkit — compares any combination of agents (orq.ai, LangGraph, CrewAI, OpenAI Agents SDK, Vercel AI SDK) head-to-head on the same dataset with LLM-as-a-judge scoring. Use when comparing agents, benchmarking, or wanting side-by-side evaluation. Do NOT use when comparing only orq.ai configurations with no external agents (use run-experiment instead).
Research and discovery workflow for document deliverables — competitive analyses, architecture comparisons, ADR scaffolding, literature reviews, vendor evaluations. No TDD requirement. Phases: gathering → synthesizing → completed. Triggers: 'discover', 'research', 'explore topic', or /discover.
Sector-rotation snapshot across A-share, HK, and US markets — point-in-time multi-factor scoring of momentum, capital flow, and valuation to rank sectors by current cycle strength. For ongoing 6–12 month cycle positioning and allocation recommendations use longbridge-sector-monitor. Triggers: "行业轮动", "板块轮动", "行业动量排名", "强势板块", "弱势板块", "行业资金流", "板块涨幅榜", "行業輪動", "板塊輪動", "行業動量排名", "強勢板塊", "弱勢板塊", "行業資金流", "板塊漲幅榜", "sector rotation", "sector momentum ranking", "leading sector", "lagging sector", "sector capital flow", "sector strength ranking".
Design failing tests for complex features using Independent Evaluation — dispatches a context-free agent that sees only the requirement spec and code paths (not the implementation approach), then returns executable failing tests. Use when starting TDD for a non-trivial feature, when the requirement is ambiguous enough that biased tests are a risk, or when the user asks for independent test design.
Comprehensive testing doctrine for software and AI systems — covers positive patterns, anti-patterns, gates for coding agents writing tests, CI discipline, and an LLM/agent evaluation primer. Use when authoring or reviewing tests, adding mocks, deciding test placement, generating tests via agents, debugging flaky CI, designing eval suites for LLM features, or rebuilding a brittle test suite. Contains 12 positive patterns (selector hierarchy, table-driven, builders, real-system gates), 25 anti-patterns across Brittleness, Flakiness, Mock-misuse, Process, and AI-specific families, 7 mandatory gates for agents writing tests, flaky-test taxonomy with quarantine workflow, contract / property / mutation testing patterns, and an oracle-ladder primer for LLM-as-judge and agent eval. Language-agnostic — pseudo-code only. Don't use for general code review, library-specific debugging unrelated to tests, non-testing CI pipeline design, or production observability.
Code review requires technical evaluation, not emotional performance.
Implement Cisco's Foundry specification for agentic AI security evaluation systems with multi-agent architecture
This skill should be used when the user asks to "optimize prompts", "design prompt templates", "evaluate LLM outputs", "build agentic systems", "implement RAG", "create few-shot examples", "analyze token usage", or "design AI workflows". Use for prompt engineering patterns, LLM evaluation frameworks, agent architectures, and structured output design.