Loading...
Loading...
Found 1,942 Skills
Content quality and E-E-A-T assessment for AI citability — evaluate experience, expertise, authoritativeness, trustworthiness, and content structure
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.
· 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.
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.
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".
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.
Design, test, and optimize prompts for LLM interactions. Cover prompt patterns (few-shot, chain-of-thought, ReAct), system prompt design, output formatting, prompt evaluation, and prompt optimization techniques. Triggers on "write prompt", "optimize prompt", "design system prompt", "few-shot examples", "chain of thought", "prompt evaluation", "LLM output formatting", "prompt testing", or "prompt patterns".
Implement Cisco's Foundry specification for agentic AI security evaluation systems with multi-agent architecture