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Found 1,943 Skills
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.
Investment proposal generation via Longbridge Securities — produces a structured investment memo covering: executive summary, company overview, investment thesis (3–5 core points), financial analysis, valuation, catalysts and timeline, risk factors, and position recommendation. Triggers: "投资提案", "投资建议书", "投资报告", "投资摘要", "核心逻辑", "投资理由", "建仓建议", "投資提案", "投資建議書", "投資報告", "投資摘要", "核心邏輯", "建倉建議", "investment proposal", "investment memo", "investment summary", "investment rationale", "position recommendation", "investment case", "buy memo".
Evaluate Expo skills in this repo end-to-end - trigger accuracy, generated code quality, and runtime screenshots on iOS simulator and Android emulator via Expo Go (web optional). Use when the user wants to eval an Expo skill, test that a skill produces working code, benchmark a skill with device screenshots, or verify a skill's output renders correctly.
Help users conduct effective hiring interviews. Use when someone is designing an interview loop, crafting interview questions, evaluating candidates in real-time, or building a structured interview process.
Audit npm, pip, and Go dependencies that OpenClaw skills try to install. Checks for known vulnerabilities, typosquatting, and malicious packages.
Retrieve industry-specific P/E ratios using Octagon MCP. Use when comparing company valuations to specific industry peers, analyzing sub-sector valuations, and understanding niche market valuations beyond broad sector averages.
GoPlus AgentGuard — AI agent security guard. Automatically blocks dangerous commands, prevents data leaks, and protects secrets. Use when reviewing third-party code, auditing skills, checking for vulnerabilities, evaluating action safety, or viewing security logs.
Machine learning development patterns, model training, evaluation, and deployment. Use when building ML pipelines, training models, feature engineering, model evaluation, or deploying ML systems to production.
Design and analyze business models using the Business Model Canvas framework. Use when evaluating startups, planning new products, pivoting existing businesses, or understanding how companies create and capture value.
Evaluate skill quality against best practices. Use when asked to "rate this skill", "review skill quality", "check skill formatting", "is this skill good", "evaluate SKILL.md", "grade this skill", or when validating skill files before publishing.
Deep-dive analysis of GitHub projects. Use when the user mentions a GitHub repo/project name and wants to understand it — triggered by phrases like "help me look at this project", "learn about XXX", "how is this project", "analyze the repo", or any request to explore/evaluate a GitHub project. Covers architecture, community health, competitive landscape, and cross-platform knowledge sources.
Markets orchestration — connects ESPN live schedules with Kalshi & Polymarket prediction markets. Unified dashboards, odds comparison, entity search, and bet evaluation across platforms. Use when: user wants to see prediction market odds alongside ESPN game schedules, compare odds across platforms, search for a team/player on Kalshi or Polymarket, check for arbitrage between ESPN odds and prediction markets, or evaluate a specific game's market value. Don't use when: user wants raw prediction market data without ESPN context — use polymarket or kalshi directly. For pure odds math (conversion, de-vigging, Kelly) — use betting. For live scores without market data — use the sport-specific skill.