Loading...
Loading...
Found 1,203 Skills
This skill should be used when establishing comprehensive QA testing processes for any software project. Use when creating test strategies, writing test cases following Google Testing Standards, executing test plans, tracking bugs with P0-P4 classification, calculating quality metrics, or generating progress reports. Includes autonomous execution capability via master prompts and complete documentation templates for third-party QA team handoffs. Implements OWASP security testing and achieves 90% coverage targets.
Jeffrey Emanuel's comprehensive markdown planning methodology for software projects. The 85%+ time-on-planning approach that makes agentic coding work at scale. Includes exact prompts used.
Semantic security scanner for OpenClaw skills. Detects prompt injection, data exfiltration, and hidden instructions that traditional code scanners miss. Use when user asks to scan skills, check skill safety, or run a security audit.
Ralph Wiggum persistence loop with intelligent multi-model routing (Gemini, Codex, Claude, Council)
Build AI-powered Ruby applications with RubyLLM. Full lifecycle - chat, tools, streaming, Rails integration, embeddings, and production deployment. Covers all providers (OpenAI, Anthropic, Gemini, etc.) with one unified API.
Chain-of-Verification (CoVe) prompting system. Converts lazy prompts into rigorous 4-stage verified output. Use for any code generation, debugging, or implementation task. Automatically invoked by wavybaby for medium/high complexity tasks. Reduces hallucinations and catches subtle bugs.
Audit and refactor existing SKILLs. Use when improving drafts, fixing quality, or aligning to spec. For creating new skills from scratch use skill-creator (anthropics/skills).
This skill should be used when the user asks to "integrate DSPy with Haystack", "optimize Haystack prompts using DSPy", "use DSPy to improve Haystack pipeline", mentions "Haystack pipeline optimization", "combining DSPy and Haystack", "extract DSPy prompt for Haystack", or wants to use DSPy's optimization capabilities to automatically improve prompts in existing Haystack pipelines.
Refine, parallelize, and verify a draft task specification into a fully planned implementation-ready task
Compress documentation, prompts, and context into minimal tokens for AGENTS.md and CLAUDE.md. Achieves 80%+ token reduction while preserving agent accuracy.
Agentica server + Claude proxy setup - architecture, startup sequence, debugging
Analyzes an MLflow session — a sequence of traces from a multi-turn chat conversation or interaction. Use when the user asks to debug a chat conversation, review session or chat history, find where a multi-turn chat went wrong, or analyze patterns across turns. Triggers on "analyze this session", "what happened in this conversation", "debug session", "review chat history", "where did this chat go wrong", "session traces", "analyze chat", "debug this chat".