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Found 1,747 Skills
Conduct comprehensive literature research with target disambiguation, evidence grading, and structured theme extraction. Creates a detailed report with mandatory completeness checklist, biological model synthesis, and testable hypotheses. For biological targets, resolves official IDs (Ensembl/UniProt), synonyms, naming collisions, and gathers expression/pathway context before literature search. Default deliverable is a report file; for single factoid questions, uses a fast verification mode and may include an inline answer. Use when users need thorough literature reviews, target profiles, or to verify specific claims from the literature.
Expert in streamlining and enhancing the development of AI Agent Applications, including AI app / agent / workflow code generation, AI model comparison and recommendation, tracing setup, and evaluation planning / setup / execution.
System architecture and technical design specialist. 🚨 TIER 2 SKILL - ON-DEMAND ACTIVATION 🚨 Use when user requests involve: - System architecture design and planning - Technical specifications and ADRs - Technology evaluation and selection - Scalability and performance planning - Integration architecture and API design - English: "design system", "architecture", "ADR", "tech stack", "scalability" - Swedish: "arkitektur", "systemdesign", "teknikval", "skalbarhet" Architecture Specialist (British female voice) provides: - System design and architecture patterns - Architecture Decision Records (ADRs) - Technology evaluation and trade-off analysis - Cloud and microservices architecture - Integration patterns and API design User confirmation optional but recommended for major architectural decisions.
Evaluate output, identify lessons, decide accept/rework. Use after implementation.
Master dispatcher for all MLflow workflows. Use this skill when the user wants to do anything with MLflow — tracing, evaluating, debugging, or improving an agent. Routes to the right MLflow sub-skill automatically. Triggers on: "use mlflow", "help with mlflow", "mlflow agent", "add mlflow to my project", "trace my agent", "evaluate my agent", or any MLflow task without a specific skill in mind.
Create and run orq.ai experiments — compare configurations against datasets using evaluators, analyze results, and generate prioritized action plans. Use when evaluating LLM agents, deployments, conversations, or RAG pipelines end-to-end. Do NOT use without a dataset and evaluators. Do NOT use for cross-framework comparisons with external agents (use compare-agents).
Formal evaluation framework for Claude Code sessions implementing eval-driven development (EDD) principles
Structured decision-making frameworks for evaluating options and making informed choices. Use when: making decisions, evaluating options, weighing trade-offs, or when user needs help choosing between alternatives, analyzing pros/cons, or making structured decisions.
Audit npm, pip, and Go dependencies that OpenClaw skills try to install. Checks for known vulnerabilities, typosquatting, and malicious packages.
Designs retrieval-augmented generation pipelines for document-based AI assistants. Includes chunking strategies, metadata schemas, retrieval algorithms, reranking, and evaluation plans. Use when building "RAG systems", "document search", "semantic search", or "knowledge bases".
Best practices for scikit-learn machine learning, model development, evaluation, and deployment in Python
Source and evaluate candidates from LinkedIn using the linkedin_scraper Python library. Use when the user wants to (1) scrape LinkedIn profiles for candidate data, (2) evaluate candidates against a job description, (3) generate boolean search strings for sourcing, (4) produce candidate scorecards, summaries, or comparison tables, or (5) any recruiting/talent-sourcing task involving LinkedIn data.