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Found 9,208 Skills
Detect, classify, and QC viral contigs.
Comprehensive systems biology and pathway analysis using multiple pathway databases (Reactome, KEGG, WikiPathways, Pathway Commons, BioModels). Performs pathway enrichment, protein-pathway mapping, keyword searches, and systems-level analysis. Use when analyzing gene sets, exploring biological pathways, or investigating systems-level biology.
V2 instinct-based observational learning. Analyzes sessions to extract reusable mobile development patterns across time.
Ingest, QC, and map reads with reproducible outputs. Use for raw read processing and coverage stats.
Displays shinkoku's current capabilities, supported personas, and known limitations. Use when the user asks "what can you do?", "what's supported?", or similar questions.
Automate Bitbucket repositories, pull requests, branches, issues, and workspace management via Rube MCP (Composio). Always search tools first for current schemas.
Explore Dart's bitwise operations for both integers and booleans, including AND, OR (inclusive & exclusive), NAND, NOR, and XNOR, with practical code examples.
Use when design, prototyping or referencing Figma files. Provides capabilities for inspecting design elements, extracting assets, generating code from designs, and managing Code Connect mappings via the figma-desktop MCP server.
Guidelines for structured logging, distributed tracing, and debugging patterns across languages. Covers logging best practices, observability, security considerations, and performance analysis.
Optimize programmatic SEO pages for visibility and citation in AI-generated answers from ChatGPT, Perplexity, Google AI Overviews, and other LLM-powered search. Use when optimizing for LLM citation, implementing llms.txt, configuring AI crawler access, structuring content for AI extraction, or when the user asks about generative engine optimization (GEO), AI search visibility, or getting cited by AI.
Open-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems with real-time insights.
Single Responsibility Principle, ensuring that code files, functions, and modules have clear and single responsibilities. Applicable to all code files.