Agent skills

Skills you can use with AI coding agents, indexed from public GitHub repositories.

Latest Most Popular

descriptive-action Use when the user asks to describe, summarize, analyze, compare, explain, or report on something (text, data, events, systems) without asking for recommendations or next steps.
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reasoning-analogical Transfer knowledge from source domains to novel target situations through structural mapping. Use when facing new markets, products, or situations where past experience provides relevant patterns. Produces adapted solutions with explicit mappings and context adjustments.
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reasoning-inductive Extract patterns and generalizations from multiple observations. Use when detecting recurring themes, building predictive rules, or identifying systemic behaviors from accumulated data. Produces validated patterns with confidence bounds and exception handling.
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foundations-problem-solution-fit Problem validation and solution design. Use when discovering customer problems, generating solution hypotheses, or defining MVP scope.
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reasoning-counterfactual Evaluate alternative scenarios by simulating interventions on past decisions or hypothetical futures. Use when assessing decisions in hindsight, planning scenarios, or comparing paths not taken. Produces comparative analysis with probability-weighted outcomes.
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reasoning-causal Execute evidence-based decision-making through 6-stage causal flow. Use for known processes, operational execution, and decisions with clear cause-effect chains.
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decision-action Use when the user needs to choose, prioritize, or make a go/no-go decision among options.
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goal-setter Transform objectives into structured goals with plans. Use when user declares intent ("I want to...", "Goal is to...", "Achieve X by Y"). Decomposes into subgoals, milestones, success criteria.
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planning-action Use when the user asks for a roadmap, timeline, phased plan, or execution sequence.
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diagnostic-action Use when the user asks why something happened, what’s wrong, root cause analysis, debugging, or incident explanation.
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extract-from-pdfs This skill should be used when extracting structured data from scientific PDFs for systematic reviews, meta-analyses, or database creation. Use when working with collections of research papers that need to be converted into analyzable datasets with validation metrics.
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biogeobears Set up and execute phylogenetic biogeographic analyses using BioGeoBEARS in R. Use when users request biogeographic reconstruction, ancestral range estimation, or want to analyze species distributions on phylogenies. Handles input file validation, data reformatting, RMarkdown workflow generation, and result visualization.
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thinking-deeply Engages structured analysis to explore multiple perspectives and context dependencies before responding. Use when users ask confirmation-seeking questions, make leading statements, request binary choices, or when feeling inclined to quickly agree or disagree without thorough consideration.
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busco-phylogeny Generate phylogenies from genome assemblies using BUSCO/compleasm-based single-copy orthologs with scheduler-aware workflow generation
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scribe-mcp-usage Operate the local Scribe MCP for any ~/projects/* repo; use when registering the server, setting projects, drafting ARCH/PHASE/CHECKLIST via manage_docs, or logging work with append_entry/get_project safeguards.
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visual-asset-workflow Generate distinctive educational visuals using creative brief methodology. Use when creating chapter illustrations, diagrams, or teaching visuals with Gemini.
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concept-scaffolding Design progressive concept scaffolding with cognitive load management. Use when breaking down complex concepts into learnable steps for educational content.
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nx-monorepo Nx monorepo management skill for AI-native development. This skill should be used when working with Nx workspaces, project graphs, affected detection, code generation, and caching. Use when: analyzing dependencies, running affected commands, generating code, configuring Nx Cloud, or optimizing build performance. Invoke nx-mcp tools for documentation queries.
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book-scaffolding Plan, structure, and scaffold large educational books using cognitive load management, just-in-time specification, and pedagogical best practices. Use this skill when planning multi-part, multi-chapter educational works that require narrative continuity, progressive complexity, and hands-on exercises. This skill helps create cohesive learning journeys that balance foundational scaffolding with advanced independence. Activate when tasks involve structuring books, managing cognitive load across chapters, defining part-level narratives, or coordinating multi-phase content development workflows.
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session-intelligence-harvester This skill should be used after productive sessions to extract learnings and route them to appropriate Reusable Intelligence Infrastructure (RII) components. Use when corrections were made, format drift was fixed, new patterns emerged, or the user explicitly asks to "harvest learnings" or "capture session intelligence". Transforms one-time fixes into permanent organizational knowledge by implementing updates across multiple files.
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tool-selection-framework Design systematic decision frameworks for selecting appropriate AI tools (Claude Code vs Gemini CLI) based on context requirements, codebase size, reasoning depth needs, and task complexity. Use this skill when starting projects with unclear tool requirements, optimizing context-constrained workflows, or designing multi-phase strategies that leverage multiple tools' strengths. This skill helps match tool capabilities to task characteristics, preventing wasted context and ensuring optimal resource allocation.
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quiz-generator Generate 50-question interactive quizzes using the Quiz component with randomized batching. Use when creating end-of-chapter assessments. Displays 15-20 questions per session with immediate feedback. NOT for static markdown quizzes.
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mcp-builder Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
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exercise-designer Designs deliberate practice exercises applying evidence-based learning strategies like retrieval practice, spaced repetition, and interleaving. Activate when educators need varied exercise types (fill-in-blank, debug-this, build-from-scratch, extend-code, AI-collaborative) targeting learning objectives with appropriate difficulty progression. Creates exercise sets that apply cognitive science principles to maximize retention and skill development. Use when designing practice activities for Python concepts, creating homework assignments, generating problem sets, or evaluating exercise quality.
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