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Agent skills

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

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multi-source-synthesis Combines multiple summaries into coherent synthesis. Activates when user requests combine these summaries, synthesize results, merge findings, or multi-source synthesis. This is the reduce step after individual sources have been summarized. Enforces deduplication, conflict surfacing, confidence scoring across sources, and attribution to all sources.
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image-summarization Describe images and screenshots by viewing content with multimodal Read tool and documenting only visible elements. Activates on what's in this image, describe this screenshot, summarize this diagram, image summary, what does this screenshot show, explain this diagram, break down this chart. Handles UI screenshots, architecture diagrams, charts, photos, code screenshots, and terminal output. Never infers from filenames.
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url-summarization Summarize web content by fetching URLs, extracting key passages with quote-grounding, and producing structured output. Activates on summarize this URL, what does this page say, summarize this article, read and summarize, summarize the documentation at, tl;dr this link, give me the highlights of this page, what's important on this site. Routes to fetching strategy based on content type — documentation, articles, API references, READMEs. Reports partial accessibility explicitly.
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summarizer Route summarization requests to the correct methodology and enforce fidelity rules. Activates on summarize, tl;dr, give me the highlights, what's important in this, break down this, what does this code do, explain this file, describe this image, read and summarize. Routes files, URLs, images, and multi-source content to type-specific strategies. Enforces anti-hallucination rules — read before summarizing, extract before abstracting, preserve counts, distinguish absence from nonexistence, prevent lossy re-summarization chains.
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scientific-thinking Use when facing unknowns, debugging without a clear cause, or making architecture decisions — enforces hypothesis-driven scientific reasoning through observation, hypothesis formulation, prediction, experiment design, and evidence-based conclusion. Use when previous attempts have failed or the problem space involves uncertainty.
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evidence-first-debugging Use when debugging software, investigating incidents, diagnosing flaky tests, or analyzing performance regressions — enforces structured observation recording with evidence IDs, causality validation, and verification gates to prevent correlation-causation pollution. Use when an agent might otherwise summarize or speculate instead of reporting observed evidence.
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experiment-protocol Design and run controlled experiments using the experiment-registry MCP server — domain-agnostic, pluggable, mechanically enforced. Use when you need evidence that a change actually improves behaviour.
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example-skill Demonstrates all available skill frontmatter fields. Use when you need a reference for skill configuration, when learning about skill capabilities, or when creating new skills from scratch.
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write-frontmatter-description Write or rewrite frontmatter description fields for Claude Code skills and agents. Use when creating new skills/agents, description exceeds 1024 characters, description uses forbidden YAML multiline indicators (>-, |-), description contains colons that trigger quoting, description lacks trigger keywords, or when optimizing descriptions for AI tool selection. Ensures descriptions are single-line, complete, informative, third-person, front-loaded with trigger conditions.
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start-refactor-task Start or complete a specific refactoring task from a task file. Use when a sub-agent needs to pick up a refactoring task, update its status, implement acceptance criteria, and run verification steps.
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subagent-refactoring-methodology Analysis criteria, transformation patterns, output format, and validation checklist for refactoring Claude Code agent prompt files. Load this skill when preparing to run the subagent-refactorer agent or when reviewing agent prompt files for structural, model optimization, or instruction quality improvements.
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audit-agent-lifecycle Audit agent lifecycle — validates agent execution capability against configuration. Accepts plugin path, runs 8 semantic audits (capability vs config alignment, skill loading correctness, inter-agent contracts, prompt contradictions, tool sufficiency, dead agents, scriptable patterns, pattern learning), writes reports to .claude/audits/. Use when auditing agent lifecycle, checking agent capabilities, verifying tool access, finding dead agents, validating agent contract alignment, or confirming agents can execute workflows.
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hooks-guide Cross-platform hooks reference for AI coding assistants — Claude Code, GitHub Copilot, Cursor, Windsurf, Amp. Covers hook authoring in Node.js CJS and Python, per-platform event schemas, inline-agent hooks and MCP in agent frontmatter, common JSON I/O, exit codes, best practices, and a fetch script to refresh docs from official sources. Use when writing, reviewing, or debugging hooks for any AI assistant.
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refactor-skill Assess and refactor oversized or multi-domain skills. First determines whether splitting or references/ extraction is appropriate — then executes the correct action. Use when a skill exceeds token thresholds (SK006/SK007) or covers multiple independent domains. Performs candidate assessment before any structural changes; cohesive single-intent skills are redirected to references/ extraction instead of splitting. When splitting is warranted — domain analysis gate, split plan, new SKILL.md generation, validation, and backwards-compatible facade conversion.
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mcp-integration Integrate MCP servers into Claude Code plugins — covers .mcp.json configuration, plugin.json mcpServers field, server types (stdio, SSE, HTTP, WebSocket), environment variable expansion, tool naming conventions, OAuth and token authentication, security best practices, and testing workflows. Use when adding an MCP server to a plugin, configuring MCP authentication, debugging MCP tool discovery, setting up Model Context Protocol integration, or choosing between stdio and SSE transport types.
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hooks-core-reference Hook system fundamentals — all events, configuration structure, matchers per event type, environment variables, execution behavior, security, and debugging. Use when creating hooks, understanding hook events, matchers, configuration locations, environment variables, or troubleshooting hook issues.
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lint Use when checking skill quality, validating frontmatter before commit, or diagnosing validator warnings. Runs the plugin validator on a skill, agent, or plugin directory — reports token complexity, broken links, frontmatter issues, and structural problems. Pass the path as an argument.
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feature-discovery Autonomous feature research and gap analysis. Use when starting /add-new-feature or analyzing existing architecture documents. Explores codebase patterns, identifies ambiguities, and produces feature-context-{slug}.md for orchestrator RT-ICA phase. Does NOT make technical decisions.
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audit-skill-completeness Evaluate a single skill's quality against 8 completeness categories derived from Anthropic's official skills repository. Scores preparation, progression, verification, scripts, examples, anti-patterns, references, and assets. Generates scored report to .claude/audits/. Use when auditing skill quality, checking marketplace readiness, evaluating skill completeness score, performing pre-publication evaluation, or comparing to Anthropic skills.
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claude-plugins-reference-2026 Complete reference for Claude Code plugins system (January 2026). Use when creating plugins, understanding plugin.json schema, marketplace configuration, bundling skills/commands/agents/hooks/MCP/LSP servers, plugin caching, validation, or distribution. Covers plugin components, directory structure, installation scopes, environment variables, CLI commands, debugging, and enterprise features.
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component-patterns Decide which plugin component type to use and how to organize components at scale. Covers the component lifecycle (discovery and activation phases), decision framework for choosing between commands, skills, agents, hooks, and MCP servers, and organization patterns for each component type. Use when asking "which component type should I use", "command vs skill vs agent", "when to use a hook vs MCP server", "component lifecycle", "how to organize plugin components", "plugin structure patterns", or "scale a plugin with many components".
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ensure-complete Use when refactoring is complete and needs validation. Performs holistic review of completed plugin refactoring, validates improvements against original assessment score, checks for documentation drift, and creates follow-up task files if issues remain
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agent-capability-analyzer Runs the description-drift experiment — spawns all Claude Code agents simultaneously to collect self-reported capabilities, then compares them against static frontmatter descriptions to reveal how reliable orchestrator routing based on descriptions actually is. Use when measuring description drift across the agent fleet, re-running the capability collection experiment, analyzing a specific agent's self-reported capabilities, or auditing whether frontmatter descriptions accurately reflect agent behavior.
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command-development Create and configure slash commands for Claude Code — the legacy .claude/commands/ format. Use when asked to "create a slash command", "add a command", "write a custom command", "define command arguments", "use command frontmatter", "organize commands", "create command with file references", "interactive command", "use AskUserQuestion in command", or for guidance on slash command structure, YAML frontmatter fields, dynamic arguments, bash execution in commands, user interaction patterns, or command development best practices. Commands are the legacy format superseded by skills — for new development prefer /plugin-creator:skill-creator
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