web-artifacts-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).

Quantitative framework for detecting AI-generated "slop" in prose through systematic analysis of structural, lexical, rhetorical, and logical patterns. Use when analyzing text authenticity, evaluating writing quality, detecting AI-generated content, or assessing whether prose has characteristic AI patterns like excessive parallelism, abstraction laddering, chiasmus abuse, platitudes, tautologies, or rhetorical overengineering.

Transform raw, unorganized information into actionable knowledge through systematic validation. Use when users want to synthesize information from multiple sources (documents, URLs, transcripts, notes) into structured knowledge documents. Supports three document types - curated context (default, optimized for recall), guidance (implementation-focused narrative), and reference (quick lookup). Combines convergent synthesis with tension preservation to maintain productive contradictions. Triggers on requests like "synthesize this information", "create knowledge document from these sources", "transform these notes into actionable guidance", or "help me organize this research".

Download and transcribe YouTube videos into clean, deduplicated Markdown documents with chapter headings. Wraps yt-dlp to fetch subtitles (manual or auto-generated), removes the rolling-text triplication artifacts from auto-subs, inserts chapter markers from video metadata, and produces both a timestamped transcript and a prose-only version. Use when the user wants to: (1) transcribe a YouTube video, (2) get a transcript or subtitles from YouTube, (3) create an InfoNugget from a video, (4) extract text from a YouTube URL or video ID, or (5) mentions yt-dlp, YouTube transcript, or video subtitles.

Transform source materials into K-DAGs (Knowledge DAGs) — modular, curated domain knowledge structured as directed acyclic graphs with typed edges, mermaid visualization, and prose context. Use when users want to build knowledge graphs from documents, synthesize multiple sources into structured ontologies, intersect existing K-DAGs to discover emergent relationships, or create machine-readable knowledge structures that resist context rot. Triggers on: 'build a knowledge graph', 'create a K-DAG', 'intersect these knowledge sources', 'map the relationships between these documents', 'synthesize an ontology from these sources'.

This skill should be used when the user asks to "add resiliency to a skill", "make this skill more robust", "improve error handling", "add validation mechanisms", "create self-correcting behavior", or discusses determinism, robustness, error correction, or homeostatic patterns in Agent Skills. Applies biological resiliency principles from Michael Levin's work to Agent Skill design.

Transform rough ideas into detailed design documents with implementation plans. Use when a user wants to develop an idea into a complete specification, create a design document from a concept, plan a feature implementation, or mentions "PDD", "prompt-driven development", "idea to design", "design doc from idea", or wants to systematically refine requirements before building. Guides through requirements clarification, research, detailed design, and implementation planning.

Analyze a codebase and generate comprehensive documentation including architecture, components, interfaces, workflows, and dependencies. Creates an AI-optimized knowledge base (index.md) and can consolidate into AGENTS.md, README.md, or CONTRIBUTING.md. Use when the user wants to document a codebase, create AGENTS.md, understand system architecture, generate developer documentation, or asks to "summarize the codebase".

This skill should be used when the user wants to "create a skill", "add a skill to plugin", "write a new skill", "improve skill description", "organize skill content", or needs guidance on skill structure, progressive disclosure, or skill development best practices for Claude Code plugins.

This skill should be used when the user asks to "create a plugin", "scaffold a plugin", "understand plugin structure", "organize plugin components", "set up plugin.json", "use ${CLAUDE_PLUGIN_ROOT}", "add commands/agents/skills/hooks", "configure auto-discovery", or needs guidance on plugin directory layout, manifest configuration, component organization, file naming conventions, or Claude Code plugin architecture best practices.

This skill should be used when the user asks about "plugin settings", "store plugin configuration", "user-configurable plugin", ".local.md files", "plugin state files", "read YAML frontmatter", "per-project plugin settings", or wants to make plugin behavior configurable. Documents the .claude/plugin-name.local.md pattern for storing plugin-specific configuration with YAML frontmatter and markdown content.

This skill should be used when the user asks to "add MCP server", "integrate MCP", "configure MCP in plugin", "use .mcp.json", "set up Model Context Protocol", "connect external service", mentions "${CLAUDE_PLUGIN_ROOT} with MCP", or discusses MCP server types (SSE, stdio, HTTP, WebSocket). Provides comprehensive guidance for integrating Model Context Protocol servers into Claude Code plugins for external tool and service integration.