skilld
AgentFreeGenerate AI agent skills from npm package documentation
Capabilities8 decomposed
npm-package-documentation-to-agent-skill-conversion
Medium confidenceAutomatically extracts API signatures, function definitions, and usage patterns from npm package README and documentation files, then generates structured skill definitions compatible with AI agent frameworks. Uses LLM-powered parsing to understand package semantics and convert unstructured documentation into machine-readable skill schemas with parameter types, return values, and usage examples.
Bridges the gap between unstructured npm documentation and structured agent skill schemas by using LLM-powered semantic understanding rather than regex or AST parsing, enabling it to handle diverse documentation styles and extract contextual information about parameter constraints and usage patterns
More flexible than manual skill definition or simple regex-based extraction because it understands semantic meaning in documentation, but slower and more expensive than static analysis approaches
claude-api-skill-generation-with-structured-output
Medium confidenceLeverages Claude's API with structured output mode to generate deterministic, schema-compliant skill definitions from package documentation. Sends documentation context to Claude with a predefined JSON schema, ensuring generated skills conform to agent framework requirements without post-processing or validation overhead.
Uses Claude's structured output mode to guarantee schema compliance without post-processing, eliminating the need for validation or retry logic that other LLM-based approaches require
More reliable than unstructured LLM generation because output is guaranteed to match schema, but less flexible than approaches that support multiple LLM providers
batch-npm-package-skill-discovery-and-generation
Medium confidenceProcesses multiple npm packages in sequence or parallel, automatically fetching package metadata, documentation, and generating skills for each. Handles package resolution, documentation discovery, and skill generation with error handling and progress tracking across a package list.
Orchestrates end-to-end package discovery, documentation fetching, and skill generation in a single workflow, handling npm registry lookups and dependency resolution rather than requiring pre-curated package lists
More comprehensive than manual skill definition but less efficient than pre-built skill libraries because it generates skills on-demand rather than leveraging pre-computed definitions
documentation-parsing-and-api-extraction
Medium confidenceExtracts API signatures, function definitions, parameter types, return values, and usage examples from unstructured package documentation (README, docs files). Uses LLM-powered semantic analysis to identify callable functions, their constraints, and contextual usage patterns without requiring structured metadata or AST parsing.
Uses LLM-powered semantic understanding to extract APIs from natural language documentation rather than relying on code parsing or structured metadata, enabling it to handle diverse documentation styles and infer constraints from examples
More flexible than AST-based extraction because it understands documentation context, but less precise than static analysis of actual source code
agent-framework-skill-schema-generation
Medium confidenceGenerates skill definitions in formats compatible with specific AI agent frameworks (Claude tools, LangChain tools, etc.). Maps extracted API information to framework-specific schema requirements, including parameter validation, return type definitions, and tool metadata (descriptions, categories, tags).
Abstracts framework-specific schema requirements behind a unified generation interface, allowing the same documentation input to produce skills for different agent frameworks with appropriate schema mappings
More convenient than manual schema writing but less flexible than hand-crafted skills because it must conform to framework constraints and may miss framework-specific optimizations
skill-parameter-type-inference-and-validation
Medium confidenceInfers parameter types, constraints, and validation rules from documentation examples, function signatures, and usage patterns. Generates parameter definitions with type information (string, number, boolean, object, array) and constraints (required/optional, min/max values, enum values, regex patterns) suitable for agent tool-calling validation.
Uses LLM-powered semantic analysis to infer parameter types and constraints from documentation examples rather than requiring explicit type annotations or source code inspection, enabling type-safe skill generation from unstructured docs
More practical than manual type specification but less accurate than static type analysis of source code or TypeScript definitions
skill-description-and-metadata-generation
Medium confidenceGenerates human-readable descriptions, usage guidelines, and metadata for skills based on package documentation. Creates descriptions suitable for agent decision-making (helping LLMs understand when to use a skill) and includes examples, warnings, and contextual information extracted from documentation.
Synthesizes skill descriptions specifically optimized for agent decision-making (helping LLMs understand when to use a tool) rather than generic documentation, using semantic analysis to extract contextual usage patterns
More targeted than copying documentation directly because it generates descriptions optimized for LLM tool-calling decisions, but less comprehensive than hand-written skill documentation
cursor-ide-integration-for-skill-generation
Medium confidenceIntegrates with Cursor IDE to enable in-editor skill generation from npm packages. Allows developers to generate skills directly from Cursor's AI assistant interface, with context from the current project and dependencies. Leverages Cursor's LLM integration to streamline the skill generation workflow within the development environment.
Embeds skill generation directly into the Cursor IDE workflow, allowing developers to generate and review skills without context switching, leveraging Cursor's built-in LLM integration
More convenient than CLI-based generation for Cursor users because it integrates into the development workflow, but limited to Cursor IDE and dependent on Cursor's LLM capabilities
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
Related Artifactssharing capabilities
Artifacts that share capabilities with skilld, ranked by overlap. Discovered automatically through the match graph.
claude-skills
232+ Claude Code skills & agent plugins for Claude Code, Codex, Gemini CLI, Cursor, and 8 more coding agents — engineering, marketing, product, compliance, C-level advisory.
superpowers-zh
🦸 AI 编程超能力 · 中文增强版 — superpowers（116k+ ⭐）完整汉化 + 6 个中国原创 skills，让 Claude Code / Copilot CLI / Hermes Agent / Cursor / Windsurf / Kiro / Gemini CLI 等 16 款 AI 编程工具真正会干活
openclaw-superpowers
44 plug-and-play skills for OpenClaw — self-modifying AI agent with cron scheduling, security guardrails, persistent memory, knowledge graphs, and MCP health monitoring. Your agent teaches itself new behaviors during conversation.
stitch-skills
A library of Agent Skills designed to work with the Stitch MCP server. Each skill follows the Agent Skills open standard, for compatibility with coding agents such as Antigravity, Gemini CLI, Claude Code, Cursor.
Agent Skills
Open format and reference SDK for packaging reusable capabilities and expertise for AI agents. [#opensource](https://github.com/agentskills/agentskills)
Skill_Seekers
Convert documentation websites, GitHub repositories, and PDFs into Claude AI skills with automatic conflict detection
Best For
- ✓AI agent developers building multi-tool systems with npm ecosystem packages
- ✓Teams automating agent capability expansion without manual schema writing
- ✓LLM application builders using Claude or similar models with tool-calling support
- ✓Teams using Claude as their primary LLM and needing deterministic skill generation
- ✓Production systems requiring schema-compliant output without fallback handling
- ✓Developers building skill generation pipelines that feed directly into agent systems
- ✓Teams managing agents with large tool ecosystems
- ✓CI/CD pipelines that need to auto-generate skills from dependency trees
Known Limitations
- ⚠Accuracy depends on documentation quality and clarity — poorly documented packages may generate incomplete or incorrect skill definitions
- ⚠Requires LLM API calls (Claude) for each package, adding latency and cost per conversion
- ⚠No built-in validation that generated skills actually work with the target package version
- ⚠Limited to npm packages with accessible documentation; private or undocumented packages will fail
- ⚠Locked to Claude API — no support for other LLM providers (GPT-4, Gemini, local models)
- ⚠Requires Claude API credits/billing for each package processed
Requirements
Input / Output
UnfragileRank
UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.
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Generate AI agent skills from npm package documentation
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