skillit-core

Render extracted code and documentation data into SKILL.md packages with reference files.

7|3|Updated Mar 21, 2026
One-click install
npx skills add https://github.com/pradeepmouli/skillit --skill skillit-core
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: skillit-core
Source: https://github.com/pradeepmouli/skillit/tree/main/skills/skillit-core
Command: npx skills add https://github.com/pradeepmouli/skillit --skill skillit-core

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps turn extracted code and documentation data into polished, discoverable SKILL.md packages without hand-authoring the rendering logic. It reduces the friction of building agent skills by centralizing progressive disclosure, token budgeting, and quality checks in one reusable core.

Core Features & Use Cases

  • Skill Rendering: Convert extracted functions, classes, types, enums, variables, and documents into SKILL.md and reference files.
  • Documentation Scanning: Parse README files, project docs, and examples so skills can include richer context beyond API signatures.
  • Audit and Quality Control: Run structured checks to catch missing documentation, weak coverage, or formatting problems before publishing.
  • Configuration Surfaces: Render CLI commands, config files, and environment-variable interfaces into agent-friendly documentation.
  • Use Case: A tool author can feed repository metadata into this Skill to generate a complete, token-aware skill package for an AI agent.

Quick Start

Use skillit-core to render your extracted project data into a skill package and write the generated files to your chosen output directory.

Frequently Asked Questions about skillit-core

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate agent skills from codebase documentation and config files?

Generate agent skills from codebases by rendering extracted functions, classes, and documents into progressive-disclosure SKILL.md packages with reference files. This process parses README files and configuration surfaces to create token-aware output for AI agents.

What is progressive disclosure in markdown documentation for AI agents?

Progressive disclosure in markdown documentation structures SKILL.md packages by layering reference files and token-aware output. It allows AI agents to consume relevant codebase context gradually without exceeding token budget limitations.

How do I audit documentation coverage before publishing an AI skill package?

Audit documentation coverage before publishing by running structured quality checks that catch missing documentation, weak coverage, and formatting problems. This validation ensures the generated skill package meets quality standards for agent consumption.

Can I render CLI commands and environment variables into agent-friendly documentation?

Render CLI commands, config files, and environment-variable interfaces into agent-friendly documentation through configuration surface rendering. This captures extracted project metadata and transforms it into accessible SKILL.md content.

What is the best way to budget tokens when creating SKILL.md packages from repository metadata?

Budget tokens when creating SKILL.md packages by applying token-aware output generation to extracted repository metadata. This ensures the rendered skill package and reference files remain within context window limits for AI agents.

Do I need extracted code data before using skillit-core, or can it parse repositories directly?

You need extracted code and documentation data before rendering, because skillit-core processes pre-extracted functions, classes, types, and documents. It does not parse raw repositories directly, but transforms prepared metadata into skill packages.