AI Dossier

Dossier Documentation

Welcome to the Dossier project documentation. A dossier is a skill — a reusable instruction set an AI executes — with trust, versioning, and cross-tool portability built in. Think npm or Docker Hub, but for AI skills: signed, versioned, shareable.

Quick Navigation

🚀 Getting Started

New to Dossier? Start here to learn how to install and use the tools.

📚 Guides

Task-oriented guides for common workflows like creating dossiers, signing them, and publishing packages.

🔧 How-To

Operator runbooks for running infrastructure day to day, like the autonomous issue pipeline.

🎓 Tutorials

Step-by-step learning experiences to help you master Dossier.

📖 Reference

Technical specifications, protocol documentation, schemas, and API references.

💡 Explanation

Conceptual documentation that helps you understand how and why Dossier works the way it does.

🏗️ Architecture

System architecture, design decisions, and architecture decision records (ADRs).

🤝 Contributing

Developer documentation for contributors including development setup, workflows, and guidelines.

📋 Planning

Project roadmaps, planning documents, and development notes.

📊 Reports

Gate reports, validation records, and raw evidence for the RFC-0001 (Batch Cycles) rollout.

What is Dossier?

A dossier is a skill that adds what a plain skill (like a Claude Code SKILL.md) lacks:

  • Trust - SHA256 checksums + cryptographic signatures, verified before execution
  • Versioning - semantic versions you can pin
  • Distribution - a registry that makes skills discoverable and installable
  • Portability - human-readable Markdown that runs on any LLM tool

A trigger skill bridges the two: a thin SKILL.md that invokes a versioned, signed dossier (ai-dossier run <registry-path>).

Key Components

  • Protocol - The dossier file format and verification standard
  • CLI - Author, verify, publish, and run dossiers; the skill bridge (install-skill / skill-export)
  • MCP Server - Model Context Protocol integration for AI agents
  • Core Library - Shared verification and parsing logic

Documentation Structure

This documentation follows the Diataxis framework:

  • Tutorials: Learning-oriented lessons for beginners
  • How-to Guides: Task-oriented recipes for specific problems
  • Reference: Information-oriented technical descriptions
  • Explanation: Understanding-oriented discussions of key topics

Get Help

License: AGPL-3.0 | Maintained by: Imboard AI

Rendered from docs/index.md in the repository. Edit it there.