imboard-ai/ai-dossier

**An open standard for signed, versioned agent skills, built for model-driven orchestration: the model reads the skill and decides the steps, and Dossier makes that safe to share and repeat with signed agent skills. Host skills anywhere — a GitHub repo, your own server, or the Dossier registry — and everyone installs the same version and can verify who wrote it and that it wasn't changed.** Website
> **Quick Concept**
> A dossier is an agent skill with a version and a signature (a .ds.md file).
> Copying skill files between machines and teammates works, until nobody knows which version is running or who changed it. Dossier gives every skill a pinned version and an Ed25519 signature, so everyone installs the same thing and can verify who wrote it and that it wasn't changed.
> Like an npm package, a dossier works wherever it lives: the format carries the version and signature, so a skill fetched from GitHub verifies the same as one installed from a registry. Signatures prove integrity and origin; they do not prevent prompt injection or make a skill safe to run.
**New here?** → 5-min Quick Start | **Using Claude Code?** → MCP in 60 Seconds | **Want to try now?** → Get started in 30 seconds
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At a Glance
**What**: Skills (.ds.md files) any AI agent can run — signed, versioned, portable across tools
**Why**: A plain skill lives in one tool and anyone can tamper with it; a dossier is that same skill made verifiable, version-pinned
**Integrity**: Built-in checksums, cryptographic signatures, and CLI verification tools
**Works with**: Claude, ChatGPT, Cursor, any LLM — no vendor lock-in
**Works with Agent Skills / SKILL.md**: Dossier is a layer on top of Agent Skills, not a rival format. It adds signing and versioning, and you move skills in and out with ai-dossier install-skill and ai-dossier skill-export.
**Status**: Protocol v1.0 (stable spec) | CLI v0.14.0 | 15+ example skills | Active development
> **File conventions**: Dossiers use .ds.md (immutable instructions) and .dsw.md (mutable working files). Signed dossiers use standard --- YAML frontmatter in the Agent Skills layout, so each one is also a valid skill; hand-written ones can use the readable ---dossier JSON layout, which ai-dossier sign converts. Learn more
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How it works: model-driven orchestration
In *model-driven orchestration* the model decides the steps, the tool calls, the retries and when the work is done. A hand-coded DAG doesn't. Dossier makes that safe to share and repeat. Every skill is signed and version-pinned. Multi-step runs sit inside a deterministic scheduler that never calls an LLM.
The *model-driven approach* was popularised by AWS Strands Agents. Dossier builds on it and adds signing, versioning and a deterministic shell.
| | Code-driven orchestration | Model-driven orchestration | Dossier: hybrid model-driven orchestration |
|--|---------------------------|---------------------------|--------------------------------------------|
| **Who decides the steps** | Your code: a DAG or state machine | The model, from instructions and tools | The model, inside each skill |
| **What wraps it** | The workflow engine | Usually nothing deterministic | A deterministic scheduler (@ai-dossier/sched) that never calls an LLM |
| **Trust in the instructions** | Code review | Whatever prompt was loaded | Skills are signed and version-pinned, verified before they run |
| **Example** | A hand-built DAG of LLM calls | AWS Strands Agents | ai-dossier run on a signed .ds.md skill |
Dossier calls this **hybrid model-driven orchestration**: model-driven inside each skill, deterministic around it. Read the full explanation in Model-driven orchestration.
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Get Started
1. Run a dossier — zero install
Pick any LLM you already have and paste this:
That's it. The LLM reads the dossier and follows its instructions — no tools needed.