# Dossier — Signed, Versioned Agent Skills for Model-Driven Orchestration

**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](https://ai-dossier.dev)

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> **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.

```
  ┌──────────────────────────────────────────────────────────────────────┐
  │                                                                      │
  │    Write a skill (.ds.md)   Verify integrity       AI executes       │
  │    in Markdown, then sign   with checksums &       the workflow      │
  │                             signatures             intelligently     │
  │                                                                      │
  │    ┌──────────┐    sign     ┌──────────┐   run     ┌──────────┐     │
  │    │  Author  │ ─────────> │  Verify  │ ────────> │ AI Agent │     │
  │    └──────────┘            └──────────┘            └──────────┘     │
  │         │                       │                       │            │
  │     .ds.md file            checksum +              validated         │
  │     with JSON              signature               results with     │
  │     frontmatter            verification            evidence         │
  │                                                                      │
  └──────────────────────────────────────────────────────────────────────┘
```

**New here?** → [5-min Quick Start](docs/getting-started/quick-start.md) | **Using Claude Code?** → [MCP in 60 Seconds](docs/tutorials/mcp-quickstart.md) | **Want to try now?** → [Get started in 30 seconds](#get-started)

---

## At a Glance

```mermaid
flowchart LR
    A["📝 Create\n.ds.md file"] --> B["🔏 Sign\nchecksum +\nsignature"]
    B --> C["✅ Verify\nintegrity &\nauthenticity"]
    C --> D["🤖 Execute\nAI runs the\nworkflow"]
    D --> E["📋 Validate\nsuccess criteria\n& evidence"]

    style A fill:#e3f2fd,stroke:#1565c0,color:#0d47a1
    style B fill:#fce4ec,stroke:#c62828,color:#b71c1c
    style C fill:#fff3e0,stroke:#ef6c00,color:#e65100
    style D fill:#e8f5e9,stroke:#2e7d32,color:#1b5e20
    style E fill:#f3e5f5,stroke:#6a1b9a,color:#4a148c
```

**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](https://agentskills.io/specification) 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](docs/explanation/faq.md#which-frontmatter-layout-does-a-dossier-use)

---

## 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](https://aws.amazon.com/blogs/opensource/strands-agents-and-the-model-driven-approach/). 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`](packages/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](docs/explanation/model-driven-orchestration.md).

---

## Get Started

### 1. Run a dossier — zero install

Pick any LLM you already have and paste this:

```
Analyze my project using the dossier at:
https://raw.githubusercontent.com/imboard-ai/ai-dossier/main/examples/guides/context-engineering-best-practices.ds.md
```

That's it. The LLM reads the dossier and follows its instructions — no tools needed.

Want to verify it first?

```bash
npx @ai-dossier/cli verify https://raw.githubusercontent.com/imboard-ai/ai-dossier/main/examples/guides/context-engineering-best-practices.ds.md
```

### 2. Add the MCP server to Claude Code

One command gives Claude Code native dossier support — discover, verify, and execute dossiers without copy-pasting URLs:

```bash
claude mcp add dossier --scope user -- npx @ai-dossier/mcp-server
```

Then ask Claude: *"List available dossiers"* or *"Run the scaffold-typescript-project dossier"*.

<details>
<summary>Alternative: Claude Code plugin (auto-updates)</summary>

```
/plugin marketplace add imboard-ai/ai-dossier
/plugin install dossier-mcp-server@ai-dossier
```
</details>

<details>
<summary>Alternative: Manual JSON config (Claude Desktop or other MCP clients)</summary>

Add to `claude_desktop_config.json` or your MCP client's config file:

```json
{
  "mcpServers": {
    "dossier": {
      "command": "npx",
      "args": ["-y", "@ai-dossier/mcp-server"]
    }
  }
}
```
</details>

### 3. Create your own dossier

Initialize dossier in your project (sets up `~/.dossier/`, hooks, and MCP config):

```bash
npx @ai-dossier/cli init
```

Then create a dossier:

```bash
npx @ai-dossier/cli create my-workflow
```

This scaffolds a `.ds.md` file you can edit. A dossier is just Markdown with a metadata block on top, which you can write as JSON:

```markdown
---dossier
{
  "title": "My Workflow",
  "version": "1.0.0",
  "protocol_version": "1.0",
  "status": "draft",
  "objective": "Describe what this automates",
  "risk_level": "low"
}
---

# My Workflow

## Actions
1. Step one — what to do
2. Step two — what to verify

## Validation
- Expected outcome was achieved
```

When you sign it, `ai-dossier sign` rewrites the header as standard `---` YAML in the [Agent Skills](https://agentskills.io/specification) layout (`name` and `description` at the top, the other fields under `metadata` as `dossier.*` strings) and adds a signature that covers it. The signed file passes `skills-ref validate` and installs into Claude Code as is. See [Spec-Shaped Dossiers](docs/reference/spec-shape.md) for the walkthrough.

See the [Authoring Guide](docs/guides/authoring-guidelines.md) for the full spec, or browse the [Dossier Registry](https://ai-dossier.dev/registry/) for real-world examples.

---

## Why Use Dossier?

**"Isn't this just a skill?"** Yes — a dossier *is* a skill. The difference is everything a plain skill (like a Claude Code `SKILL.md`) lacks:

|  | Plain skill (`SKILL.md`) | Dossier |
|--|--------------------------|---------|
| **Trust** | Unsigned — anyone can tamper | Checksum + cryptographic signature, verified before run |
| **Versioning** | Informal | Semantic versioning you can pin |
| **Distribution** | Copy-paste / per-tool | Registry — discoverable, `ai-dossier install-skill` |
| **Portability** | Locked to one tool | Same file runs on Claude, ChatGPT, Cursor, any LLM |
| **Validation** | None | Built-in success criteria |

**Trigger skills bridge the two**: a thin `SKILL.md` whose job is to invoke a versioned, signed dossier (`ai-dossier run <registry-path>`) — you keep the natural-language trigger *and* gain signing, versioning, and registry distribution.

**"How about AGENTS.md files?"** Different job: `AGENTS.md` explains *your project*; a dossier automates a *workflow*. They're complementary.

---

## Architecture

```mermaid
graph TB
    subgraph Packages["@ai-dossier packages"]
        Core["@ai-dossier/core\nParsing, verification,\nlinting, risk assessment"]
        CLI["@ai-dossier/cli\nCommand-line tool\nverify, sign, search, run"]
        MCP["@ai-dossier/mcp-server\nMCP integration for\nClaude Code & others"]
        Registry["@ai-dossier/registry\nVercel serverless API\nDiscover & publish"]
    end

    subgraph Inputs["Dossier Files"]
        DS[".ds.md\nImmutable instructions\nfrontmatter + Markdown"]
        DSW[".dsw.md\nMutable working files\nExecution state"]
    end

    subgraph Consumers["AI Agents"]
        Claude["Claude Code"]
        ChatGPT["ChatGPT"]
        Cursor["Cursor"]
        Other["Any LLM"]
    end

    DS --> Core
    DSW --> Core
    Core --> CLI
    Core --> MCP
    CLI --> Registry
    MCP --> Claude
    MCP --> ChatGPT
    MCP --> Cursor
    MCP --> Other
    CLI -->|"verify & run"| Consumers

    style Core fill:#e3f2fd,stroke:#1565c0,color:#0d47a1
    style CLI fill:#e8f5e9,stroke:#2e7d32,color:#1b5e20
    style MCP fill:#fff3e0,stroke:#ef6c00,color:#e65100
    style Registry fill:#f3e5f5,stroke:#6a1b9a,color:#4a148c
    style DS fill:#fff9c4,stroke:#f9a825,color:#f57f17
    style DSW fill:#fff9c4,stroke:#f9a825,color:#f57f17
```

### Verification Pipeline

Every dossier goes through a multi-stage security pipeline before execution:

```mermaid
flowchart TD
    Start(["dossier verify file.ds.md"]) --> Parse["Parse frontmatter\n+ Markdown body"]
    Parse --> Checksum{"Checksum\nverification"}

    Checksum -->|"SHA-256 match"| SigCheck{"Signature\nverification"}
    Checksum -->|"mismatch"| Block["BLOCK execution\nContent tampered"]

    SigCheck -->|"valid + trusted"| Risk["Risk assessment"]
    SigCheck -->|"valid + untrusted"| Risk
    SigCheck -->|"unsigned"| Risk
    SigCheck -->|"invalid"| Block

    Risk -->|"low"| Safe["SAFE to execute"]
    Risk -->|"medium/high"| Caution["PROCEED with caution"]
    Risk -->|"critical + unsigned"| Block

    style Start fill:#e3f2fd,stroke:#1565c0,color:#0d47a1
    style Safe fill:#e8f5e9,stroke:#2e7d32,color:#1b5e20
    style Caution fill:#fff3e0,stroke:#ef6c00,color:#e65100
    style Block fill:#ffebee,stroke:#c62828,color:#b71c1c
    style Checksum fill:#f5f5f5,stroke:#616161,color:#212121
    style SigCheck fill:#f5f5f5,stroke:#616161,color:#212121
    style Risk fill:#f5f5f5,stroke:#616161,color:#212121
```

See [ARCHITECTURE.md](ARCHITECTURE.md) for the full system architecture.

---

## Examples

| Example | Use Case |
|---------|----------|
| [Scaffold TypeScript Project](./examples/setup/scaffold-typescript-project.ds.md) | Scaffold a production-ready TS project with CI, testing, linting |
| [Context Engineering Best Practices](./examples/guides/context-engineering-best-practices.ds.md) | Reference guide for writing effective AI agent context files |

Browse the **[Dossier Registry](https://ai-dossier.dev/registry/)** for the full collection — DevOps, databases, data science, security, and more.

```bash
# Search from the CLI
npx @ai-dossier/cli search deploy
```

---

## Security & Verification

```mermaid
flowchart LR
    Author["Author"] -->|"signs"| Dossier[".ds.md"]
    Dossier -->|"distributed via"| Registry["Registry / URL"]
    Registry -->|"fetched by"| CLI["CLI / MCP"]
    CLI -->|"verifies"| Checks["Checksum\n+ Signature\n+ Risk Level"]
    Checks -->|"safe"| Execute["Execute"]
    Checks -->|"blocked"| Reject["Reject"]

    style Author fill:#e3f2fd,stroke:#1565c0,color:#0d47a1
    style Dossier fill:#fff9c4,stroke:#f9a825,color:#f57f17
    style Checks fill:#fff3e0,stroke:#ef6c00,color:#e65100
    style Execute fill:#e8f5e9,stroke:#2e7d32,color:#1b5e20
    style Reject fill:#ffebee,stroke:#c62828,color:#b71c1c
```

- Use the CLI tool (`ai-dossier verify`) to verify checksums/signatures before execution
- Prefer MCP mode for sandboxed, permissioned operations
- **External reference declaration**: Dossiers that fetch or link to external URLs must declare them in `external_references` with trust levels. The linter flags undeclared URLs, and the MCP server's `read_dossier` tool returns `security_notices` for any undeclared external URLs found in the body. This mitigates transitive trust risks from unvetted external content.
- See [SECURITY_STATUS.md](./SECURITY_STATUS.md) for current guarantees and limitations

---

## Registry & Multi-Registry Support

The CLI supports multiple registries for discovering, publishing, and sharing dossiers across teams and organizations.

```mermaid
flowchart LR
    CLI["dossier CLI"] -->|"parallel query"| R1["Public Registry\ndossier-registry.vercel.app"]
    CLI -->|"parallel query"| R2["Internal Registry\ndossier.company.com"]
    CLI -->|"parallel query"| R3["Mirror Registry\nmirror.example.com"]

    R1 -->|"results"| Merge["Merge results\n(partial failure OK)"]
    R2 -->|"results"| Merge
    R3 -->|"error"| Merge

    Merge --> User["User sees\ncombined results"]

    style CLI fill:#e3f2fd,stroke:#1565c0,color:#0d47a1
    style Merge fill:#e8f5e9,stroke:#2e7d32,color:#1b5e20
    style R3 fill:#ffebee,stroke:#c62828,color:#b71c1c
```

- **Multi-registry**: Configure multiple registries (public, internal, mirrors) queried in parallel
- **HTTPS enforcement**: All registry URLs must use HTTPS to protect credentials in transit
- **Per-registry credentials**: Each registry has isolated authentication — a compromised token cannot access other registries
- **Project-level config**: Add a `.dossierrc.json` to your project for team-shared registry settings

```bash
# Add a private registry
dossier config --add-registry internal --url https://dossier.company.com

# List configured registries
dossier config --list-registries
```

See the [CLI documentation](./cli/README.md#config-command) for full registry management options.

---

## Adopter Playbooks

- **Solo Dev**: paste a `.ds.md` into your LLM and run via MCP or CLI
- **OSS Maintainer**: add `/dossiers` + a CI check that runs the Reality Check on your README
- **Platform Team**: start with init -> deploy -> rollback dossiers; wire secrets & scanners

Detailed playbooks in [docs/guides/adopter-playbooks.md](docs/guides/adopter-playbooks.md)

---

## Documentation

| | |
|---|---|
| **Getting Started** | [Quick Start](docs/getting-started/quick-start.md) · [Installation](docs/getting-started/installation.md) · [MCP in 60 Seconds](docs/tutorials/mcp-quickstart.md) · [Your First Dossier](docs/tutorials/your-first-dossier.md) · [FAQ](docs/explanation/faq.md) |
| **Reference** | [Protocol](docs/reference/protocol.md) · [Specification](docs/reference/specification.md) · [Schema](docs/reference/schema.md) · [JSON Schema](./dossier-schema.json) · [Plan Artifacts](docs/reference/plan-artifact.md) · [Core API](docs/reference/core-api.md) · [Capability Manifest](docs/reference/capabilities.md) |
| **Guides** | [Authoring Guidelines](docs/guides/authoring-guidelines.md) · [Dossier Guide](docs/guides/dossier-guide.md) · [CI/CD Integration](docs/guides/ci-cd-integration.md) · [Execution Tracing](docs/guides/tracing.md) · [Runstate Milestones](cli/README.md#runstate--workflow-milestones) · [Scheduler Core](cli/README.md#scheduler-core-sched) · [Adopter Playbooks](docs/guides/adopter-playbooks.md) · [Autonomous Issue Pipeline](docs/how-to/autonomous-pipeline.md) · [Examples](./examples/) |
| **Packages** | [CLI](./cli/) · [MCP Server](./mcp-server/) · [Core Library](./packages/core/) · [Scheduler](./packages/sched/) · [Registry](./registry/) |
| **Project** | [Architecture](ARCHITECTURE.md) · [Contributing](CONTRIBUTING.md) · [Security](SECURITY.md) · [Changelog](CHANGELOG.md) |

---

## Philosophy

> "A skill tells an agent what to do. A dossier lets you trust it."

Dossiers take the agent skill and add what copying files lacks: a pinnable version, a verifiable signature (so you can tell who wrote it and that it was not changed), and a registry to install it from. Verification proves integrity and origin; it does not prevent prompt injection or make a skill safe to run.

**The dossier standard** enables:
- **Trust**: cryptographic signatures and checksums, verified before execution
- **Versioning**: semantic versions you can pin and upgrade deliberately
- **Distribution**: a registry that makes skills discoverable and installable
- **Portability**: any project, any workflow, any LLM — no vendor lock-in
- **Adaptability**: agents understand context and adjust behavior

---

**Dossier: Signed, Versioned Agent Skills for Model-Driven Orchestration**
*Skills you can trust.*

---

## FAQ

### What is model-driven orchestration?

It is orchestration where the model decides the steps, tool calls, retries and when the work is done, instead of a hand-coded DAG or state machine. The term was popularised by AWS Strands Agents. Dossier adds signed, version-pinned skills and a deterministic scheduler around the model. See [How it works](#how-it-works-model-driven-orchestration).

### How do I sign an agent skill?

Signed agent skills start as a `.ds.md` file. Sign it with the CLI to add a checksum and an Ed25519 signature, and anyone can then verify it before running. See the [5-min Quick Start](docs/getting-started/quick-start.md) and [Create your own dossier](#3-create-your-own-dossier).

### How is Dossier different from SKILL.md?

It is a layer on top, not a replacement. A `SKILL.md` is an Agent Skills file; a dossier is a skill that also carries a pinned version, a signature and registry distribution. `ai-dossier install-skill` and `ai-dossier skill-export` move skills between the two. See [Why Use Dossier?](#why-use-dossier).

### Does verification make a skill safe?

No. Verification proves integrity (the skill was not changed) and origin (who signed it). It does not prevent prompt injection or make a skill safe to run. Read a skill, and trust its author, before you run it. See [Security & Verification](#security--verification).

---

## License

This project is licensed under the [GNU Affero General Public License v3.0 (AGPL-3.0)](LICENSE). You are free to use, copy, modify, and distribute it, provided that any modified versions or network services using this software also make their source code available under the same license.

## References

See [REFERENCES.md](REFERENCES.md) for the full list of academic references and industry research supporting the dossier approach.