Dossier — Automation Instructions for AI Agents

Stop writing brittle scripts. Start writing instructions that AI executes intelligently.

Quick Concept Dossier turns plain-text instructions into executable workflows with built-in verification. Like Dockerfiles for AI automation — structured, portable, verifiable.

  ┌──────────────────────────────────────────────────────────────────────┐
  │                                                                      │
  │    Write instructions       Verify integrity       AI executes       │
  │    in Markdown (.ds.md)     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 | Using Claude Code? → MCP in 60 Seconds | Want to try now? → Get started in 30 seconds

At a Glance

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: Structured instruction files (.ds.md) that AI agents execute intelligently Why: Replace brittle scripts with adaptive, verifiable automation that handles edge cases naturally Safety: Built-in checksums, cryptographic signatures, and CLI verification tools Works with: Claude, ChatGPT, Cursor, any LLM — no vendor lock-in

Status: Protocol v1.0 (stable spec) | CLI v0.8.0 | 15+ example templates | Active development

File conventions: Dossiers use .ds.md (immutable instructions) and .dsw.md (mutable working files). Frontmatter uses ---dossier (JSON) instead of --- (YAML) to avoid parser conflicts. Learn more

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?

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:

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

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

/plugin marketplace add imboard-ai/ai-dossier
/plugin install dossier-mcp-server@ai-dossier

{
  "mcpServers": {
    "dossier": {
      "command": "npx",
      "args": ["-y", "@ai-dossier/mcp-server"]
    }
  }
}

3. Create your own dossier

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

npx @ai-dossier/cli init

Then create a dossier:

npx @ai-dossier/cli create my-workflow

This scaffolds a .ds.md file you can edit. A dossier is just Markdown with a JSON frontmatter block:

---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

See the Authoring Guide for the full spec, or browse the Dossier Registry for real-world examples.

Why Use Dossier?

"How is this different from AGENTS.md files?" Many projects already use files like AGENTS.md or .cursorrules for AI context. Here's the key distinction:

They're complementary: Use AGENTS.md to explain your project, use dossiers to automate workflows.

Architecture

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\nJSON frontmatter + 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:

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 for the full system architecture.

Examples

Browse the Dossier Registry for the full collection — DevOps, databases, data science, security, and more.

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

Security & Verification

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 for current guarantees and limitations

Registry & Multi-Registry Support

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

flowchart LR
    CLI["dossier CLI"] -->|"parallel query"| R1["Public Registry\nregistry.dossier.dev"]
    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

# 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 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

Documentation

Philosophy

"Agents need structure. Dossiers provide it."

Dossiers embody this philosophy - they give AI agents clear structure and guidance, enabling them to intelligently automate complex workflows that would be brittle to script.

The dossier standard enables:

• Adaptability: LLMs understand context and adjust behavior
• Maintainability: Markdown documentation instead of complex scripts
• Collaboration: Clear, readable instructions anyone can contribute to
• Continuous improvement: Self-improving through the protocol
• Universal adoption: Any project, any workflow, any implementation

Dossier: Universal LLM Automation Standard Structure your agents. Not your scripts.

License

This project is licensed under the GNU Affero General Public License v3.0 (AGPL-3.0). 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 for the full list of academic references and industry research supporting the dossier approach.