ai.imboard/dossier

Name: ai.imboard/dossier
Rating: 0.4 (7 reviews)
Author: imboard-ai

AI 与智能体

by imboard-ai

面向 dossier automation standard 的 MCP server，使 LLMs 能发现、验证并执行 dossiers 自动化流程。

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什么是 ai.imboard/dossier？

面向 dossier automation standard 的 MCP server，使 LLMs 能发现、验证并执行 dossiers 自动化流程。

README

Dossier — Portable, Signed Skills for Any AI Agent

Skills are easy to write. Dossiers make them trustworthy, versioned, and portable across every LLM tool.

Quick Concept 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.

code

  ┌──────────────────────────────────────────────────────────────────────┐
  │                                                                      │
  │    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

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, and shareable 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 v0.8.5 | 15+ example skills | 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:

code

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>

code

/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 JSON frontmatter block:

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

See the Authoring Guide for the full spec, or browse the Dossier 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\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:

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 for the full system architecture.

Examples

Example	Use Case
Scaffold TypeScript Project	Scaffold a production-ready TS project with CI, testing, linting
Context Engineering Best Practices	Reference guide for writing effective AI agent context files

Browse the Dossier 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 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 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


Getting Started	Quick Start · Installation · MCP in 60 Seconds · Your First Dossier · FAQ
Reference	Protocol · Specification · Schema · JSON Schema
Guides	Authoring Guidelines · Dossier Guide · CI/CD Integration · Execution Tracing · Adopter Playbooks · Examples
Packages	CLI · MCP Server · Core Library · Registry
Project	Architecture · Contributing · Security · Changelog

Philosophy

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

Dossiers take the skill — a reusable instruction set any AI can run — and add the things that make it safe to share: a verifiable signature, a pinnable version, and a registry to distribute it through.

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: Portable, Verifiable Skills for Any LLM Skills you can trust.

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.

常见问题