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MCP server for dossier automation standard - enables LLMs to discover, verify, and execute dossiers
MCP server for dossier automation standard - enables LLMs to discover, verify, and execute dossiers
Valid MCP server (1 strong, 1 medium validity signals). No known CVEs in dependencies. ⚠️ Package registry links to a different repository than scanned source. Imported from the Official MCP Registry. 1 finding(s) downgraded by scanner intelligence.
9 files analyzed · 1 issue found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
Add this to your MCP configuration file:
{
"mcpServers": {
"ai-imboard-dossier": {
"args": [
"-y",
"@ai-dossier/cli"
],
"command": "npx"
}
}
}From the project's GitHub README.
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
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
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
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
Add to claude_desktop_config.json or your MCP client's config file:
{
"mcpServers": {
"dossier": {
"command": "npx",
"args": ["-y", "@ai-dossier/mcp-server"]
}
}
}
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.
"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:
| AGENTS.md | Dossier | |
|---|---|---|
| Purpose | Project context & conventions | Executable workflow automation |
| Validation | None | Built-in success criteria |
| Security | None | Checksums + cryptographic signatures |
| Portability | Project-specific | Cross-project, shareable |
| Tooling | None | CLI verification, MCP integration |
| Versioning | Informal | Semantic versioning (v1.0.0) |
They're complementary: Use AGENTS.md to explain your project, use dossiers to automate workflows.
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
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.
| 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.
# Search from the CLI
npx @ai-dossier/cli search deploy
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
ai-dossier verify) to verify checksums/signatures before executionexternal_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.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
.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.
.ds.md into your LLM and run via MCP or CLI/dossiers + a CI check that runs the Reality Check on your READMEDetailed playbooks in docs/guides/adopter-playbooks.md
| Getting Started | Quick Start · MCP in 60 Seconds · Your First Dossier · FAQ |
| Reference | Protocol · Specification · Schema · JSON Schema |
| Guides | Authoring Guidelines · Dossier Guide · CI/CD Integration · Adopter Playbooks · Examples |
| Packages | CLI · MCP Server · Core Library · Registry |
| Project | Architecture · Contributing · Security · Changelog |
"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:
Dossier: Universal LLM Automation Standard Structure your agents. Not your scripts.
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.
See REFERENCES.md for the full list of academic references and industry research supporting the dossier approach.
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