ai.imboard/dossier
MCP server for dossier automation standard - enables LLMs to discover, verify, and execute dossiers
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Dossier β 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 β structured, portable, verifiable.
ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β β
β Write instructions Verify integrity AI executes β
β in Markdown (.ds.md) with checksums & the workflow β
β signatures intelligently β
β β
β ββββββββββββ sign ββββββββββββ run ββββββββββββ β
β β Author β βββββββββ> β Verify β ββββββββ> β AI Agent β β
β ββββββββββββ ββββββββββββ ββββββββββββ β
β β β β β
β .ds.md file checksum + validated β
β with JSON signature results with β
β frontmatter verification evidence β
β β
ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
New here? β 5-min Quick Start | Using Claude Code? β MCP in 60 Seconds | Want to try now? β Get started in 30 seconds
At a Glance
flowchart LR
A["π Create\n.ds.md file"] --> B["π Sign\nchecksum +\nsignature"]
B --> C["β
Verify\nintegrity &\nauthenticity"]
C --> D["π€ Execute\nAI runs the\nworkflow"]
D --> E["π 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 β 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 β 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 β 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 β 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".
Alternative: Claude Code plugin (auto-updates)
/plugin marketplace add imboard-ai/ai-dossier
/plugin install dossier-mcp-server@ai-dossier
Alternative: Manual JSON config (Claude Desktop or other MCP clients)
Add to claude_desktop_config.json or your MCP client's config file:
{
"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 β what to do
2. Step two β 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:
| 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.
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
| 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 β 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_referenceswith trust levels. The linter flags undeclared URLs, and the MCP server'sread_dossiertool returnssecurity_noticesfor 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 β a compromised token cannot access other registries
- Project-level config: Add a
.dossierrc.jsonto 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.mdinto 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 Β· MCP in 60 Seconds Β· Your First Dossier Β· FAQ |
| Reference | Protocol Β· Specification Β· Schema Β· JSON Schema |
| Guides | Authoring Guidelines Β· Dossier Guide Β· CI/CD Integration Β· Adopter Playbooks Β· Examples |
| Packages | CLI Β· MCP Server Β· Core Library Β· Registry |
| Project | Architecture Β· Contributing Β· Security Β· Changelog |
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.
