Snap

One skill library for all your AI agents, with on-demand skill discovery.

Watch the launch video · Music and sound credits

Current limits: 64 skills in the dashboard; each skill is a single SKILL.md. Associated scripts, assets and multi-file packages are not bundled.

Quick start

Requires Git and a running Docker installation with Compose. No host Node.js or Python install is needed. These are the three shell commands; after copying .env, configure the model and replace JWT_SECRET before starting the stack. The first image build downloads dependencies and can take several minutes.

git clone https://github.com/sanjaykannan8/Snap.git && cd Snap
cp -n .env.example .env
# Edit .env as described below before running the third command.
docker compose up -d --build

git clone https://github.com/sanjaykannan8/Snap.git; if ($LASTEXITCODE -eq 0) { Set-Location Snap }
if (!(Test-Path .env)) { Copy-Item .env.example .env }
# Edit .env as described below before running the third command.
docker compose up -d --build

If already cloned, enter your existing Snap directory instead of cloning again. Keep an existing .env and edit its settings. Install Docker Desktop on Windows/macOS, or Docker Engine with Compose on Linux. Check docker info if the daemon is unavailable.

Configure the model

Open .env in your editor. Replace JWT_SECRET with a random secret, then choose one routing provider:

# Cloudflare Workers AI
ROUTING_PROVIDER=cloudflare
CLOUDFLARE_ACCOUNT_ID=YOUR_ACCOUNT_ID
CLOUDFLARE_API_TOKEN=YOUR_WORKERS_AI_TOKEN

Or point Snap at an already running System One-compatible model server:

ROUTING_PROVIDER=local
LOCAL_SYSTEMONE_URL=http://YOUR_MODEL_HOST:30000/v1/systemone
LOCAL_SYSTEMONE_MODEL=Cloudflare/clef-flash
LOCAL_SYSTEMONE_API_KEY=YOUR_MODEL_SERVER_KEY

Use host.docker.internal for a model on the Docker host, or a reachable hostname/IP for another machine. Set the model name and key to those expected by your endpoint; the key can be blank for a server without authentication. Snap does not install the model server.

openssl rand -hex 32

Paste the result into JWT_SECRET in .env.

$snapBytes = New-Object byte[] 32
[Security.Cryptography.RandomNumberGenerator]::Create().GetBytes($snapBytes)
-join ($snapBytes | ForEach-Object { $_.ToString('x2') })

Paste the result into JWT_SECRET in .env.

After startup, open localhost:3000, create an account, publish a SKILL.md, and generate a key under Connections. Copy the displayed configuration into your agent. The init container should exit successfully; web, mcp, dynamodb and minio should stay running. Check with docker compose ps -a.

Related MCP server: skill-router-mcp

Why Snap

• Publish once, use across agents. Maintain one central library and connect MCP clients to it.

• Load skills on demand. Snap returns at most one matched skill per call, without preloading the whole library into the agent.

• Choose your decision endpoint. Use Cloudflare Workers AI or a System One-compatible server. The endpoint receives the task and skill names/descriptions.

A decision model returns probabilities for typed choices instead of generating text. Snap defaults to Clef-flash; its System One state/questions interface is compatible with TypeSafe's Jev API, with the model identifier configured for your endpoint.

How it works

1. Publish a SKILL.md with a name and description.

2. Connect your agent through MCP using its generated X-Snap-API-Key header.

3. The agent calls get_skill with a task; Snap scores the library metadata and returns a skill when the confidence clears SKILL_MATCH_THRESHOLD (default 0.42). Otherwise it returns no match.

Example get_skill call

Input through your MCP client:

{
  "name": "get_skill",
  "arguments": {
    "problem": "Extract the invoice line items from this scanned PDF into a table."
  }
}

Illustrative decoded tool response for an object-backed skill (confidence is the model's score, not a guarantee):

{
  "name": "pdf-extract",
  "description": "Extract text, tables and form fields from PDF files, including scanned PDFs that need OCR.",
  "confidence": 0.97,
  "content": "---\nname: pdf-extract\n---\nUse pdfplumber for text and tables. Fall back to OCR for scanned pages.",
  "url": "https://storage.example/snap-skills/pdf-extract/SKILL.md?X-Amz-Signature=...",
  "expiresInSeconds": 300
}

No match returns No matching skill found. See network and response details for content handling and download URLs.

Mini benchmark

Measured on 2026-11-01, using live Cloudflare/clef-flash inference through a LAN System One endpoint, local DynamoDB and MinIO, and authenticated HTTP MCP calls:

Small synthetic smoke test: eight in-scope and four out-of-scope prompts, each run twice per size (72 measured calls), with one excluded warm-up per size and threshold 0.42. Timing includes authentication, catalog lookup, model inference, matched skill retrieval, signing and the MCP response; it excludes agent reasoning and a separate download. No request errors occurred. These fixtures do not establish production accuracy or a universal latency target. Method, fixtures, raw results and reproduction.

Snap vs native skills

Native skills: a simpler choice for one agent with a small local library, especially when you need bundled scripts/assets or existing client-specific workflows.

Snap: useful when several agents should share one centrally managed library and retrieve a single matched skill through MCP. It adds a service, storage and an inference dependency.

Connect your agent

Connections generates setup for Claude Code, Codex, Cursor and VS Code. Copy the new key before dismissing the panel; it is shown once. Client configuration examples, including OpenCode.

Skill format

---
name: api-review
description: Review API endpoints for consistency and error handling.
---

# API review
Check request validation, response shapes, and error handling.

Troubleshooting

More documentation

• Expose Snap on your network

• Frontend development

• Configuration reference

• Backend and deployment

Stop the local stack with docker compose down; named data volumes are preserved.

Security and limitations

MCP keys are account-scoped and stored as SHA-256 hashes. The dashboard uses signed JWTs in HttpOnly, SameSite=Lax cookies. Keep keys and .env files private; use HTTPS and SESSION_COOKIE_SECURE=true for internet-facing deployments. Only publish and apply skill instructions you trust.

• The dashboard caps the library at 64 single-file skills; multi-file packages are not supported.

• No explicit “none applies” model option exists yet. The confidence threshold can return false matches, as the benchmark demonstrates.

• Context use depends on the client, library and frequency of calls. Retrieved content and history still accumulate; no general break-even point or constant total-session context has been established.

• Automatic fallback to the larger Clef model and an R2 deployment are not implemented.

Contributing

Run npm run lint for frontend changes and uv run pytest from mcp-service for MCP changes. The live benchmark is opt-in and requires a model endpoint.

License

MIT © 2026 Kannan K