signalry-proxy

signalry-proxy

Transparent LLM API proxy that captures skill metadata for Signalry. Change one env var, keep all your existing code.

Quick start

# Start the proxy
npx signalry-proxy --port 4000 --candidate-id YOUR_ID --api-key sig_YOUR_KEY

# Point your SDK at it (one env var change)
export OPENAI_BASE_URL=http://localhost:4000/v1

# All existing code works unchanged
python -c "import openai; openai.chat.completions.create(model='gpt-4o', messages=[...])"

How it works

• Your code sends API requests to localhost:4000 instead of api.openai.com
• The proxy forwards the request to the real provider -- unchanged, with all your headers and auth
• The provider's response streams back to your code -- identical, transparent
• In the background, the proxy extracts skill patterns (languages, frameworks, domains) from the request
• Only anonymized metadata is sent to Signalry -- never raw text

Supported providers

Routes are auto-detected from request paths:

Path	Provider
/v1/chat/completions	OpenAI (also xAI, DeepSeek, any OpenAI-compatible)
/v1/messages	Anthropic
/v1/chat/complete	Mistral

Override with path prefixes: /openai/v1/..., /anthropic/v1/..., /deepseek/v1/...

Configuration

CLI flags

--port, -p          Port to listen on (default: 4000)
--candidate-id, -c  Signalry candidate ID
--api-key, -k       Signalry API key
--server-url, -s    Signalry server URL
--config            Path to config file (default: ~/.signalry/proxy.json)
--debug             Log extraction activity to stderr

Config file (~/.signalry/proxy.json)

{
  "port": 4000,
  "candidateId": "xxx",
  "apiKey": "sig_xxx",
  "serverUrl": "https://signalry-livid.vercel.app",
  "providers": {
    "openai": "https://api.openai.com",
    "anthropic": "https://api.anthropic.com",
    "xai": "https://api.x.ai",
    "deepseek": "https://api.deepseek.com",
    "mistral": "https://api.mistral.ai"
  }
}

Environment variables

SIGNALRY_PROXY_PORT=4000
SIGNALRY_CANDIDATE_ID=xxx
SIGNALRY_API_KEY=sig_xxx
SIGNALRY_SERVER_URL=https://signalry-livid.vercel.app

Priority: CLI flags > config file > env vars.

Privacy model

The proxy sees raw text (it has to, to forward it). But it:

• Extracts skills from the request messages locally
• Runs PII stripping on the extraction (not the forwarded request)
• Sends ONLY extraction metadata to Signalry (languages, frameworks, domains, complexity)
• Does NOT log, store, or cache the raw request/response body
• The forwarded request goes directly to the provider -- unchanged

What IS sent to Signalry: { model: "gpt-4o", languages: ["Python"], frameworks: ["FastAPI"], domain: "backend", complexity: "moderate" }

What is NOT sent: Your prompts, code, API keys, or any raw text.

For maximum privacy, use signalry-sdk instead (extraction happens inside your process with no proxy).

Streaming

The proxy fully supports SSE streaming (stream: true). Streaming responses are piped directly from the provider to your code without buffering. Skill extraction runs on the request body only, so streaming adds zero latency.

Zero dependencies

Built entirely on Node.js built-in modules (http, https, url, path, fs, crypto). No node_modules needed.

Health check

curl http://localhost:4000/health
# {"status":"ok","version":"0.1.0"}