pref0

name	pref0
description	Learn user preferences from conversations and personalize responses automatically. Preferences compound over time — corrections like "use TypeScript, not JavaScript" are captured and injected into future sessions.
version	1.0.0
user-invocable	true
metadata	{"openclaw":{"requires":{"env":["PREF0_API_KEY"]},"primaryEnv":"PREF0_API_KEY"}}

pref0 — Preference Learning for AI Agents

You have access to the pref0 API. It learns user preferences from conversations and serves them back at inference time. The more conversations you track, the better it gets.

When to use this skill

After a conversation ends → Track it

After finishing a conversation (or at natural breakpoints), send the messages to pref0 so it can extract preferences. This is especially valuable when the user corrects you (e.g., "use pnpm, not npm") or states explicit preferences (e.g., "always use metric units").

Before responding to a user → Fetch their preferences

Before generating a response, fetch the user's learned preferences and follow them. This prevents the user from having to repeat themselves across sessions.

API Reference

Base URL: https://api.pref0.com Auth: Authorization: Bearer $PREF0_API_KEY

Track a conversation (POST /v1/track)

Send a conversation so pref0 can learn from it. It extracts corrections, explicit preferences, and behavioral patterns automatically.

curl -X POST https://api.pref0.com/v1/track \
  -H "Authorization: Bearer $PREF0_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "userId": "<user-id>",
    "messages": [
      { "role": "user", "content": "Help me set up a new project" },
      { "role": "assistant", "content": "Here is a project using npm and JavaScript..." },
      { "role": "user", "content": "Use pnpm, not npm. And TypeScript." },
      { "role": "assistant", "content": "Updated to pnpm and TypeScript..." }
    ]
  }'

Response:

{
  "messagesAnalyzed": 4,
  "preferences": { "created": 2, "reinforced": 0, "decreased": 0, "removed": 0 },
  "patterns": { "created": 1, "reinforced": 0 }
}

The response tells you how many messages were processed (messagesAnalyzed) and exactly what changed: created (new preference learned), reinforced (existing preference seen again, confidence increased), decreased (user retracted, confidence lowered), removed (fully retracted and deleted).

Get learned preferences (GET /v1/profiles/:userId)

Retrieve the user's learned preference profile. Use ?minConfidence=0.5 to only get well-learned preferences suitable for system prompt injection.

curl https://api.pref0.com/v1/profiles/<user-id>?minConfidence=0.5 \
  -H "Authorization: Bearer $PREF0_API_KEY"

Response:

{
  "userId": "user_abc123",
  "preferences": [
    {
      "key": "language",
      "value": "typescript",
      "confidence": 0.85,
      "evidence": "User said: Use TypeScript, not JavaScript",
      "firstSeen": "2026-01-15T10:00:00.000Z",
      "lastSeen": "2026-02-05T14:30:00.000Z"
    },
    {
      "key": "package_manager",
      "value": "pnpm",
      "confidence": 0.85,
      "evidence": "User said: use pnpm instead of npm",
      "firstSeen": "2026-01-15T10:00:00.000Z",
       
    
    
       
       
       
       
       
       
    
  
   
         
  
   

Each preference includes evidence (the quote that triggered extraction), firstSeen (when first learned), and lastSeen (when last reinforced). The prompt field is a ready-to-use string you can append directly to your system prompt.

Delete a user profile (DELETE /v1/profiles/:userId)

Reset a user's learned preferences. Use for preference resets or data deletion requests.

curl -X DELETE https://api.pref0.com/v1/profiles/<user-id> \
  -H "Authorization: Bearer $PREF0_API_KEY"

Returns 204 No Content.

How to integrate into your workflow

1. Identify the user. Use a stable user ID (email, account ID, phone number — whatever you have).

2. At the start of a session, fetch preferences:

   • Call GET /v1/profiles/{userId}?minConfidence=0.5
   • Use the prompt field to inject into your system prompt directly, or use the structured preferences array for more control.

3. At the end of a session, track the conversation:

   • Call POST /v1/track with the full message history
   • pref0 handles extraction and confidence scoring automatically

4. Preferences compound over time. Corrections start at 0.70 confidence, implied preferences at 0.40. Each repeated signal adds +0.15, capped at 1.0.

Confidence guide

Signal type	Starting confidence	Example
Explicit correction	0.70	"Use Tailwind, not Bootstrap"
Implied preference	0.40	"Deploy it to Vercel"
Behavioral pattern	0.30	User consistently wants short replies
Each repeat	+0.15	Same preference across sessions

Setup

1. Sign up at pref0.com
2. Create an API key in the dashboard
3. Set the PREF0_API_KEY environment variable
4. First 100 requests/month are free, then $5 per 1,000 requests