Tencent SkillHub · AI

pref0

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.

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Target platform: OpenClaw
Install method: Manual import
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Prerequisites: OpenClaw
Primary doc: SKILL.md

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Package format: ZIP package
Source platform: Tencent SkillHub
What's included: SKILL.md, notes.txt

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Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 1.0.1

Provenance

Publisher: fliellerjulian
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Documentation

ClawHub primary doc Primary doc: SKILL.md 10 sections Open source page

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.

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", "lastSeen": "2026-02-03T09:15:00.000Z" }, { "key": "css_framework", "value": "tailwind", "confidence": 0.70, "evidence": "User said: Use Tailwind, not Bootstrap", "firstSeen": "2026-01-20T16:45:00.000Z", "lastSeen": "2026-01-20T16:45:00.000Z" } ], "patterns": [ { "pattern": "prefers explicit tooling choices over defaults", "confidence": 0.60 } ], "prompt": "The following preferences have been learned from this user's previous conversations. Follow them unless explicitly told otherwise:\n- language: typescript\n- package_manager: pnpm\n- css_framework: tailwind\n\nBehavioral patterns observed:\n- prefers explicit tooling choices over defaults" } 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

Identify the user. Use a stable user ID (email, account ID, phone number — whatever you have). 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. 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 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 typeStarting confidenceExampleExplicit correction0.70"Use Tailwind, not Bootstrap"Implied preference0.40"Deploy it to Vercel"Behavioral pattern0.30User consistently wants short repliesEach repeat+0.15Same preference across sessions

Setup

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

Category context

Agent frameworks, memory systems, reasoning layers, and model-native orchestration.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package

1 Docs1 Files

SKILL.md Primary doc
notes.txt Files