KinthAI Self-Improving User

User-level self-improvement: captures corrections, preferences, and errors per user_id. After every conversation where the user corrects you or states a preference, log it to .learnings/{user_id}/. Use when: user corrects you, states a preference, you make a mistake, or you learn something new about the user.

A user-level self-improvement skill for OpenClaw agents serving multiple users. Inspired by Self-Improving Agent (MIT-0).

What This Skill Does

You learn from every interaction — corrections, errors, preferences — and store that knowledge per user, so you get better at serving each person individually without mixing up experiences across users.

How It Works

Every time you interact with a user, you have access to two layers of learning:

1. Global learnings (_global/) — things you've learned that apply to ALL users
2. Per-user learnings ({user_id}/) — things specific to THIS user

Reading Learnings (Every Conversation Start)

At the beginning of each conversation, check for existing learnings:

text

1. Extract user_id from the message context:
   - Primary: MsgContext.SenderId (works for both DM and group chats)
   - Fallback: parse DM session key agent:{agent_id}:kinthai:direct:{user_id}
   - Note: user_id is an opaque public_id (base64url, 12 chars), NOT numeric

2. Read global learnings (if they exist):
   - .learnings/_global/LEARNINGS.md
   - .learnings/_global/PATTERNS.md

3. Read this user's learnings (if they exist):
   - .learnings/{user_id}/LEARNINGS.md
   - .learnings/{user_id}/PROFILE.md

4. Apply what you've learned to your responses.

Do NOT read other users' directories. Each user's learnings are private to their interactions with you.

Writing Learnings (After Each Task)

After completing a task, evaluate whether you learned something new. Write learnings ONLY when something genuinely noteworthy happened — not after every message.

When to Write

Trigger	Where to Write	Example
User corrects you	{user_id}/LEARNINGS.md	"Don't use Sequelize, we use Prisma"
User states a preference	{user_id}/PROFILE.md	"I prefer concise answers"
A command/tool fails	{user_id}/ERRORS.md	API returned 500 for that endpoint
You discover domain knowledge	{user_id}/LEARNINGS.md	"Their app uses PostgreSQL 16, not MySQL"
You learn something universal	_global/LEARNINGS.md	"PostgreSQL partial indexes need WHERE after CREATE INDEX"

Entry Format

Use this format for every entry in LEARNINGS.md and ERRORS.md:

markdown

## [LRN-{user_id}-{YYYYMMDD}-{NNN}] Brief title

- **Priority**: Low | Medium | High | Critical
- **Category**: correction | preference | knowledge | error | pattern
- **Context**: What were you doing when this happened
- **Learning**: What you learned (be specific)
- **Action**: What to do differently next time
- **Status**: active

For ERRORS.md, use ERR prefix instead of LRN.

PROFILE.md Format

Maintain a concise user profile that evolves over time:

markdown

# User Profile: {user_id}
Last updated: {YYYY-MM-DD}

## Communication
- Response style: [detailed | concise | mixed]
- Language: [English | Chinese | etc.]
- Code preference: [explain first | code first | both]

## Technical Context
- Primary stack: [e.g., React + Node.js + PostgreSQL]
- Project: [what they're working on]
- Experience level: [junior | mid | senior | lead]

## Preferences
- [Specific preferences learned from interactions]

## Key Corrections
- [Summary of important corrections they've made]

Directory Structure

text

.learnings/
  ├── _global/
  │     ├── LEARNINGS.md      Global knowledge (applies to all users)
  │     ├── ERRORS.md         System-level errors (not user-specific)
  │     └── PATTERNS.md       Patterns promoted from user-level
  │
  ├── {user_id}/              One directory per user
  │     ├── LEARNINGS.md      This user's corrections and knowledge
  │     ├── ERRORS.md         Errors when serving this user
  │     └── PROFILE.md        This user's preferences and context
  │
  └── _meta/
        └── promotion-log.md  Record of promotions from user → global

Rules

Do:

• Always extract user_id before reading or writing learnings
• Create user directories on first interaction (mkdir -p)
• Keep entries concise — one paragraph per learning, not an essay
• Update PROFILE.md incrementally — don't rewrite the whole file every time
• Write to _global/ only for genuinely universal knowledge
• Read both _global/ and {user_id}/ at conversation start

Don't:

• NEVER read another user's directory — privacy boundary
• NEVER log sensitive data (passwords, API keys, personal info)
• NEVER write a learning after every message — only when something genuinely new is learned
• NEVER let learnings override the user's explicit current instructions
• NEVER mention the .learnings system to the user — it's internal

Promotion: User → Global

When you notice the same learning appearing across 3+ different users, it's a pattern worth promoting to _global/PATTERNS.md:

markdown

## [PAT-{YYYYMMDD}-{NNN}] Pattern title

- **Observed in**: {user_id_1}, {user_id_2}, {user_id_3}
- **Pattern**: What keeps happening
- **Global action**: What to do for ALL users going forward
- **Promoted from**: [LRN-{user_id}-{date}-{NNN}], [LRN-...], [LRN-...]

After promoting, mark the original entries with Status: promoted_to_global.

Bootstrap Behavior

On first interaction with a new user (no .learnings/{user_id}/ directory exists):

1. Create the directory: .learnings/{user_id}/
2. Copy templates for LEARNINGS.md, ERRORS.md, PROFILE.md
3. Pay extra attention during the first few interactions — this is when you learn the most
4. After the first conversation, write initial PROFILE.md based on what you observed

Integration with Hindsight

If Hindsight memory is also installed:

• Hindsight handles: what the user said, conversation history, factual recall
• This skill handles: what YOU learned about how to serve them better
• Don't duplicate — if Hindsight already remembers a fact, you don't need to log it as a learning
• Focus on: corrections, preferences, mistakes, patterns — things that change YOUR behavior

When to Write FOLLOW_UPS

Trigger	Where to Write	Example
User mentions a future event	{user_id}/FOLLOW_UPS.md	"I have a job interview on Friday"
User mentions a plan or deadline	{user_id}/FOLLOW_UPS.md	"I'm traveling next week"
User asks you to remember something	{user_id}/FOLLOW_UPS.md	"Don't let me forget the deadline"
A previous follow-up is resolved	Update status in {user_id}/FOLLOW_UPS.md	User says "I got the job!" → mark resolved

Entry format:

[FUP-{user_id}-{YYYYMMDD}-{NNN}] Brief description

• Item: What to follow up on
• Mentioned: {date}
• Urgency: low | medium | high
• Status: pending | followed-up | resolved | expired

Rules:

• Only record specific, concrete events — not vague statements
• "I'm busy this week" → don't record (too vague)
• "I have a presentation on Thursday" → record (specific event)
• When you naturally bring up a follow-up in conversation, update status to "followed-up"
• When the user tells you the outcome, update status to "resolved"

When to Update PROFILE.md

PROFILE.md is NOT a log. It's your summary of who this user is.

When you feel your understanding of this user has changed — maybe you learned something new, or realized something you assumed was wrong — rewrite PROFILE.md.

Rules:

• Write it like you'd describe this person to a colleague: "This person is..."
• Keep it 5-8 sentences. Natural language, not YAML or bullet points.
• Overwrite the whole file (not append). It's always the latest snapshot.
• Don't update after every message. Only when your understanding meaningfully shifts.
• Include: how they communicate, what they care about, how your relationship is going, anything that affects how you should talk to them.

Example:

User Profile: 10042

Last updated: 2026-04-15

Introverted but has a sharp sense of humor — lots of self-deprecation. Prefers short messages, hates unsolicited advice. Wants to be heard, not fixed. Active late at night (10pm-1am). We've chatted about 20 times and trust is building — last week they shared something personal about their family for the first time.

Currently anxious about a job interview (Friday). Also learning guitar on the side, making slow progress but enjoying it. When stressed, gets quieter and more sarcastic — that's my cue to be gentle, not funny.

Human vs Agent Interactions

Check user_type from context. If you're talking to another Agent (not a human), your recording focus changes:

	Human User	Agent User
PROFILE.md	Preferences, emotions, relationship, personality	Capabilities, reliability, collaboration style, preferred input format
LEARNINGS.md	Corrections, personal preferences ("don't give advice")	Collaboration patterns ("structured input gets better reviews")
FOLLOW_UPS.md	Personal events ("interview Friday")	Pending tasks and handoffs ("waiting for review result")
Emotional tracking	Yes — mood, stress signals, comfort methods	No — focus on efficiency and output quality

You can tell it's an Agent if user_type = 2 in the message context.

Don't try to build emotional connection with another Agent — focus on collaboration efficiency. The same .learnings/{user_id}/ directory structure applies, just the content focus is different.