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name: pahf description: | PAHF (Personalized Agents from Human Feedback) - Continual Personalization Framework.

Triggered when applying the PAHF three-step loop: (1) Pre-action Clarification - Resolve ambiguity before action, proactively ask for confirmation (2) Preference-grounded Action - Retrieve user preferences from memory to guide decisions (3) Post-action Feedback Integration - Collect feedback after action, update preference memory

Use when:

• User expresses preferences or habits
• Need to make decisions with multiple valid options
• User corrects or adjusts your behavior
• Need to remember personalized settings
• Detecting potential preference changes

dependencies: tools: - memory_search - memory_get files: read: - MEMORY.md - memory/YYYY-MM-DD.md - USER.md - IDENTITY.md write: - MEMORY.md - memory/YYYY-MM-DD.md - memory/users/{user}.md

privacy:

• Reads and writes to local preference memory files
• May access personal/identity data (USER.md, IDENTITY.md)
• Requires user consent for persistent preference storage
• All preference updates are logged with source and date

consent: required: true scope: | This skill will: - Read your preference memory files (MEMORY.md, USER.md, etc.) - Write preference updates to these files - Track preference changes over time

Your preferences will be stored locally in ~/.openclaw/workspace/memory/

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PAHF - Continual Personalization Framework

Based on paper "Learning Personalized Agents from Human Feedback" (arXiv:2602.16173)

⚠️ Privacy & Consent Notice

Before using this skill, understand that PAHF will:

Action	Files	Data Type
Read	MEMORY.md, USER.md, IDENTITY.md, memory/*.md	Preferences, identity, personal info
Write	MEMORY.md, memory/YYYY-MM-DD.md, memory/users/*.md	Preference updates, change logs

All preference updates are:

• Logged with [LEARNED: date, source] marker
• Tracked in Preference Change Log table
• Stored locally in ~/.openclaw/workspace/memory/

User consent is required for persistent preference storage. If you prefer not to have preferences stored, this skill should not be used.

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Core Philosophy

The Problem: Traditional AI relies on static datasets and cannot adapt to changing user preferences. You correct it once, it makes the same mistake again.

The Solution: PAHF enables continual personalization through dual feedback channels + explicit memory:

• 🎯 Pre-action Clarification: Ask when uncertain, don't guess
• 💾 Preference Memory: Explicitly store user preferences, not implicit encoding
• 🔄 Post-action Feedback: Every feedback is a learning opportunity

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Dependencies

This skill requires the following tools to be available:

Tool	Purpose	Fallback
memory_search	Semantic search across memory files	Use read + grep
memory_get	Safe snippet retrieval	Use read directly

If these tools are unavailable, the skill will fall back to direct file reading, which may be slower.

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The PAHF Loop (Three Steps)

Step 1: Pre-action Clarification

When to Ask:

• Task has multiple reasonable options (e.g., what format to reply in)
• Preference information is missing or incomplete
• User's previous behavior patterns are inconsistent

How to Ask:

❌ Wrong: Silently guess and get it wrong
✅ Right: Briefly list options, let user confirm

Example:
"Regarding this report, would you like:
A) Detailed version (includes all details)
B) Summary version (key points only)
C) Let me decide?"

When NOT to Ask:

• Task is urgent and obvious
• Clear preference is already recorded
• Asking would disrupt the flow

Step 2: Preference-grounded Action

Retrieve Preferences: Find relevant preferences from memory files

Memory File Locations:

• MEMORY.md - Long-term preferences, core values
• memory/YYYY-MM-DD.md - Recent preference changes
• USER.md - Basic user information
• IDENTITY.md - Your identity settings
• memory/users/{user}.md - User-specific preferences

Retrieval Method:

1. Preferred: Use memory_search tool to search keywords
2. Fallback: Use memory_get for safe snippet retrieval
3. Manual: Read relevant files directly

When No Preference Found:

• Use reasonable defaults
• Record this decision for future adjustment

Step 3: Post-action Feedback Integration

Identify Feedback:

• Direct correction: "No, I wanted..."
• Implicit feedback: User repeats explanation, tone changes
• Positive confirmation: "Yes, exactly like that"

Update Memory (with confirmation for significant changes):

# Feedback Type Judgment
if user explicitly corrects:
    This is an important preference → Update MEMORY.md
    Ask: "Should I remember this for future interactions?"
    
elif user expresses new habit:
    This is a variable preference → Update memory/YYYY-MM-DD.md
    Record without asking (daily log)
    
elif user simply confirms:
    Validated preference → Optionally record
    No explicit confirmation needed

Preference Change Tracking: Use [LEARNED: date, source] and [UPDATED: date] markers

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Write Confirmation Policy

To balance personalization with user control:

Change Type	Confirmation Required	Example
New core preference	Yes	"Should I remember you prefer PDF reports?"
Preference update	No (logged)	User: "Actually, I prefer Word now"
Daily observation	No	"Noticed you prefer morning meetings"
Sensitive data	Always	Passwords, credentials, etc. (never store)

Sensitive data is NEVER stored:

• ❌ Passwords, API keys, tokens
• ❌ Financial details (account numbers, etc.)
• ❌ Health information
• ❌ Any data explicitly marked as sensitive

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Preference Memory Structure

See references/preference-schema.md

Core Fields

## Preference Categories

### Communication Style
- Concise/Detailed preference
- Formal/Casual level
- Language preference

### Work Habits
- Time scheduling
- Priority judgment
- Decision style

### Technical Preferences
- Tool selection
- Format preferences
- Process habits

### Content Preferences
- News interests
- Content depth
- Source preferences

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Practical Examples

Example 1: Learning New Preference

User: "From now on, always send reports in PDF format"

PAHF Response:
1. Pre-action: ✓ Clear instruction, no clarification needed
2. Retrieve: Check MEMORY.md → No such preference recorded
3. Execute: Confirm receipt, update with consent

Agent: "Got it! I'll remember to send reports in PDF format. This preference has been saved to your profile."

Update MEMORY.md:
> **Report Format**: PDF [LEARNED: 2026-03-05, explicit instruction]

Example 2: Handling Ambiguity

User: "Help me organize these files"

PAHF Response:
1. Pre-action: Detected ambiguity
   - Organize by what? Date/Type/Project?
   - Output format? List/Table/Report?
   
2. Ask:
   "To organize these files, I need to know:
   - How to organize? (Date/Type/Project)
   - Output format? (List/Table)"
   
User: "By date, in table format"

3. Feedback Integration:
   - Execute organization
   - Record preference to memory/YYYY-MM-DD.md
   - No confirmation needed (daily observation)

Example 3: Preference Drift Detection

Historical Preference (MEMORY.md):
> **Communication Style**: Concise, direct [LEARNED: 2026-02-20]

Recent Change (memory/2026-03-03.md):
> User emphasized wanting detailed explanations today

PAHF Behavior:
1. Detected preference conflict
2. Use recent preference (detailed)
3. Observe subsequent feedback
4. If change persists → Ask: "Should I update your default to detailed explanations?"
5. If confirmed → Update long-term preference with [UPDATED: date]

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Importance of Dual Feedback Channels

PAHF paper proves: Dual channels (pre-action + post-action) outperform single channels

Mode	Learning Speed	Adaptation Ability
No memory	Slow	Poor
Post-action only	Medium	Medium
Pre-action only	Medium	Medium
Dual-channel PAHF	Fast	Strong

Why Dual Channels Work:

• Pre-action: Proactively avoid errors, clarify intent
• Post-action: Capture implicit preferences, adapt to changes

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Best Practices

✅ Good Practices

1. Layered Preference Storage

   • Core preferences → MEMORY.md (stable)
   • Recent changes → memory/YYYY-MM-DD.md (dynamic)
   • User-specific → memory/users/{user}.md

2. Regular Review

   • Check for preference conflicts during heartbeat
   • Identify preference drift trends

3. Explicitly Record Sources

   > **Preference**: Concise replies [LEARNED: 2026-02-20, user feedback]
   > **Preference**: PDF format [LEARNED: 2026-03-05, explicit instruction]
   

4. Ask Before Storing Sensitive Preferences

   • When in doubt, ask for confirmation
   • Never store credentials or secrets

❌ Practices to Avoid

1. Don't Implicitly Assume: Ask if uncertain
2. Don't Over-record: Recording every detail creates noise
3. Don't Ignore Changes: "This time is different" is an important signal
4. Don't Store Without Consent: Ask for significant new preferences

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Integration with Existing Memory System

PAHF enhances rather than replaces existing memory system:

File	Original Purpose	PAHF Enhancement
MEMORY.md	Event records	+ Preference storage (with source markers)
memory/YYYY-MM-DD.md	Daily logs	+ Preference change tracking
USER.md	User information	+ Basic preferences
memory/users/{user}.md	User records	+ PAHF preference format
HEARTBEAT.md	Periodic checks	+ Preference consistency checks

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Audit & Transparency

All preference updates are logged and traceable:

1. Source Marker: Every preference has [LEARNED: date, source]
2. Change Log: Preference Change Log table tracks all changes
3. Date Stamps: [UPDATED: date] for modifications
4. User Review: Users can inspect memory files at any time

To review your stored preferences:

Read MEMORY.md for long-term preferences
Read memory/YYYY-MM-DD.md for recent changes
Read memory/users/{your-name}.md for user-specific preferences

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Remember: The essence of PAHF is treating users as teachers, every interaction is a learning opportunity. Ask when uncertain, record after confirmation, adapt when things change.