name: openclaw-continuous-learning slug: openclaw-continuous-learning version: 1.2.1 description: | Instinct-based learning system for OpenClaw. Analyzes sessions, detects patterns, creates atomic learnings with confidence scoring, and suggests optimizations for self-evolution.

Works alongside agent-self-improvement for complete learning: internal session analysis + external user feedback.

Use when: you want your AI agent to learn from its own behavior, improve over time, discover optimization opportunities, or build a self-improving automation system.

Don't use when: static agent behavior is preferred. triggers:

continuous learning
self improving agent
agent evolution
pattern detection
session analysis
ai learning
agent optimization
automation improvement
self evolution metadata: openclaw: emoji: "🧠" requires: bins: ["node"]

Continuous Learning for AI Agents

An instinct-based learning system that helps AI agents improve themselves through observation and pattern detection.

What This Skill Does

Analyzes session history - Reviews agent interactions and outputs
Detects patterns - Identifies recurring behaviors, preferences, workflows
Creates instincts - Atomic learnings with confidence scores
Suggests optimizations - Based on observed behavior patterns
Enables self-evolution - Converts insights into improvements

When to Use

Use when:

Building self-improving AI agents
Want agent to learn from interactions
Discovering optimization opportunities
Creating adaptive automation
Tracking behavioral patterns

Skip when:

Static, unchanging behavior preferred
No session history available
Simple, deterministic workflows only

Architecture

~/.openclaw/agents/ (session .jsonl files)
        │
        ▼
┌───────────────────────────────────────────┐
│ analyze.mjs                                │
│ • Reads session history                   │
│ • Extracts tool calls & errors             │
│ • Detects patterns                         │
└───────────────────────────────────────────┘
        │
        ▼
┌───────────────────────────────────────────┐
│ memory/learning/                           │
│ • instincts.jsonl (atomic learnings)       │
│ • patterns.json (aggregated)              │
│ • optimizations.json (suggestions)         │
└───────────────────────────────────────────┘

External Feedback (Sub-Skill)

This skill works with agent-self-improvement (ClawHub) for external user feedback capture:

Internal Learning: Session analysis (this skill)
External Learning: User feedback via SKILL:agent-self-improvement

Combined Usage

# Nightly: Internal analysis
SKILL:openclaw-continuous-learning --analyze

# After any output: Capture feedback
SKILL:agent-self-improvement --job <task> --feedback "<user response>"

# Daily: Generate combined improvements
SKILL:agent-self-improvement --improve all

Feedback Flow

User Response → agent-self-improvement → Directive Hints
        ↓
Session Analysis → openclaw-continuous-learning → Internal Patterns
        ↓
Combined Insights → Agent Optimization

Both skills store learnings in memory/learning/ and can reference each other's data.

Confidence Scoring

Score	Meaning	Behavior
0.3	Tentative	Suggested but not enforced
0.5	Moderate	Applied when relevant
0.7	Strong	Auto-approved
0.9	Core behavior	Always apply

Confidence increases when:

Pattern observed repeatedly
User doesn't correct behavior
Multiple observations agree

Confidence decreases when:

User explicitly corrects
Pattern not observed recently
Contradicting evidence appears

Key Concepts

Instincts

An instinct is a small learned behavior:

id: prefer-simplicity
trigger: "when solving problems"
confidence: 0.7
domain: problem_solving
---
# Prefer Simple Solutions

## Action
Always choose the simplest solution that meets requirements.

## Evidence
- Observed preference for minimal code
- User corrected over-engineered approaches

Patterns

Aggregated observations grouped by category:

code_style
testing
git
debugging
workflow
communication

Optimizations

Actionable improvements derived from patterns.

Use Cases

1. Agent Self-Improvement

Agent observes its own sessions:
- What works consistently?
- What gets corrected?
- What patterns emerge?

Creates instincts → Applies high-confidence patterns

2. User Preference Learning

Learn user preferences from interactions:
- Coding style preferences
- Communication preferences
- Workflow preferences

Adapt behavior accordingly

3. Performance Optimization

Detect performance patterns:
- Slow operations
- Bottlenecks
- Optimization opportunities

Suggest improvements

4. Error Pattern Detection

Track error patterns:
- Common failures
- Resolution strategies
- Prevention approaches

Build error-handling instincts

Quick Start

# Analyze sessions (reads agent .jsonl files from ~/.openclaw/agents/)
cd ~/.openclaw/workspace/skills/openclaw-continuous-learning
node scripts/analyze.mjs

# List learned instincts
node scripts/analyze.mjs instincts

# Show optimizations
node scripts/analyze.mjs list

# Show error patterns
node scripts/analyze.mjs errors

Setup

1. Create storage directory

mkdir -p ~/.openclaw/workspace/memory/learning

2. Schedule analysis

Add to cron for periodic analysis:

{
  "id": "continuous-learning",
  "schedule": "0 22 * * *"
}

3. Integrate with daily tips

Connect to daily summary for optimization delivery.

File Structure

~/.openclaw/workspace/
└── memory/
    └── learning/
        ├── instincts.jsonl    # Atomic learnings
        ├── patterns.json      # Aggregated patterns
        └── optimizations.json # Suggestions

Example Output

🧠 Learning Report

Patterns Detected:
- prefer-simplicity (0.7) ↑2
- test-first (0.5) ↑1
- commit-often (0.3) new

Confidence Changes:
- minimal-code: 0.5 → 0.7

Suggested:
1. Prioritize simple solutions
2. Add pre-commit hooks
3. Enable stricter typing

Best Practices

Start simple - Few patterns, low confidence
Validate often - Check if patterns still hold
Review suggestions - Don't auto-apply everything
Track confidence - Update based on results
Export/share - Build library of common patterns

FAQ

How is this different from memory? Memory stores facts. This learns behavioral patterns and preferences.

How long to see results? Depends on session volume. Typically 1-2 weeks for meaningful patterns.

Is it safe to auto-apply? Only high-confidence (0.7+) patterns. Always review suggestions first.

Related Skills

skill-engineer - Quality-gated skill development
compound-engineering - Session review and learning
memory-setup - Memory configuration
openclaw-daily-tips - Daily optimization tips

Version: 1.1.0
Inspired by: Anthropic's continuous learning patterns, Claude Code homunculus

Openclaw Continuous Learning

Description