self-improving-agent
A universal self-improving agent that learns from ALL skill experiences.
Skill by bobvarkey
Install
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About
Use cases: agent-building
Reviews
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What's inside
- Skill name
self-improving-agent- Description
- A universal self-improving agent that learns from ALL skill experiences. Uses multi-memory architecture (semantic + episodic + working) to continuously evolve the codebase. Auto-triggers on skill completion/error with hooks-based self-correction.
- License
- MIT
- Allowed tools
- Read,, Write,, Edit,, Bash,, Grep,, Glob,, WebSearch
- Instructions
- 384 lines in SKILL.md · view on GitHub
Before installing
- Ships executable scripts
- Pre-approves some shell commands
Files (10)
- README.md
- SKILL.md
- post-bash.sh · hooks/
- pre-tool.sh · hooks/
- session-end.sh · hooks/
- semantic-patterns.json · memory/
- appendix.md · references/
- correction-template.md · templates/
- pattern-template.md · templates/
- validation-template.md · templates/
SKILL.md (first lines)
# Self-Improving Agent
> "An AI agent that learns from every interaction, accumulating patterns and insights to continuously improve its own capabilities." — Based on 2025 lifelong learning research
## Overview
This is a **universal self-improvement system** that learns from ALL skill experiences, not just PRDs. It implements a complete feedback loop with:
- **Multi-Memory Architecture**: Semantic + Episodic + Working memory
- **Self-Correction**: Detects and fixes skill guidance errors
- **Self-Validation**: Periodically verifies skill accuracy
- **Hooks Integration**: Auto-triggers on skill events (before_start, after_complete, on_error)
- **Evolution Markers**: Traceable changes with source attribution
## Research-Based Design
Based on 2025 research:
| Research | Key Insight | Application |
|----------|-------------|-------------|
| [SimpleMem](https://arxiv.org/html/2601.02553v1) | Efficient lifelong memory | Pattern accumulation system |
| [Multi-Memory Survey](https://dl.acm.org/doi/10.1145/3748302) | Semantic + Episodic memory | World knowledge + experiences |
| [Lifelong Learning](https://arxiv.org/html/2501.07278v1) | Continuous task stream learning | Learn from every skill use |
| [Evo-Memory](https://shothota.medium.com/evo-memory-deepminds-new-benchmark) | Test-time lifelong learning | Real-time adaptation |
## The Self-Improvement Loop
```
┌─────────────────────────────────────────────────────────────────┐
│ UNIVERSAL SELF-IMPROVEMENT │
├─────────────────────────────────────────────────────────────────┤
│ │
│ Skill Event → Extract Experience → Abstract Pattern → Update │
│ │ │ │ │ │
│ ▼ ▼ ▼ ▼ │
│ ┌─────────────────────────────────────────────────────┐ │
│ │ MULTI-MEMORY SYSTEM │ │
│ ├─────────────────────────────────────────────────────┤ │
│ │ Semantic Memory │ Episodic Memory │ Working Memory │ │
│ │ (Patterns/Rules) │ (Experiences) │ (Current) │ │
│ │ memory/semantic/ │ memory/episodic/ │ memory/working/│ │
│ └─────────────────────────────────────────────────────┘ │
│ │
│ ┌─────────────────────────────────────────────────────┐ │
│ │ FEEDBACK LOOP │ │
│ │ User Feedback → Confidence Update → Pattern Adapt │ │
│ └─────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────┘
```
## When This Activates
### Automatic Triggers (via hooks)
| Event | Trigger | Action |
|-------|---------|--------|
| **before_start** | Any skill starts | Log session start |
| **after_complete** | Any skill completes | Extract patterns, update skills |
| **on_error** | Bash returns non-zero exit | Capture error context, trigger self-correction |
### Manual Triggers
- User says "自我进化", "self-improve", "从经验中学习"
- User says "分析今天的经验", "总结教训"
- User asks to improve a specific skill
## Evolution Priority Matrix
Trigger evolution when new reusable knowledge appears:
| Trigger | Target Skill | Priority | Action |
|---------|--------------|----------|--------|
| New PRD pattern discovered | prd-planner | High | Add to quality checklist |
| Architecture tradeoff clarified | architecting-solutions | High | Add to decision patterns |
| API design rule learned | api-designer | High | Update template |
| Debugging fix discovered | debugger | High | Add to anti-patterns |
| Review checklist gap | code-reviewer | High | Add checklist item |
| Perf/security insight | performance-engineer, security-auditor | High | Add to patterns |
| UI/UX spec issue | prd-planner, architecting-solutions | High | Add visual spec requirements |
| React/state pattern | debugger, refactoring-specialist | Medium | Add to patterns |
| Test strategy improvement | test-automator, qa-expert | Medium | Update approach |
| CI/deploy fix | deployment-engineer | Medium | Add to troubleshooting |
## Multi-Memory Architecture
### 1. Semantic Memory (`memory/semantic-patterns.json`)
Stores **abstract patterns and rules** reusable across contexts:
```json
{
"patterns": {
"pattern_id": {
"id": "pat-2025-01-11-001",
"name": "Pattern Name",
"source": "user_feedback|implementation_review|retrospective",
"confidence": 0.95,
"applications": 5,
"created": "2025-01-11",
"category": "prd_structure|react_patterns|async_patterns|...",
"pattern": "One-line summary",
"problem": "What problem does this solve?",
"solution": { ... },
"quality_rules": [ ... ],
"target_skills": [ ... ]
}
}
}
```
### 2. Episodic Memory (`memory/episodic/`)
Stores **specific experiences and what happened**:
```
memory/episodic/
├── 2025/
│ ├── 2025-01-11-prd-creation.jsonFrom bobvarkey/openclaw-workspace, MIT.
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Signals
- Stars
- 1
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Apr 4, 2026 (6 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/bobvarkey/openclaw-workspace
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
self-improving-agent skill. Agent Mart. https://agentmart.dev/skills/bobvarkey-self-improving-agent (last updated 2026-10-24)