Self-Improving Agent

A universal self-improvement system that learns from ALL skill experiences and continuously updates the codebase.

Overview

This agent learns from every skill interaction to achieve true lifelong learning. It implements a complete feedback loop with multi-memory architecture, self-correction, and evolution markers.

Key Features

Multi-Memory Architecture: Semantic + Episodic + Working memory

Universal Learning: Learns from ALL skills, not just PRDs

Pattern Extraction: Converts experiences into reusable patterns

Self-Correction: Fixes skill guidance when errors occur

Self-Validation: Periodically verifies skill accuracy

Automatic Updates: Updates related skills based on learned patterns

Confidence Tracking: Measures pattern reliability over time

Human-in-the-Loop: Collects feedback to validate improvements

Memory System

``` ~/.claude/memory/ ├── semantic/ # Patterns, rules, best practices ├── episodic/ # Specific experiences and episodes └── working/ # Current session context ```

How It Works

``` Any Skill Completes ↓ Extract Experience → Identify Patterns → Update Skills → Consolidate Memory ↓ ↓ ↓ ↓ What happened? What can we reuse? Which skills? Track metrics ```

Installation

```bash ln -s ~/path/to/agent-playbook/skills/self-improving-agent ~/.claude/skills/self-improving-agent ```

Hooks (Optional)

Wire hooks to capture errors and session-end signals:

```json { "hooks": { "PreToolUse": [ { "matcher": "Bash|Write|Edit", "hooks": [ { "type": "command", "command": "bash ${SKILLS_DIR}/self-improving-agent/hooks/pre-tool.sh \"$TOOL_NAME\" \"$TOOL_INPUT\"" } ] } ], "PostToolUse": [ { "matcher": "Bash", "hooks": [ { "type": "command", "command": "bash ${SKILLS_DIR}/self-improving-agent/hooks/post-bash.sh \"$TOOL_OUTPUT\" \"$EXIT_CODE\"" } ] } ], "Stop": [ { "matcher": "", "hooks": [ { "type": "command", "command": "bash ${SKILLS_DIR}/self-improving-agent/hooks/session-end.sh" } ] } ] } } ```

Triggering

Automatic

After ANY skill completes:

prd-planner

code-reviewer

debugger

refactoring-specialist

etc.

Manual

``` "自我进化" "self-improve" "分析今天的经验" "总结这次教训" ```

Example Learning

Episode

```yaml Skill: debugger Situation: Form submission doesn't refresh data Root Cause: Empty callback function Pattern: Always verify callbacks have implementations Confidence: 0.95 → Updates: debugger, prd-implementation-precheck ```

Skill Update

```markdown

Auto-Update (2025-01-11)

Pattern Added

Callback Verification: Always verify that callback functions passed as props are not empty and actually execute logic. Source: Episode ep-2025-01-11-003 (3 occurrences) Action: Added to debugger checklist ```

Research Basis

[SimpleMem: Efficient Lifelong Memory](https://arxiv.org/html/2601.02553v1)

[ACM Memory Mechanisms Survey](https://dl.acm.org/doi/10.1145/3748302)

[Lifelong Learning of LLM Agents](https://arxiv.org/html/2501.07278v1)

Templates

Reusable templates live in `skills/self-improving-agent/templates`:

`pattern-template.md`

`correction-template.md`

`validation-template.md`

License

MIT