lev-self
[WHAT] Self-improvement system transforming event streams into actionable system improvements [HOW] Analyze events.jsonl for patterns, generate proposals with confidence scores, human-approve, apply to skills/config/daemons [WHEN] Use when asking what lev can learn, proposing improvements, or conducting fail-forward root cause analysis [WHY] Enables proactive system evolution based on observed failures and patterns instead of reactive fixes Triggers: "what can lev learn", "propose improvements", "self-learn", "why did X fail", "improve lev", "fail-forward"
معلومات المصدر
- المستودع
- lev-os/agents
- آخر نشاط في المصدر
- ٧ مارس ٢٠٢٦ في ٠٠:١٤
- لغة SKILL.md المكتشفة
- الإنجليزية
- النجوم
- ٢٢
- التفرعات
- ٢
خيارات التثبيت
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
مراجعة ملفات المصدر
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
مستكشف الملفات
5 ملفاتعرض SKILL.md
SKILL.md
تعليمات المصدر · معاينة للقراءة فقط- name
- lev-self
- description
- [WHAT] Self-improvement system transforming event streams into actionable system improvements [HOW] Analyze events.jsonl for patterns, generate proposals with confidence scores, human-approve, apply to skills/config/daemons [WHEN] Use when asking what lev can learn, proposing improvements, or conducting fail-forward root cause analysis [WHY] Enables proactive system evolution based on observed failures and patterns instead of reactive fixes Triggers: "what can lev learn", "propose improvements", "self-learn", "why did X fail", "improve lev", "fail-forward"
- version
- 1.0.0
- storage
- {"events":"~/lev/.lev/events.jsonl","proposals":"~/.config/lev/proposals/","patterns":"~/.config/lev/patterns.jsonl"}
- lifecycle_integration
- {"stage":"all stages (meta-improvement)","input_artifact":"events.jsonl + patterns","output_artifact":"improvement-proposals.md"}
# Lev Self-Improvement
Transform event streams into actionable system improvements. Proactive learning (not just reactive fixes).
## Quick Reference
| Trigger | Action |
|---------|--------|
| "what can lev learn?" | Full analysis cycle |
| "propose improvements" | Generate proposals from patterns |
| "why did X fail?" | Root cause analysis (fail-forward) |
| "self-learn" | Automated improvement cycle |
## Architecture
```
events.jsonl → Analyze → Patterns → Proposals → Human Review → Apply
↑ │
└──────────── feedback loop ──────────────────┘
```
## Core Workflows
### 1. Event Analysis
```bash
lev learn analyze # Detect patterns in events.jsonl
lev learn analyze --since 24h # Last 24 hours only
```
See: `references/event-analysis.md`
### 2. Fail-Forward Protocol
When something fails, extract learning:
1. Capture: What happened? (exact error, context)
2. Root Cause: Why? (5 whys, dependencies)
3. Proposal: How to prevent? (skill patch, config change)
4. Confidence: How sure? (low/medium/high)
See: `references/fail-forward.md`
### 3. Proposal Workflow
```yaml
# ~/.config/lev/proposals/{id}.yaml
id: prop-abc123
type: skill-patch | config-change | new-workflow
target: ~/.claude/skills/lev/SKILL.md
confidence: 0.85
description: "Add timeout handling"
diff: |
+ timeout: 30s
status: pending | approved | rejected | applied
```
See: `references/proposals.md`
## Evolution Targets
| Target | Location | When |
|--------|----------|------|
| Skills | ~/.claude/skills/*/SKILL.md | Behavior improvements |
| Config | ~/lev/.lev/config.yaml | Settings optimization |
| Daemons | ~/.config/lev/daemons.yaml | Process tuning |
| Workflows | ~/lev/workflows/*.yaml | Pattern codification |
## Relationship to Other Skills
- **skill-evolver** (absorbed): Reactive skill fixes
- **lev** (sibling): Behavior definition (lev-self improves it)
- **bd** (integration): Track proposals as issues
## Storage Schema
```
~/.config/lev/
├── proposals/ # Pending proposals
│ └── {id}.yaml
├── memory/
│ ├── patterns.jsonl # Detected patterns
│ ├── proposals.jsonl # Proposal history
│ └── applied.jsonl # Applied changes + outcomes
└── patterns.jsonl # Active patterns for matching
```
## Confidence Thresholds
| Confidence | Action |
|------------|--------|
| ≥90% | Auto-apply (after notification) |
| 70-89% | Propose with recommendation |
| 50-69% | Propose, request review |
| <50% | Log only, don't propose |
See: `references/tracking-schema.md`
## CLI Quick Reference
```bash
# Analysis
lev learn analyze # Full event analysis
lev learn patterns # Show detected patterns
# Proposals
lev learn propose # Generate proposals from patterns
lev learn review # Interactive proposal review
lev learn apply <id> # Apply approved proposal
# Tracking
lev learn status # Show pending proposals
lev learn history # Show applied changes
```
## Human-in-the-Loop
**All changes require confirmation:**
1. Proposal generated → notification
2. Human reviews diff
3. Approve/reject/modify
4. Applied changes logged with outcome
**Rollback:**
```bash
lev learn rollback <id> # Revert applied proposal
```
---
*For detailed schemas and protocols, see references/*
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