self-improve
Apply learned improvements to the Aura Frog plugin. Updates rules, adjusts agent routing, modifies workflow configurations, and generates knowledge base entries.
Best use case
self-improve is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Apply learned improvements to the Aura Frog plugin. Updates rules, adjusts agent routing, modifies workflow configurations, and generates knowledge base entries.
Teams using self-improve should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/self-improve/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How self-improve Compares
| Feature / Agent | self-improve | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Apply learned improvements to the Aura Frog plugin. Updates rules, adjusts agent routing, modifies workflow configurations, and generates knowledge base entries.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
> **AI-consumed reference.** Optimized for Claude to read during execution.
> Human-readable explanation: see [docs/architecture/HIERARCHICAL_PLANNING.md](../../../docs/architecture/HIERARCHICAL_PLANNING.md)
> or [docs/getting-started/](../../../docs/getting-started/) depending on topic.
# Self-Improve Skill
Apply learned improvements: update rules, adjust agent routing, modify workflow configs, generate knowledge entries.
---
## Usage
```bash
/af learn apply # Review and apply pending
/af learn apply --auto # Auto-apply high-confidence (>=0.8)
/af learn apply --preview # Preview without applying
/af learn apply --id <pattern_id> # Apply specific pattern
```
---
## Improvement Types
```toon
types[4]{type,target,example}:
Rule updates,rules/*.md,Increase coverage threshold 80→85
Agent routing,agent-detector config,Default react-expert for .tsx
Workflow adjustments,workflow config,Increase Phase 2 timeout
Knowledge base,knowledge entries,TDD reduces bugs by 40% for APIs
```
---
## Safety Guards
**Approval required** unless: `--auto` AND confidence >= 0.8 AND frequency >= 5.
**Rollback:** Every change creates backup + log. `/af learn rollback <id>` or `--all`.
**Validation:** Syntax check, conflict detection, impact assessment before applying.
---
## Apply Process
1. **Fetch:** Query `v_improvement_suggestions WHERE applied = FALSE`
2. **Generate:** Determine target files, create modifications, calculate impact
3. **Review:** Present diff with confidence, frequency, evidence. User chooses: Apply / Skip / Modify
4. **Apply:** Create backup, apply modification, mark applied in Supabase, log change
---
## Rollback
```bash
/af learn rollback <change_id> # Specific change
/af learn rollback --list # List recent changes
/af learn rollback --all # All changes from today
```
---
## Configuration
```yaml
learning:
self_improve:
enabled: true
auto_apply_threshold: 0.8
min_frequency: 5
backup_dir: backups/
max_auto_per_day: 10
```
---