skill-tuning

<required_reading>
@~/.maestro/workflows/run-mode.md
@~/.maestro/workflows/codex-run-mode.md
</required_reading>

# Skill Tuning

Autonomous diagnosis and optimization for skill execution issues.

## Pre-load (before execution)

1. **Codebase docs**: If `.workflow/codebase/ARCHITECTURE.md` exists, read for project context
2. **Specs**: `maestro load --type spec --category coding` — load coding conventions
3. **Wiki knowledge**: `maestro search "skill design optimization" --json` — top 5 entries as prior context
4. All optional — proceed without if unavailable

## Architecture

```
┌─────────────────────────────────────────────────────┐
│  Phase 0: Read Specs (mandatory)                    │
│  → problem-taxonomy.md, tuning-strategies.md         │
└─────────────────────────────────────────────────────┘
                        ↓
┌─────────────────────────────────────────────────────┐
│  Orchestrator (state-driven)                         │
│  Read state → Select action → Execute → Update → ✓ │
└─────────────────────────────────────────────────────┘
        ↓                           ↓
┌──────────────────────┐   ┌──────────────────┐
│  Diagnosis Phase     │   │ Agy CLI       │
│  • Context          │   │ Deep analysis    │
│  • Memory           │   │ (on-demand)      │
│  • DataFlow         │   │                  │
│  • Agent            │   │ Complex issues   │
│  • Docs             │   │ Architecture     │
│  • Token Usage      │   │ Performance      │
└──────────────────────┘   └──────────────────┘
                ↓
        ┌───────────────────┐
        │  Fix & Verify     │
        │  Apply → Re-test  │
        └───────────────────┘
```

## Core Issues Detected

| Priority | Problem | Root Cause | Fix Strategy |
|----------|---------|-----------|--------------|
| **P0** | Authoring Violation | Intermediate files, state bloat, file relay | eliminate_intermediate, minimize_state |
| **P1** | Data Flow Disruption | Scattered state, inconsistent formats | state_centralization, schema_enforcement |
| **P2** | Agent Coordination | Fragile chains, no error handling | error_wrapping, result_validation |
| **P3** | Context Explosion | Unbounded history, full content passing | sliding_window, path_reference |
| **P4** | Long-tail Forgetting | Early constraint loss | constraint_injection, checkpoint_restore |
| **P5** | Token Consumption | Verbose prompts, state bloat | prompt_compression, lazy_loading |

## Problem Categories (Detailed Specs)

See [specs/problem-taxonomy.md](specs/problem-taxonomy.md) for:
- Detection patterns (regex/checks)
- Severity calculations
- Impact assessments

## Tuning Strategies (Detailed Specs)

See [specs/tuning-strategies.md](specs/tuning-strategies.md) for:
- 10+ strategies per category
- Implementation patterns
- Verification methods

## Workflow

| Step | Action | Orchestrator Decision | Output |
|------|--------|----------------------|--------|
| 1 | `action-init` | status='pending' | Backup, session created |
| 2 | `action-analyze-requirements` | After init | Required dimensions + coverage |
| 3 | Diagnosis (6 types) | Focus areas | state.diagnosis.{type} |
| 4 | `action-agy-analysis` | Critical issues OR user request | Deep findings |
| 5 | `action-generate-report` | All diagnosis complete | state.final_report |
| 6 | `action-propose-fixes` | Issues found | state.proposed_fixes[] |
| 7 | `action-apply-fix` | Pending fixes | Applied + verified |
| 8 | `action-complete` | Quality gates pass | session.status='completed' |

## Action Reference

| Category | Actions | Purpose |
|----------|---------|---------|
| **Setup** | action-init | Initialize backup, session state |
| **Analysis** | action-analyze-requirements | Decompose user request via Agy CLI |
| **Diagnosis** | action-diagnose-{context,memory,dataflow,agent,docs,token_consumption} | Detect category-specific issues |
| **Deep Analysis** | action-agy-analysis | Agy CLI: complex/critical issues |
| **Reporting** | action-generate-report | Consolidate findings → final_report |
| **Fixing** | action-propose-fixes, action-apply-fix | Generate + apply fixes |
| **Verify** | action-verify | Re-run diagnosis, check gates |
| **Exit** | action-complete, action-abort | Finalize or rollback |

Full action details: [phases/actions/](phases/actions/)

## State Management

**Single source of truth**: `{run_dir}/outputs/skill-tuning-{ts}/state.json`

```json
{
  "status": "pending|running|completed|failed",
  "target_skill": { "name": "...", "path": "..." },
  "diagnosis": {
    "context": {...},
    "memory": {...},
    "dataflow": {...},
    "agent": {...},
    "docs": {...},
    "token_consumption": {...}
  },
  "issues": [{"id":"...", "severity":"...", "category":"...", "strategy":"..."}],
  "proposed_fixes": [...],
  "applied_fixes": [...],
  "quality_gate": "pass|fail",
  "final_report": "..."
}
```

See [phases/state-schema.md](phases/state-schema.md) for complete schema.

## Orchestrator Logic

See [phases/orchestrator.md](phases/orchestrator.md) for:
- Decision logic (termination checks → action selection)
- State transitions
- Error recovery

## Key Principles

1. **Problem-First**: Diagnosis before any fix
2. **Data-Driven**: Record traces, token counts, snapshots
3. **Iterative**: Multiple rounds until quality gates pass
4. **Reversible**: All changes with backup checkpoints
5. **Non-Invasive**: Minimal changes, maximum clarity

## Usage Examples

```bash
# Basic skill diagnosis
/skill-tuning "Fix memory leaks in my skill"

# Deep analysis with Agy
/skill-tuning "Architecture issues in async workflow"

# Focus on specific areas
/skill-tuning "Optimize token consumption and fix agent coordination"

# Custom issue
/skill-tuning "My skill produces inconsistent outputs"
```

## Output

After completion, review:
- `{run_dir}/outputs/skill-tuning-{ts}/state.json` - Full state with final_report
- `state.final_report` - Markdown summary (in state.json)
- `state.applied_fixes` - List of applied fixes with verificat