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skill-tuning

Universal skill diagnosis and optimization tool. Detect and fix skill execution issues including context explosion, long-tail forgetting, data flow disruption, and agent coordination failures. Supports Agy CLI for deep analysis. Triggers on "skill tuning", "tune skill", "skill diagnosis", "optimize skill", "skill debug".

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catlog22/maestro-flow
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2026년 10월 15일 14:35
감지된 SKILL.md 언어
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SKILL.md
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name
skill-tuning
disable-model-invocation
true
description
Universal skill diagnosis and optimization tool. Detect and fix skill execution issues including context explosion, long-tail forgetting, data flow disruption, and agent coordination failures. Supports Agy CLI for deep analysis. Triggers on "skill tuning", "tune skill", "skill diagnosis", "optimize skill", "skill debug".
allowed-tools
["Bash","Glob","Grep","Read","Write","followup_task","interrupt_agent","list_agents","request_user_input","send_message","spawn_agent","spawn_agents_on_csv","wait_agent"]
session-mode
run
version
0.5.92
contract
{"discovery":"self-described","consumes":[],"produces":[],"gates":{"entry":[],"exit":[]}}
<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 verification results ## Reference Documents | Document | Purpose | |----------|---------| | [specs/problem-taxonomy.md](specs/problem-taxonomy.md) | Classification + detection patterns | | [specs/tuning-strategies.md](specs/tuning-strategies.md) | Fix implementation guide | | [specs/dimension-mapping.md](specs/dimension-mapping.md) | Dimension ↔ Spec mapping | | [specs/quality-gates.md](specs/quality-gates.md) | Quality verification criteria | | [phases/orchestrator.md](phases/orchestrator.md) | Workflow orchestration | | [phases/state-schema.md](phases/state-schema.md) | State structure definition | | [phases/actions/](phases/actions/) | Individual action implementations |
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