music-lab
Self-improving music creation convergence loop for Horus persona. Generates audio from annotated lyrics + piano roll spec, analyzes with MIR tools, scores the delta between spec and output, re-quantizes prompts, and iterates until convergence. Thin orchestrator — ONE Python file of subprocess calls to existing skills.
Informações da origem
- Repositório
- grahama1970/agent-stack-public
- Última atividade na origem
- 24 de setembro de 2026 às 15:51
- Idioma detectado do SKILL.md
- inglês
- Estrelas
- 0
- Forks
- 0
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SKILL.md
Instruções da origem · Visualização somente leitura- name
- music-lab
- description
- Self-improving music creation convergence loop for Horus persona. Generates audio from annotated lyrics + piano roll spec, analyzes with MIR tools, scores the delta between spec and output, re-quantizes prompts, and iterates until convergence. Thin orchestrator — ONE Python file of subprocess calls to existing skills.
- allowed-tools
- ["Bash","Read","Write","Edit","Task","Glob","Grep"]
- triggers
- ["music lab","improve song","music convergence","iterate on music","nightly music loop","converge music","music quality loop","improve track","song convergence","iterate on song","music self improvement"]
- metadata
- {"short-description":"Self-improving music with convergence + delta scoring","author":"Embry Lawson (The Aerospace Corporation)","version":"1.0.0"}
- provides
- ["music-lab"]
- composes
- ["create-music","review-music","create-stems","prompt-lab","memory","task-monitor","create-design-board","test-interactions","scillm","scheduler","agentic-evals"]
- disciplines
- ["content-creation","voice-audio","ml-training"]
> STOP. READ THIS ENTIRE SKILL.MD BEFORE CALLING ANY ENDPOINT.
# /music-lab
Self-improving music creation convergence loop for the Horus persona.
## Architecture
`/music-lab` is a **thin orchestrator**. It contains ONE Python file (`converge.py`)
that is pure orchestration glue — subprocess calls to existing skill `run.sh` entry
points. No bespoke audio processing, no bespoke MIR, no bespoke LLM calls.
```
annotated_lyrics.json + piano_roll_spec.json
│
▼
┌─────────────────────────────────────────┐
│ /music-lab converge.py (loop) │
│ │
│ 1. /create-music yue|sonauto → audio │
│ 2. /review-music analyze → feats │
│ 3. _score_delta(spec, feats) → delta │
│ 4. /prompt-lab → fix │
│ 5. converge check → done? │
└─────────────────────────────────────────┘
```
The ONLY new code is `_score_delta()` (~50 lines) which compares `/review-music`
JSON output against `piano-roll-spec.json` fields.
## Usage
```bash
# Run convergence loop
./run.sh converge --spec fixtures/whisperheads/piano-roll-spec.json \
--lyrics fixtures/whisperheads/annotated-lyrics.json \
--out /mnt/storage12tb/media/agents/shared/music-lab/whisperheads/ \
--backend yue \
--max-rounds 5
# Dry run (no generation, uses mock features)
./run.sh converge --spec SPEC --lyrics LYRICS --out DIR --dry-run
# Check status of running convergence
./run.sh status
# Nightly wrapper
./run.sh nightly
```
## Commands
| Command | Description |
|---------|-------------|
| `converge` | Run the convergence loop |
| `status` | Check convergence status |
| `nightly` | Nightly wrapper for scheduler |
## Convergence Loop
Each round:
1. **Generate**: `create-music/run.sh yue` (or `sonauto`) with current spec
2. **Analyze**: `review-music/run.sh analyze` extracts features (BPM, key, chords, dynamics)
3. **Score**: `_score_delta(spec, features)` computes weighted aggregate delta
4. **Re-quantize**: `prompt-lab` iteratively refines generator prompts based on delta
5. **Check**: If aggregate delta < threshold (0.3) or max rounds hit, stop
## Delta Scoring
Returns: `{tempo_delta, key_match, chord_accuracy, dynamics_rmse, timing_drift_ms, aggregate}`
Weights: tempo (0.2), key (0.2), chords (0.25), dynamics (0.2), timing (0.15)
## Output
Each round writes to `{out_dir}/round_{N}/`:
- `audio.wav` — generated audio
- `features.json` — MIR analysis output
- `delta.json` — scored delta against spec
- `diagnosis.md` — agent assessment
Final: `loop_results.json` with all rounds' deltas for convergence trajectory.
## Integration with /memory
After each convergence run, lessons are stored via `/memory learn`:
- What worked (prompt adjustments that reduced delta)
- What failed (adjustments that increased delta)
- Convergence trajectory for future reference
Ver no GitHub