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.
معلومات المصدر
- المستودع
- grahama1970/agent-stack-public
- آخر نشاط في المصدر
- ٢٤ سبتمبر ٢٠٢٦ في ١٥:٥١
- لغة SKILL.md المكتشفة
- الإنجليزية
- النجوم
- ٠
- التفرعات
- ٠
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
12 ملفاتعرض SKILL.md
SKILL.md
تعليمات المصدر · معاينة للقراءة فقط- 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
عرض على GitHub