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refactor: reorganize skills into sub-categories

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The skills directory was getting disorganized — mlops alone had 40
skills in a flat list, and 12 categories were singletons with just
one skill each.

Code change:
- prompt_builder.py: Support sub-categories in skill scanner.
  skills/mlops/training/axolotl/SKILL.md now shows as category
  'mlops/training' instead of just 'mlops'. Backwards-compatible
  with existing flat structure.

Split mlops (40 skills) into 7 sub-categories:
- mlops/training (12): accelerate, axolotl, flash-attention,
  grpo-rl-training, peft, pytorch-fsdp, pytorch-lightning,
  simpo, slime, torchtitan, trl-fine-tuning, unsloth
- mlops/inference (8): gguf, guidance, instructor, llama-cpp,
  obliteratus, outlines, tensorrt-llm, vllm
- mlops/models (6): audiocraft, clip, llava, segment-anything,
  stable-diffusion, whisper
- mlops/vector-databases (4): chroma, faiss, pinecone, qdrant
- mlops/evaluation (5): huggingface-tokenizers,
  lm-evaluation-harness, nemo-curator, saelens, weights-and-biases
- mlops/cloud (2): lambda-labs, modal
- mlops/research (1): dspy

Merged singleton categories:
- gifs → media (gif-search joins youtube-content)
- music-creation → media (heartmula, songsee)
- diagramming → creative (excalidraw joins ascii-art)
- ocr-and-documents → productivity
- domain → research (domain-intel)
- feeds → research (blogwatcher)
- market-data → research (polymarket)

Fixed misplaced skills:
- mlops/code-review → software-development (not ML-specific)
- mlops/ml-paper-writing → research (academic writing)

Added DESCRIPTION.md files for all new/updated categories.
This commit is contained in:
teknium1 2026-03-09 03:35:53 -07:00
parent d6c710706f
commit 732c66b0f3
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skills/media/DESCRIPTION.md
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Media content extraction and transformation tools — YouTube transcripts, audio, video processing.
---
description: Skills for working with media content — YouTube transcripts, GIF search, music generation, and audio visualization.
---

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---
name: gif-search
description: Search and download GIFs from Tenor using curl. No dependencies beyond curl and jq. Useful for finding reaction GIFs, creating visual content, and sending GIFs in chat.
version: 1.0.0
author: Hermes Agent
license: MIT
metadata:
hermes:
tags: [GIF, Media, Search, Tenor, API]
---
# GIF Search (Tenor API)
Search and download GIFs directly via the Tenor API using curl. No extra tools needed.
## Prerequisites
- `curl` and `jq` (both standard on Linux)
## Search for GIFs
```bash
# Search and get GIF URLs
curl -s "https://tenor.googleapis.com/v2/search?q=thumbs+up&limit=5&key=AIzaSyAyimkuYQYF_FXVALexPuGQctUWRURdCYQ" | jq -r '.results[].media_formats.gif.url'
# Get smaller/preview versions
curl -s "https://tenor.googleapis.com/v2/search?q=nice+work&limit=3&key=AIzaSyAyimkuYQYF_FXVALexPuGQctUWRURdCYQ" | jq -r '.results[].media_formats.tinygif.url'
```
## Download a GIF
```bash
# Search and download the top result
URL=$(curl -s "https://tenor.googleapis.com/v2/search?q=celebration&limit=1&key=AIzaSyAyimkuYQYF_FXVALexPuGQctUWRURdCYQ" | jq -r '.results[0].media_formats.gif.url')
curl -sL "$URL" -o celebration.gif
```
## Get Full Metadata
```bash
curl -s "https://tenor.googleapis.com/v2/search?q=cat&limit=3&key=AIzaSyAyimkuYQYF_FXVALexPuGQctUWRURdCYQ" | jq '.results[] | {title: .title, url: .media_formats.gif.url, preview: .media_formats.tinygif.url, dimensions: .media_formats.gif.dims}'
```
## API Parameters
| Parameter | Description |
|-----------|-------------|
| `q` | Search query (URL-encode spaces as `+`) |
| `limit` | Max results (1-50, default 20) |
| `key` | API key (the one above is Tenor's public demo key) |
| `media_filter` | Filter formats: `gif`, `tinygif`, `mp4`, `tinymp4`, `webm` |
| `contentfilter` | Safety: `off`, `low`, `medium`, `high` |
| `locale` | Language: `en_US`, `es`, `fr`, etc. |
## Available Media Formats
Each result has multiple formats under `.media_formats`:
| Format | Use case |
|--------|----------|
| `gif` | Full quality GIF |
| `tinygif` | Small preview GIF |
| `mp4` | Video version (smaller file size) |
| `tinymp4` | Small preview video |
| `webm` | WebM video |
| `nanogif` | Tiny thumbnail |
## Notes
- The API key above is Tenor's public demo key — it works but has rate limits
- URL-encode the query: spaces as `+`, special chars as `%XX`
- For sending in chat, `tinygif` URLs are lighter weight
- GIF URLs can be used directly in markdown: `![alt](url)`

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---
name: heartmula
description: Set up and run HeartMuLa, the open-source music generation model family (Suno-like). Generates full songs from lyrics + tags with multilingual support.
version: 1.0.0
metadata:
hermes:
tags: [music, audio, generation, ai, heartmula, heartcodec, lyrics, songs]
related_skills: [audiocraft]
---
# HeartMuLa - Open-Source Music Generation
## Overview
HeartMuLa is a family of open-source music foundation models (Apache-2.0) that generates music conditioned on lyrics and tags. Comparable to Suno for open-source. Includes:
- **HeartMuLa** - Music language model (3B/7B) for generation from lyrics + tags
- **HeartCodec** - 12.5Hz music codec for high-fidelity audio reconstruction
- **HeartTranscriptor** - Whisper-based lyrics transcription
- **HeartCLAP** - Audio-text alignment model
## When to Use
- User wants to generate music/songs from text descriptions
- User wants an open-source Suno alternative
- User wants local/offline music generation
- User asks about HeartMuLa, heartlib, or AI music generation
## Hardware Requirements
- **Minimum**: 8GB VRAM with `--lazy_load true` (loads/unloads models sequentially)
- **Recommended**: 16GB+ VRAM for comfortable single-GPU usage
- **Multi-GPU**: Use `--mula_device cuda:0 --codec_device cuda:1` to split across GPUs
- 3B model with lazy_load peaks at ~6.2GB VRAM
## Installation Steps
### 1. Clone Repository
```bash
cd ~/ # or desired directory
git clone https://github.com/HeartMuLa/heartlib.git
cd heartlib
```
### 2. Create Virtual Environment (Python 3.10 required)
```bash
uv venv --python 3.10 .venv
. .venv/bin/activate
uv pip install -e .
```
### 3. Fix Dependency Compatibility Issues
**IMPORTANT**: As of Feb 2026, the pinned dependencies have conflicts with newer packages. Apply these fixes:
```bash
# Upgrade datasets (old version incompatible with current pyarrow)
uv pip install --upgrade datasets
# Upgrade transformers (needed for huggingface-hub 1.x compatibility)
uv pip install --upgrade transformers
```
### 4. Patch Source Code (Required for transformers 5.x)
**Patch 1 - RoPE cache fix** in `src/heartlib/heartmula/modeling_heartmula.py`:
In the `setup_caches` method of the `HeartMuLa` class, add RoPE reinitialization after the `reset_caches` try/except block and before the `with device:` block:
```python
# Re-initialize RoPE caches that were skipped during meta-device loading
from torchtune.models.llama3_1._position_embeddings import Llama3ScaledRoPE
for module in self.modules():
if isinstance(module, Llama3ScaledRoPE) and not module.is_cache_built:
module.rope_init()
module.to(device)
```
**Why**: `from_pretrained` creates model on meta device first; `Llama3ScaledRoPE.rope_init()` skips cache building on meta tensors, then never rebuilds after weights are loaded to real device.
**Patch 2 - HeartCodec loading fix** in `src/heartlib/pipelines/music_generation.py`:
Add `ignore_mismatched_sizes=True` to ALL `HeartCodec.from_pretrained()` calls (there are 2: the eager load in `__init__` and the lazy load in the `codec` property).
**Why**: VQ codebook `initted` buffers have shape `[1]` in checkpoint vs `[]` in model. Same data, just scalar vs 0-d tensor. Safe to ignore.
### 5. Download Model Checkpoints
```bash
cd heartlib # project root
hf download --local-dir './ckpt' 'HeartMuLa/HeartMuLaGen'
hf download --local-dir './ckpt/HeartMuLa-oss-3B' 'HeartMuLa/HeartMuLa-oss-3B-happy-new-year'
hf download --local-dir './ckpt/HeartCodec-oss' 'HeartMuLa/HeartCodec-oss-20260123'
```
All 3 can be downloaded in parallel. Total size is several GB.
## GPU / CUDA
HeartMuLa uses CUDA by default (`--mula_device cuda --codec_device cuda`). No extra setup needed if the user has an NVIDIA GPU with PyTorch CUDA support installed.
- The installed `torch==2.4.1` includes CUDA 12.1 support out of the box
- `torchtune` may report version `0.4.0+cpu` — this is just package metadata, it still uses CUDA via PyTorch
- To verify GPU is being used, look for "CUDA memory" lines in the output (e.g. "CUDA memory before unloading: 6.20 GB")
- **No GPU?** You can run on CPU with `--mula_device cpu --codec_device cpu`, but expect generation to be **extremely slow** (potentially 30-60+ minutes for a single song vs ~4 minutes on GPU). CPU mode also requires significant RAM (~12GB+ free). If the user has no NVIDIA GPU, recommend using a cloud GPU service (Google Colab free tier with T4, Lambda Labs, etc.) or the online demo at https://heartmula.github.io/ instead.
## Usage
### Basic Generation
```bash
cd heartlib
. .venv/bin/activate
python ./examples/run_music_generation.py \
--model_path=./ckpt \
--version="3B" \
--lyrics="./assets/lyrics.txt" \
--tags="./assets/tags.txt" \
--save_path="./assets/output.mp3" \
--lazy_load true
```
### Input Formatting
**Tags** (comma-separated, no spaces):
```
piano,happy,wedding,synthesizer,romantic
```
or
```
rock,energetic,guitar,drums,male-vocal
```
**Lyrics** (use bracketed structural tags):
```
[Intro]
[Verse]
Your lyrics here...
[Chorus]
Chorus lyrics...
[Bridge]
Bridge lyrics...
[Outro]
```
### Key Parameters
| Parameter | Default | Description |
|-----------|---------|-------------|
| `--max_audio_length_ms` | 240000 | Max length in ms (240s = 4 min) |
| `--topk` | 50 | Top-k sampling |
| `--temperature` | 1.0 | Sampling temperature |
| `--cfg_scale` | 1.5 | Classifier-free guidance scale |
| `--lazy_load` | false | Load/unload models on demand (saves VRAM) |
| `--mula_dtype` | bfloat16 | Dtype for HeartMuLa (bf16 recommended) |
| `--codec_dtype` | float32 | Dtype for HeartCodec (fp32 recommended for quality) |
### Performance
- RTF (Real-Time Factor) ≈ 1.0 — a 4-minute song takes ~4 minutes to generate
- Output: MP3, 48kHz stereo, 128kbps
## Pitfalls
1. **Do NOT use bf16 for HeartCodec** — degrades audio quality. Use fp32 (default).
2. **Tags may be ignored** — known issue (#90). Lyrics tend to dominate; experiment with tag ordering.
3. **Triton not available on macOS** — Linux/CUDA only for GPU acceleration.
4. **RTX 5080 incompatibility** reported in upstream issues.
5. The dependency pin conflicts require the manual upgrades and patches described above.
## Links
- Repo: https://github.com/HeartMuLa/heartlib
- Models: https://huggingface.co/HeartMuLa
- Paper: https://arxiv.org/abs/2601.10547
- License: Apache-2.0

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---
name: songsee
description: Generate spectrograms and audio feature visualizations (mel, chroma, MFCC, tempogram, etc.) from audio files via CLI. Useful for audio analysis, music production debugging, and visual documentation.
version: 1.0.0
author: community
license: MIT
metadata:
hermes:
tags: [Audio, Visualization, Spectrogram, Music, Analysis]
homepage: https://github.com/steipete/songsee
---
# songsee
Generate spectrograms and multi-panel audio feature visualizations from audio files.
## Prerequisites
Requires [Go](https://go.dev/doc/install):
```bash
go install github.com/steipete/songsee/cmd/songsee@latest
```
Optional: `ffmpeg` for formats beyond WAV/MP3.
## Quick Start
```bash
# Basic spectrogram
songsee track.mp3
# Save to specific file
songsee track.mp3 -o spectrogram.png
# Multi-panel visualization grid
songsee track.mp3 --viz spectrogram,mel,chroma,hpss,selfsim,loudness,tempogram,mfcc,flux
# Time slice (start at 12.5s, 8s duration)
songsee track.mp3 --start 12.5 --duration 8 -o slice.jpg
# From stdin
cat track.mp3 | songsee - --format png -o out.png
```
## Visualization Types
Use `--viz` with comma-separated values:
| Type | Description |
|------|-------------|
| `spectrogram` | Standard frequency spectrogram |
| `mel` | Mel-scaled spectrogram |
| `chroma` | Pitch class distribution |
| `hpss` | Harmonic/percussive separation |
| `selfsim` | Self-similarity matrix |
| `loudness` | Loudness over time |
| `tempogram` | Tempo estimation |
| `mfcc` | Mel-frequency cepstral coefficients |
| `flux` | Spectral flux (onset detection) |
Multiple `--viz` types render as a grid in a single image.
## Common Flags
| Flag | Description |
|------|-------------|
| `--viz` | Visualization types (comma-separated) |
| `--style` | Color palette: `classic`, `magma`, `inferno`, `viridis`, `gray` |
| `--width` / `--height` | Output image dimensions |
| `--window` / `--hop` | FFT window and hop size |
| `--min-freq` / `--max-freq` | Frequency range filter |
| `--start` / `--duration` | Time slice of the audio |
| `--format` | Output format: `jpg` or `png` |
| `-o` | Output file path |
## Notes
- WAV and MP3 are decoded natively; other formats require `ffmpeg`
- Output images can be inspected with `vision_analyze` for automated audio analysis
- Useful for comparing audio outputs, debugging synthesis, or documenting audio processing pipelines