refactor: reorganize skills into sub-categories

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
217 changed files with 39 additions and 4 deletions

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@@ -186,6 +186,8 @@ def build_skills_system_prompt() -> str:
# Collect skills with descriptions, grouped by category
# Each entry: (skill_name, description)
# Supports sub-categories: skills/mlops/training/axolotl/SKILL.md
# → category "mlops/training", skill "axolotl"
skills_by_category: dict[str, list[tuple[str, str]]] = {}
for skill_file in skills_dir.rglob("SKILL.md"):
# Skip skills incompatible with the current OS platform
@@ -194,8 +196,13 @@ def build_skills_system_prompt() -> str:
rel_path = skill_file.relative_to(skills_dir)
parts = rel_path.parts
if len(parts) >= 2:
category = parts[0]
# Category is everything between skills_dir and the skill folder
# e.g. parts = ("mlops", "training", "axolotl", "SKILL.md")
# → category = "mlops/training", skill_name = "axolotl"
# e.g. parts = ("github", "github-auth", "SKILL.md")
# → category = "github", skill_name = "github-auth"
skill_name = parts[-2]
category = "/".join(parts[:-2]) if len(parts) > 2 else parts[0]
else:
category = "general"
skill_name = skill_file.parent.name
@@ -206,9 +213,11 @@ def build_skills_system_prompt() -> str:
return ""
# Read category-level descriptions from DESCRIPTION.md
# Checks both the exact category path and parent directories
category_descriptions = {}
for category in skills_by_category:
desc_file = skills_dir / category / "DESCRIPTION.md"
cat_path = Path(category)
desc_file = skills_dir / cat_path / "DESCRIPTION.md"
if desc_file.exists():
try:
content = desc_file.read_text(encoding="utf-8")

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@@ -0,0 +1,3 @@
---
description: Creative content generation — ASCII art, hand-drawn style diagrams, and visual design tools.
---

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@@ -1 +1,3 @@
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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@@ -0,0 +1,3 @@
---
description: GPU cloud providers and serverless compute platforms for ML workloads.
---

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@@ -0,0 +1,3 @@
---
description: Model evaluation benchmarks, experiment tracking, data curation, tokenizers, and interpretability tools.
---

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@@ -0,0 +1,3 @@
---
description: Model serving, quantization (GGUF/GPTQ), structured output, inference optimization, and model surgery tools for deploying and running LLMs.
---

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@@ -0,0 +1,3 @@
---
description: Specific model architectures and tools — computer vision (CLIP, SAM, Stable Diffusion), speech (Whisper), audio generation (AudioCraft), and multimodal models (LLaVA).
---

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@@ -0,0 +1,3 @@
---
description: ML research frameworks for building and optimizing AI systems with declarative programming.
---

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@@ -0,0 +1,3 @@
---
description: Fine-tuning, RLHF/DPO/GRPO training, distributed training frameworks, and optimization tools for training LLMs and other models.
---

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