Merge: skill reorganization + sub-category support

- Sub-category support in prompt_builder.py (backwards-compatible)
- Split mlops (40 skills) into 7 logical sub-categories
- Merged 8 singleton categories into logical parents
- Fixed 2 misplaced skills (code-review, ml-paper-writing)
This commit is contained in:
teknium1
2026-03-09 03:40:11 -07:00
217 changed files with 39 additions and 4 deletions

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@@ -195,6 +195,8 @@ 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
@@ -203,8 +205,13 @@ 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
@@ -215,9 +222,11 @@ 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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