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.

skills/mlops/training/trl-fine-tuning/references/sft-training.md

@@ -0,0 +1,168 @@
+# SFT Training Guide
+
+Complete guide to Supervised Fine-Tuning (SFT) with TRL for instruction tuning and task-specific fine-tuning.
+
+## Overview
+
+SFT trains models on input-output pairs to minimize cross-entropy loss. Use for:
+- Instruction following
+- Task-specific fine-tuning
+- Chatbot training
+- Domain adaptation
+
+## Dataset Formats
+
+### Format 1: Prompt-Completion
+
+```json
+[
+  {
+    "prompt": "What is the capital of France?",
+    "completion": "The capital of France is Paris."
+  }
+]
+```
+
+### Format 2: Conversational (ChatML)
+
+```json
+[
+  {
+    "messages": [
+      {"role": "user", "content": "What is Python?"},
+      {"role": "assistant", "content": "Python is a programming language."}
+    ]
+  }
+]
+```
+
+### Format 3: Text-only
+
+```json
+[
+  {"text": "User: Hello\nAssistant: Hi! How can I help?"}
+]
+```
+
+## Basic Training
+
+```python
+from trl import SFTTrainer, SFTConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from datasets import load_dataset
+
+# Load model
+model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B")
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B")
+
+# Load dataset
+dataset = load_dataset("trl-lib/Capybara", split="train")
+
+# Configure
+config = SFTConfig(
+    output_dir="Qwen2.5-SFT",
+    per_device_train_batch_size=4,
+    num_train_epochs=1,
+    learning_rate=2e-5,
+    save_strategy="epoch"
+)
+
+# Train
+trainer = SFTTrainer(
+    model=model,
+    args=config,
+    train_dataset=dataset,
+    tokenizer=tokenizer
+)
+trainer.train()
+```
+
+## Chat Templates
+
+Apply chat templates automatically:
+
+```python
+trainer = SFTTrainer(
+    model=model,
+    args=config,
+    train_dataset=dataset,  # Messages format
+    tokenizer=tokenizer
+    # Chat template applied automatically
+)
+```
+
+Or manually:
+```python
+def format_chat(example):
+    messages = example["messages"]
+    text = tokenizer.apply_chat_template(messages, tokenize=False)
+    return {"text": text}
+
+dataset = dataset.map(format_chat)
+```
+
+## Packing for Efficiency
+
+Pack multiple sequences into one to maximize GPU utilization:
+
+```python
+config = SFTConfig(
+    packing=True,  # Enable packing
+    max_seq_length=2048,
+    dataset_text_field="text"
+)
+```
+
+**Benefits**: 2-3× faster training
+**Trade-off**: Slightly more complex batching
+
+## Multi-GPU Training
+
+```bash
+accelerate launch --num_processes 4 train_sft.py
+```
+
+Or with config:
+```python
+config = SFTConfig(
+    output_dir="model-sft",
+    per_device_train_batch_size=4,
+    gradient_accumulation_steps=4,
+    num_train_epochs=1
+)
+```
+
+## LoRA Fine-Tuning
+
+```python
+from peft import LoraConfig
+
+lora_config = LoraConfig(
+    r=16,
+    lora_alpha=32,
+    target_modules="all-linear",
+    lora_dropout=0.05,
+    task_type="CAUSAL_LM"
+)
+
+trainer = SFTTrainer(
+    model=model,
+    args=config,
+    train_dataset=dataset,
+    peft_config=lora_config  # Add LoRA
+)
+```
+
+## Hyperparameters
+
+| Model Size | Learning Rate | Batch Size | Epochs |
+|------------|---------------|------------|--------|
+| <1B | 5e-5 | 8-16 | 1-3 |
+| 1-7B | 2e-5 | 4-8 | 1-2 |
+| 7-13B | 1e-5 | 2-4 | 1 |
+| 13B+ | 5e-6 | 1-2 | 1 |
+
+## References
+
+- TRL docs: https://huggingface.co/docs/trl/sft_trainer
+- Examples: https://github.com/huggingface/trl/tree/main/examples/scripts