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/torchtitan/references/checkpoint.md

@@ -0,0 +1,181 @@
+# Checkpointing in TorchTitan
+
+TorchTitan uses PyTorch Distributed Checkpoint (DCP) for fault-tolerant, interoperable checkpointing.
+
+## Basic Configuration
+
+```toml
+[checkpoint]
+enable = true
+folder = "checkpoint"
+interval = 500
+```
+
+## Save Model Only (Smaller Checkpoints)
+
+Exclude optimizer state and training metadata:
+
+```toml
+[checkpoint]
+enable = true
+last_save_model_only = true
+export_dtype = "bfloat16"  # Optional: export in lower precision
+```
+
+## Excluding Keys from Loading
+
+Partial checkpoint loading for modified settings:
+
+```toml
+[checkpoint]
+enable = true
+exclude_from_loading = ["data_loader", "lr_scheduler"]
+```
+
+CLI equivalent:
+```bash
+--checkpoint.exclude_from_loading data_loader,lr_scheduler
+```
+
+## Creating Seed Checkpoints
+
+Required for Pipeline Parallelism to ensure consistent initialization:
+
+```bash
+NGPU=1 CONFIG_FILE=<path_to_config> ./run_train.sh \
+  --checkpoint.enable \
+  --checkpoint.create_seed_checkpoint \
+  --parallelism.data_parallel_replicate_degree 1 \
+  --parallelism.data_parallel_shard_degree 1 \
+  --parallelism.tensor_parallel_degree 1 \
+  --parallelism.pipeline_parallel_degree 1 \
+  --parallelism.context_parallel_degree 1 \
+  --parallelism.expert_parallel_degree 1
+```
+
+This initializes on single CPU for reproducible initialization across any GPU count.
+
+## Async Checkpointing
+
+Reduce checkpoint overhead with async writes:
+
+```toml
+[checkpoint]
+enable = true
+async_mode = "async"  # Options: "disabled", "async", "async_with_pinned_mem"
+```
+
+## HuggingFace Conversion
+
+### During Training
+
+Save directly in HuggingFace format:
+
+```toml
+[checkpoint]
+last_save_in_hf = true
+last_save_model_only = true
+```
+
+Load from HuggingFace:
+
+```toml
+[checkpoint]
+initial_load_in_hf = true
+
+[model]
+hf_assets_path = "./path/to/hf/checkpoint"
+```
+
+### Offline Conversion
+
+Convert without running training:
+
+```bash
+# HuggingFace -> TorchTitan
+python ./scripts/checkpoint_conversion/convert_from_hf.py \
+  <input_dir> <output_dir> \
+  --model_name llama3 \
+  --model_flavor 8B
+
+# TorchTitan -> HuggingFace
+python ./scripts/checkpoint_conversion/convert_to_hf.py \
+  <input_dir> <output_dir> \
+  --hf_assets_path ./assets/hf/Llama3.1-8B \
+  --model_name llama3 \
+  --model_flavor 8B
+```
+
+### Example
+
+```bash
+python ./scripts/convert_from_hf.py \
+  ~/.cache/huggingface/hub/models--meta-llama--Meta-Llama-3-8B/snapshots/8cde5ca8380496c9a6cc7ef3a8b46a0372a1d920/ \
+  ./initial_load_path/ \
+  --model_name llama3 \
+  --model_flavor 8B
+```
+
+## Converting to Single .pt File
+
+Convert DCP sharded checkpoint to single PyTorch file:
+
+```bash
+python -m torch.distributed.checkpoint.format_utils \
+  dcp_to_torch \
+  torchtitan/outputs/checkpoint/step-1000 \
+  checkpoint.pt
+```
+
+## Checkpoint Structure
+
+DCP saves sharded checkpoints that can be resharded for different parallelism configurations:
+
+```
+checkpoint/
+├── step-500/
+│   ├── .metadata
+│   ├── __0_0.distcp
+│   ├── __0_1.distcp
+│   └── ...
+└── step-1000/
+    └── ...
+```
+
+## Resume Training
+
+Training auto-resumes from the latest checkpoint in the configured folder. To resume from a specific step:
+
+```toml
+[checkpoint]
+load_step = 500  # Resume from step 500
+```
+
+## Interoperability with TorchTune
+
+Checkpoints saved with `last_save_model_only = true` can be loaded directly into [torchtune](https://github.com/pytorch/torchtune) for fine-tuning.
+
+## Full Configuration Example
+
+```toml
+[checkpoint]
+enable = true
+folder = "checkpoint"
+interval = 500
+load_step = -1  # -1 = latest, or specify step number
+last_save_model_only = true
+export_dtype = "bfloat16"
+async_mode = "async"
+exclude_from_loading = []
+last_save_in_hf = false
+initial_load_in_hf = false
+create_seed_checkpoint = false
+```
+
+## Best Practices
+
+1. **Large models**: Use `async_mode = "async"` to overlap checkpoint saves with training
+2. **Fine-tuning export**: Enable `last_save_model_only` and `export_dtype = "bfloat16"` for smaller files
+3. **Pipeline parallelism**: Always create seed checkpoint first
+4. **Debugging**: Save frequent checkpoints during development, reduce for production
+5. **HF interop**: Use conversion scripts for offline conversion, direct save/load for training workflows