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/custom-models.md

@@ -0,0 +1,258 @@
+# Adding Custom Models to TorchTitan
+
+This guide explains how to add a new model to TorchTitan following the established patterns.
+
+## Directory Structure
+
+```
+torchtitan/models/your_model/
+├── model/
+│   ├── __init__.py
+│   ├── args.py          # Model arguments
+│   ├── model.py         # Model definition
+│   └── state_dict_adapter.py  # HF conversion (optional)
+├── infra/
+│   ├── __init__.py
+│   ├── parallelize.py   # TP, FSDP, compile application
+│   └── pipeline.py      # PP application (optional)
+├── train_configs/
+│   ├── debug_model.toml
+│   └── your_model_XB.toml
+├── __init__.py          # TrainSpec registration
+└── README.md
+```
+
+## Step 1: Define Model Arguments
+
+Inherit from `BaseModelArgs`:
+
+```python
+# model/args.py
+from torchtitan.protocols.model import BaseModelArgs
+from dataclasses import dataclass
+
+@dataclass
+class YourModelArgs(BaseModelArgs):
+    dim: int = 4096
+    n_layers: int = 32
+    n_heads: int = 32
+    vocab_size: int = 128256
+
+    def get_nparams_and_flops(self, seq_len: int) -> tuple[int, int]:
+        """Return (num_params, flops_per_token) for throughput calculation."""
+        nparams = self.vocab_size * self.dim + ...  # Calculate params
+        flops = 6 * nparams  # Approximate: 6 * params for forward+backward
+        return nparams, flops
+
+    def update_from_config(self, job_config) -> "YourModelArgs":
+        """Update args from training config."""
+        # Override specific args from job_config if needed
+        return self
+```
+
+## Step 2: Define Model
+
+Inherit from `ModelProtocol`:
+
+```python
+# model/model.py
+import torch.nn as nn
+from torchtitan.protocols.model import ModelProtocol
+from .args import YourModelArgs
+
+class YourModel(ModelProtocol):
+    def __init__(self, args: YourModelArgs):
+        super().__init__()
+        self.args = args
+        self.tok_embeddings = nn.Embedding(args.vocab_size, args.dim)
+        self.layers = nn.ModuleDict({
+            str(i): TransformerBlock(args) for i in range(args.n_layers)
+        })
+        self.norm = RMSNorm(args.dim)
+        self.output = nn.Linear(args.dim, args.vocab_size, bias=False)
+
+    def forward(self, tokens: torch.Tensor) -> torch.Tensor:
+        h = self.tok_embeddings(tokens)
+        for layer in self.layers.values():
+            h = layer(h)
+        h = self.norm(h)
+        return self.output(h)
+
+    def init_weights(self):
+        """Initialize weights recursively."""
+        for module in self.modules():
+            if hasattr(module, 'init_weights') and module is not self:
+                module.init_weights()
+            elif isinstance(module, nn.Linear):
+                nn.init.normal_(module.weight, std=0.02)
+```
+
+**Important guidelines**:
+- Write single-device model code (parallelism applied externally)
+- Use `nn.ModuleDict` for layers (preserves FQNs when deleting for PP)
+- Make input/output layers optional for PP compatibility
+- Define `init_weights()` recursively
+
+## Step 3: Parallelize Function
+
+```python
+# infra/parallelize.py
+from torch.distributed._composable.fsdp import fully_shard
+from torch.distributed.tensor.parallel import parallelize_module
+
+def parallelize_your_model(
+    model: YourModel,
+    world_mesh: DeviceMesh,
+    parallel_dims: ParallelDims,
+    job_config: JobConfig,
+):
+    # Apply in this order: TP -> AC -> compile -> FSDP
+
+    # 1. Tensor Parallelism
+    if parallel_dims.tp_enabled:
+        apply_tp(model, world_mesh["tp"], job_config)
+
+    # 2. Activation Checkpointing
+    if job_config.activation_checkpoint.mode == "full":
+        apply_ac(model, job_config)
+
+    # 3. torch.compile
+    if job_config.compile.enable:
+        model = torch.compile(model)
+
+    # 4. FSDP
+    if parallel_dims.dp_enabled:
+        apply_fsdp(model, world_mesh["dp"], job_config)
+
+    return model
+```
+
+## Step 4: Create TrainSpec
+
+```python
+# __init__.py
+from torchtitan.protocols.train_spec import TrainSpec, register_train_spec
+from .model.model import YourModel
+from .model.args import YourModelArgs
+from .infra.parallelize import parallelize_your_model
+
+MODEL_CONFIGS = {
+    "8B": YourModelArgs(dim=4096, n_layers=32, n_heads=32),
+    "70B": YourModelArgs(dim=8192, n_layers=80, n_heads=64),
+}
+
+def get_train_spec(flavor: str) -> TrainSpec:
+    return TrainSpec(
+        model_cls=YourModel,
+        model_args=MODEL_CONFIGS[flavor],
+        parallelize_fn=parallelize_your_model,
+        pipeline_fn=None,  # Or your_pipeline_fn for PP
+        build_optimizer_fn=build_optimizer,  # Reuse existing
+        build_lr_scheduler_fn=build_lr_scheduler,  # Reuse existing
+        build_dataloader_fn=build_dataloader,  # Reuse existing
+        build_tokenizer_fn=build_tokenizer,  # Reuse existing
+        build_loss_fn=build_loss,  # Reuse existing
+        state_dict_adapter=None,  # Or YourStateDictAdapter
+    )
+
+# Register so train.py can find it
+register_train_spec("your_model", get_train_spec)
+```
+
+## Step 5: State Dict Adapter (Optional)
+
+For HuggingFace checkpoint conversion:
+
+```python
+# model/state_dict_adapter.py
+from torchtitan.protocols.state_dict_adapter import BaseStateDictAdapter
+
+class YourStateDictAdapter(BaseStateDictAdapter):
+    def to_hf(self, state_dict: dict) -> dict:
+        """Convert torchtitan state dict to HF format."""
+        hf_state_dict = {}
+        for key, value in state_dict.items():
+            hf_key = self._convert_key_to_hf(key)
+            hf_state_dict[hf_key] = value
+        return hf_state_dict
+
+    def from_hf(self, state_dict: dict) -> dict:
+        """Convert HF state dict to torchtitan format."""
+        tt_state_dict = {}
+        for key, value in state_dict.items():
+            tt_key = self._convert_key_from_hf(key)
+            tt_state_dict[tt_key] = value
+        return tt_state_dict
+```
+
+## Step 6: Training Config
+
+```toml
+# train_configs/your_model_8b.toml
+[job]
+dump_folder = "./outputs"
+description = "Your Model 8B training"
+
+[model]
+name = "your_model"
+flavor = "8B"
+
+[optimizer]
+name = "AdamW"
+lr = 3e-4
+
+[training]
+local_batch_size = 2
+seq_len = 8192
+steps = 1000
+dataset = "c4"
+
+[parallelism]
+data_parallel_shard_degree = -1
+tensor_parallel_degree = 1
+```
+
+## Step 7: Register Model
+
+Add to `torchtitan/models/__init__.py`:
+
+```python
+from .your_model import get_train_spec as get_your_model_train_spec
+
+MODEL_REGISTRY["your_model"] = get_your_model_train_spec
+```
+
+## Testing
+
+### Numerics Test
+
+Compare output with HuggingFace implementation:
+
+```python
+def test_numerics():
+    # Load same checkpoint into both implementations
+    tt_model = YourModel(args).load_checkpoint(...)
+    hf_model = HFYourModel.from_pretrained(...)
+
+    # Compare outputs
+    input_ids = torch.randint(0, vocab_size, (1, 128))
+    tt_output = tt_model(input_ids)
+    hf_output = hf_model(input_ids).logits
+
+    torch.testing.assert_close(tt_output, hf_output, atol=1e-4, rtol=1e-4)
+```
+
+### Loss Convergence
+
+Compare loss curves with verified baseline (see `docs/converging.md`).
+
+### Performance Benchmark
+
+Add benchmark config to `benchmarks/` folder.
+
+## Guiding Principles
+
+1. **Readability over flexibility**: Don't over-abstract
+2. **Minimal model changes**: Parallelism applied externally
+3. **Clean, minimal codebase**: Reuse existing components where possible
+4. **Single-device semantics**: Model code should work on single GPU