fix: restore all removed bundled skills + fix skills sync system

- Restored 21 skills removed in commits 757d012 and 740dd92:
  accelerate, audiocraft, code-review, faiss, flash-attention, gguf,
  grpo-rl-training, guidance, llava, nemo-curator, obliteratus, peft,
  pytorch-fsdp, pytorch-lightning, simpo, slime, stable-diffusion,
  tensorrt-llm, torchtitan, trl-fine-tuning, whisper

- Rewrote sync_skills() with proper update semantics:
  * New skills (not in manifest): copied to user dir
  * Existing skills (in manifest + on disk): updated via hash comparison
  * User-deleted skills (in manifest, not on disk): respected, not re-added
  * Stale manifest entries (removed from bundled): cleaned from manifest

- Added sync_skills() to CLI startup (cmd_chat) and gateway startup
  (start_gateway) — previously only ran during 'hermes update'

- Updated cmd_update output to show new/updated/cleaned counts

- Rewrote tests: 20 tests covering manifest CRUD, dir hashing, fresh
  install, user deletion respect, update detection, stale cleanup, and
  name collision handling

75 bundled skills total. 2002 tests pass.

skills/mlops/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