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/pytorch-lightning/references/distributed.md

@@ -0,0 +1,490 @@
+# PyTorch Lightning Distributed Training
+
+## Distributed Strategies
+
+Lightning supports multiple distributed strategies with a single parameter change.
+
+### 1. DDP (DistributedDataParallel)
+
+**Default strategy for multi-GPU**:
+
+```python
+# Automatic DDP on all available GPUs
+trainer = L.Trainer(accelerator='gpu', devices=4, strategy='ddp')
+
+# Or auto-detect
+trainer = L.Trainer(accelerator='gpu', devices='auto')
+```
+
+**How DDP works**:
+- Replicates model on each GPU
+- Each GPU processes different batch
+- Gradients all-reduced across GPUs
+- Model weights synchronized
+
+**Launch**:
+```bash
+# Lightning handles spawning processes automatically
+python train.py
+```
+
+**DDP Configuration**:
+```python
+from lightning.pytorch.strategies import DDPStrategy
+
+strategy = DDPStrategy(
+    find_unused_parameters=False,  # Set True if model has unused params
+    gradient_as_bucket_view=True,  # Memory optimization
+    static_graph=False,  # Set True if graph doesn't change
+)
+
+trainer = L.Trainer(strategy=strategy)
+```
+
+### 2. FSDP (Fully Sharded Data Parallel)
+
+**For large models (7B+ parameters)**:
+
+```python
+from lightning.pytorch.strategies import FSDPStrategy
+
+strategy = FSDPStrategy(
+    sharding_strategy="FULL_SHARD",  # ZeRO-3 equivalent
+    activation_checkpointing=None,   # Or specify layer types
+    cpu_offload=False,               # CPU offload for memory
+)
+
+trainer = L.Trainer(
+    accelerator='gpu',
+    devices=8,
+    strategy=strategy,
+    precision='bf16'  # Recommended with FSDP
+)
+
+trainer.fit(model, train_loader)
+```
+
+**FSDP Sharding Strategies**:
+```python
+# FULL_SHARD (most memory efficient, equivalent to ZeRO-3)
+strategy = FSDPStrategy(sharding_strategy="FULL_SHARD")
+
+# SHARD_GRAD_OP (less memory efficient, equivalent to ZeRO-2)
+strategy = FSDPStrategy(sharding_strategy="SHARD_GRAD_OP")
+
+# NO_SHARD (no sharding, like DDP)
+strategy = FSDPStrategy(sharding_strategy="NO_SHARD")
+```
+
+**Auto-wrap policy** (wrap transformer blocks):
+```python
+from torch.distributed.fsdp.wrap import transformer_auto_wrap_policy
+from transformers.models.gpt2.modeling_gpt2 import GPT2Block
+import functools
+
+auto_wrap_policy = functools.partial(
+    transformer_auto_wrap_policy,
+    transformer_layer_cls={GPT2Block}
+)
+
+strategy = FSDPStrategy(
+    auto_wrap_policy=auto_wrap_policy,
+    activation_checkpointing_policy={GPT2Block}  # Checkpoint these blocks
+)
+```
+
+### 3. DeepSpeed
+
+**For massive models (70B+ parameters)**:
+
+```python
+from lightning.pytorch.strategies import DeepSpeedStrategy
+
+# DeepSpeed ZeRO-3 with CPU offload
+strategy = DeepSpeedStrategy(
+    stage=3,                       # ZeRO-3
+    offload_optimizer=True,        # CPU offload optimizer
+    offload_parameters=True,       # CPU offload parameters
+    cpu_checkpointing=True,        # Checkpoint to CPU
+)
+
+trainer = L.Trainer(
+    accelerator='gpu',
+    devices=8,
+    strategy=strategy,
+    precision='bf16'
+)
+
+trainer.fit(model, train_loader)
+```
+
+**DeepSpeed configuration file**:
+```json
+{
+  "train_batch_size": "auto",
+  "train_micro_batch_size_per_gpu": "auto",
+  "gradient_accumulation_steps": "auto",
+  "zero_optimization": {
+    "stage": 3,
+    "offload_optimizer": {
+      "device": "cpu",
+      "pin_memory": true
+    },
+    "offload_param": {
+      "device": "cpu",
+      "pin_memory": true
+    },
+    "overlap_comm": true,
+    "contiguous_gradients": true,
+    "reduce_bucket_size": 5e8,
+    "stage3_prefetch_bucket_size": 5e8,
+    "stage3_param_persistence_threshold": 1e6
+  },
+  "bf16": {
+    "enabled": true
+  }
+}
+```
+
+**Use config file**:
+```python
+strategy = DeepSpeedStrategy(config='deepspeed_config.json')
+trainer = L.Trainer(strategy=strategy)
+```
+
+### 4. DDP Spawn
+
+**Windows-compatible DDP**:
+
+```python
+# Use when DDP doesn't work (e.g., Windows, Jupyter)
+trainer = L.Trainer(
+    accelerator='gpu',
+    devices=2,
+    strategy='ddp_spawn'  # Spawns new processes
+)
+```
+
+**Note**: Slower than DDP due to process spawning overhead
+
+## Multi-Node Training
+
+### Setup Multi-Node Cluster
+
+**Node 0 (master)**:
+```bash
+export MASTER_ADDR=192.168.1.100
+export MASTER_PORT=12355
+export WORLD_SIZE=16  # 2 nodes × 8 GPUs
+export NODE_RANK=0
+
+python train.py
+```
+
+**Node 1 (worker)**:
+```bash
+export MASTER_ADDR=192.168.1.100
+export MASTER_PORT=12355
+export WORLD_SIZE=16
+export NODE_RANK=1
+
+python train.py
+```
+
+**Training script**:
+```python
+trainer = L.Trainer(
+    accelerator='gpu',
+    devices=8,              # GPUs per node
+    num_nodes=2,            # Total nodes
+    strategy='ddp'
+)
+
+trainer.fit(model, train_loader)
+```
+
+### SLURM Integration
+
+**SLURM job script**:
+```bash
+#!/bin/bash
+#SBATCH --nodes=4
+#SBATCH --ntasks-per-node=8
+#SBATCH --gres=gpu:8
+#SBATCH --time=24:00:00
+
+# Lightning auto-detects SLURM environment
+srun python train.py
+```
+
+**Training script** (no changes needed):
+```python
+# Lightning automatically reads SLURM environment variables
+trainer = L.Trainer(
+    accelerator='gpu',
+    devices=8,
+    num_nodes=4,  # From SBATCH --nodes
+    strategy='ddp'
+)
+```
+
+### Kubernetes (KubeFlow)
+
+**Training script**:
+```python
+import os
+
+# Lightning auto-detects Kubernetes
+trainer = L.Trainer(
+    accelerator='gpu',
+    devices=int(os.getenv('WORLD_SIZE', 1)),
+    strategy='ddp'
+)
+```
+
+## Mixed Precision Training
+
+### BF16 (A100/H100)
+
+```python
+trainer = L.Trainer(
+    precision='bf16',  # Or 'bf16-mixed'
+    accelerator='gpu'
+)
+```
+
+**Advantages**:
+- No gradient scaler needed
+- Same dynamic range as FP32
+- 2× speedup, 50% memory reduction
+
+### FP16 (V100, older GPUs)
+
+```python
+trainer = L.Trainer(
+    precision='16-mixed',  # Or just '16'
+    accelerator='gpu'
+)
+```
+
+**Automatic gradient scaling** handled by Lightning
+
+### FP8 (H100)
+
+```python
+# Requires transformer_engine
+# pip install transformer-engine[pytorch]
+
+trainer = L.Trainer(
+    precision='transformer-engine',
+    accelerator='gpu'
+)
+```
+
+**Benefits**: 2× faster than BF16 on H100
+
+## Gradient Accumulation
+
+**Simulate larger batch size**:
+
+```python
+trainer = L.Trainer(
+    accumulate_grad_batches=4,  # Accumulate 4 batches
+    precision='bf16'
+)
+
+# Effective batch = batch_size × accumulate_grad_batches × num_gpus
+# Example: 32 × 4 × 8 = 1024
+```
+
+**Dynamic accumulation**:
+```python
+# Accumulate more early in training
+trainer = L.Trainer(
+    accumulate_grad_batches={
+        0: 8,   # Epochs 0-4: accumulate 8
+        5: 4,   # Epochs 5-9: accumulate 4
+        10: 2   # Epochs 10+: accumulate 2
+    }
+)
+```
+
+## Checkpointing in Distributed
+
+### Save Checkpoint
+
+```python
+from lightning.pytorch.callbacks import ModelCheckpoint
+
+# Only rank 0 saves by default
+checkpoint = ModelCheckpoint(
+    dirpath='checkpoints/',
+    filename='model-{epoch:02d}',
+    save_top_k=3
+)
+
+trainer = L.Trainer(callbacks=[checkpoint], strategy='ddp')
+trainer.fit(model, train_loader)
+```
+
+**Manual save**:
+```python
+class MyModel(L.LightningModule):
+    def training_step(self, batch, batch_idx):
+        # Training...
+        loss = ...
+
+        # Save every 1000 steps (only rank 0)
+        if batch_idx % 1000 == 0 and self.trainer.is_global_zero:
+            self.trainer.save_checkpoint(f'checkpoint_step_{batch_idx}.ckpt')
+
+        return loss
+```
+
+### Load Checkpoint
+
+```python
+# Resume training
+trainer = L.Trainer(strategy='ddp')
+trainer.fit(model, train_loader, ckpt_path='checkpoints/last.ckpt')
+
+# Load for inference
+model = MyModel.load_from_checkpoint('checkpoints/best.ckpt')
+model.eval()
+```
+
+## Strategy Comparison
+
+| Strategy | Memory Efficiency | Speed | Use Case |
+|----------|------------------|-------|----------|
+| DDP | Low | Fast | Small models (<7B), single node |
+| FSDP | High | Medium | Large models (7-70B) |
+| DeepSpeed ZeRO-2 | Medium | Fast | Medium models (1-13B) |
+| DeepSpeed ZeRO-3 | Very High | Slower | Massive models (70B+) |
+| DDP Spawn | Low | Slow | Windows, debugging |
+
+## Best Practices
+
+### 1. Choose Right Strategy
+
+```python
+# Model size guide
+if model_params < 1e9:  # <1B
+    strategy = 'ddp'
+elif model_params < 7e9:  # 1-7B
+    strategy = 'ddp' or DeepSpeedStrategy(stage=2)
+elif model_params < 70e9:  # 7-70B
+    strategy = FSDPStrategy(sharding_strategy="FULL_SHARD")
+else:  # 70B+
+    strategy = DeepSpeedStrategy(stage=3, offload_optimizer=True)
+
+trainer = L.Trainer(strategy=strategy)
+```
+
+### 2. Avoid Sync Issues
+
+```python
+class MyModel(L.LightningModule):
+    def training_step(self, batch, batch_idx):
+        # WRONG: This runs on all GPUs independently
+        if batch_idx % 100 == 0:
+            self.log_something()  # Logged 8 times on 8 GPUs!
+
+        # CORRECT: Use is_global_zero
+        if batch_idx % 100 == 0 and self.trainer.is_global_zero:
+            self.log_something()  # Logged once
+
+        loss = ...
+        return loss
+```
+
+### 3. Efficient Data Loading
+
+```python
+from torch.utils.data import DataLoader, DistributedSampler
+
+# Lightning handles DistributedSampler automatically
+train_loader = DataLoader(
+    dataset,
+    batch_size=32,
+    num_workers=4,  # 4 workers per GPU
+    pin_memory=True,
+    persistent_workers=True
+)
+
+# Lightning automatically wraps with DistributedSampler in DDP
+trainer.fit(model, train_loader)
+```
+
+### 4. Reduce Communication Overhead
+
+```python
+from lightning.pytorch.strategies import DDPStrategy
+
+strategy = DDPStrategy(
+    gradient_as_bucket_view=True,  # Reduce memory copies
+    static_graph=True,  # If model graph doesn't change (faster)
+)
+
+trainer = L.Trainer(strategy=strategy)
+```
+
+## Common Issues
+
+### Issue: NCCL Timeout
+
+**Symptom**: Training hangs with `NCCL timeout` error
+
+**Solution 1**: Increase timeout
+```bash
+export NCCL_TIMEOUT=3600  # 1 hour
+python train.py
+```
+
+**Solution 2**: Check network
+```bash
+# Test inter-node communication
+nvidia-smi nvlink -s
+
+# Verify all nodes can ping each other
+ping <node-2-ip>
+```
+
+### Issue: OOM with FSDP
+
+**Solution**: Enable CPU offload
+```python
+strategy = FSDPStrategy(
+    sharding_strategy="FULL_SHARD",
+    cpu_offload=True  # Offload to CPU
+)
+```
+
+### Issue: Different Results with DDP
+
+**Cause**: Different random seeds per GPU
+
+**Solution**: Set seed in LightningModule
+```python
+class MyModel(L.LightningModule):
+    def __init__(self):
+        super().__init__()
+        L.seed_everything(42, workers=True)  # Same seed everywhere
+```
+
+### Issue: DeepSpeed Config Errors
+
+**Solution**: Use Lightning's auto config
+```python
+strategy = DeepSpeedStrategy(
+    stage=3,
+    # Don't specify config file, Lightning generates automatically
+)
+```
+
+## Resources
+
+- Distributed strategies: https://lightning.ai/docs/pytorch/stable/accelerators/gpu_intermediate.html
+- FSDP guide: https://lightning.ai/docs/pytorch/stable/advanced/model_parallel/fsdp.html
+- DeepSpeed: https://lightning.ai/docs/pytorch/stable/advanced/model_parallel/deepspeed.html
+- Multi-node: https://lightning.ai/docs/pytorch/stable/clouds/cluster.html