Add skills tools and enhance model integration

- Introduced new skills tools: `skills_categories`, `skills_list`, and `skill_view` in `model_tools.py`, allowing for better organization and access to skill-related functionalities.
- Updated `toolsets.py` to include a new `skills` toolset, providing a dedicated space for skill tools.
- Enhanced `batch_runner.py` to recognize and validate skills tools during batch processing.
- Added comprehensive tool definitions for skills tools, ensuring compatibility with OpenAI's expected format.
- Created new shell script `test_skills_kimi.sh` for testing skills tool functionality with Kimi K2.5.
- Added example skill files demonstrating the structure and usage of skills within the Hermes-Agent framework, including `SKILL.md` for example and audiocraft skills.
- Improved documentation for skills tools and their integration into the existing tool framework, ensuring clarity for future development and usage.

skills/mlops/torchtitan/references/float8.md

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+# Float8 Training in TorchTitan
+
+Float8 training provides substantial speedups for models where GEMMs are large enough that the FP8 tensorcore speedup outweighs dynamic quantization overhead.
+
+## Hardware Requirements
+
+- NVIDIA H100 or newer GPUs (FP8 Tensor Cores)
+- Blackwell GPUs for MXFP8 training
+
+## Installation
+
+```bash
+USE_CPP=0 pip install git+https://github.com/pytorch/ao.git
+```
+
+## Usage: Tensorwise Scaling
+
+Standard Float8 with tensorwise dynamic scaling:
+
+```bash
+CONFIG_FILE="./torchtitan/models/llama3/train_configs/llama3_8b.toml" ./run_train.sh \
+  --model.converters="quantize.linear.float8" \
+  --quantize.linear.float8.enable_fsdp_float8_all_gather \
+  --quantize.linear.float8.precompute_float8_dynamic_scale_for_fsdp \
+  --compile.enable
+```
+
+### Key Arguments
+
+| Argument | Description |
+|----------|-------------|
+| `--model.converters="quantize.linear.float8"` | Swap `nn.Linear` with `Float8Linear` |
+| `--quantize.linear.float8.enable_fsdp_float8_all_gather` | Communicate in float8 to save bandwidth |
+| `--quantize.linear.float8.precompute_float8_dynamic_scale_for_fsdp` | Single all-reduce for all AMAX/scales |
+| `--compile.enable` | Required - fuses float8 scaling/casting kernels |
+
+## Usage: Rowwise Scaling
+
+Higher accuracy than tensorwise scaling:
+
+```bash
+CONFIG_FILE="./torchtitan/models/llama3/train_configs/llama3_8b.toml" ./run_train.sh \
+  --model.converters="quantize.linear.float8" \
+  --quantize.linear.float8.recipe_name rowwise \
+  --compile.enable
+```
+
+## Filtering Layers
+
+Not all layers benefit from Float8. Filter small layers:
+
+```bash
+--quantize.linear.float8.filter_fqns="attention.wk,attention.wv,output"
+```
+
+### Auto-filtering
+
+Automatically skip layers too small to benefit:
+
+```bash
+--quantize.linear.float8.filter_fqns="auto_filter_small_kn"
+```
+
+Thresholds based on H100 microbenchmarks where speedup > overhead.
+
+## TOML Configuration
+
+```toml
+[model]
+converters = ["quantize.linear.float8"]
+
+[quantize.linear.float8]
+enable_fsdp_float8_all_gather = true
+precompute_float8_dynamic_scale_for_fsdp = true
+filter_fqns = ["output", "auto_filter_small_kn"]
+
+[compile]
+enable = true
+components = ["model", "loss"]
+```
+
+## How Float8 Works with Distributed Training
+
+### Single Device
+
+Cast input and weight to float8 inside forward before calling `torch._scaled_mm`:
+
+```python
+# Float8 matmul requires scales
+torch._scaled_mm(input_fp8, weight_fp8, scale_a=scale_input, scale_b=scale_weight)
+```
+
+### FSDP + Float8
+
+1. Cast sharded high-precision weights (1/N per rank) to float8
+2. Perform float8 all-gather (saves bandwidth vs bf16/fp32)
+3. Communicate `max(abs)` across ranks for scale computation
+4. At forward start, have unsharded float8 weights ready
+
+**Net benefit**: Float8 all-gather + amax communication can beat bf16/fp32 all-gather, depending on world size and message size.
+
+### TP + Float8
+
+- **Input**: Cast sharded input to float8, all-gather in float8
+- **Weights**: Communicate `max(abs)` for sharded weights
+- **Matmul**: Float8 input (unsharded) x float8 weight (sharded) with global scales
+
+## Scaling Strategies
+
+| Strategy | Status | Description |
+|----------|--------|-------------|
+| Tensorwise dynamic | Stable | Single scale per tensor |
+| Rowwise dynamic | Alpha | Scale per row, higher accuracy |
+
+## Performance Gains
+
+From benchmarks on H100:
+
+| Configuration | TPS/GPU | vs Baseline |
+|---------------|---------|-------------|
+| FSDP only | 5,762 | - |
+| FSDP + compile | 6,667 | +16% |
+| FSDP + compile + Float8 | 8,532 | +48% |
+
+## Determining Float8 Benefit
+
+Check [torchao microbenchmarks](https://github.com/pytorch/ao/tree/main/torchao/float8#performance) for forward+backward pass speedups on "layer norm => linear => sigmoid" for different M,N,K sizes.
+
+Rule of thumb: GEMMs with K,N > 4096 typically benefit from Float8.
+
+## MXFP8 Training (Blackwell)
+
+For NVIDIA Blackwell GPUs, TorchTitan supports MXFP8 (Microscaling FP8) for both dense and MoE models. See [docs/mxfp8.md](https://github.com/pytorch/torchtitan/blob/main/docs/mxfp8.md) for details.