skills: move 7 niche mlops/mcp skills to optional (#12474)

Built-in → optional-skills/:
  mlops/training/peft         → optional-skills/mlops/peft
  mlops/training/pytorch-fsdp → optional-skills/mlops/pytorch-fsdp
  mlops/models/clip           → optional-skills/mlops/clip
  mlops/models/stable-diffusion → optional-skills/mlops/stable-diffusion
  mlops/models/whisper        → optional-skills/mlops/whisper
  mlops/cloud/modal           → optional-skills/mlops/modal
  mcp/mcporter                → optional-skills/mcp/mcporter

Built-in mlops training kept: axolotl, trl-fine-tuning, unsloth.
Built-in mlops models kept: audiocraft, segment-anything.
Built-in mlops evaluation/research/huggingface-hub/inference all kept.
native-mcp stays built-in (documents the native MCP tool); mcporter was a
redundant alternative CLI.

Also: removed now-empty skills/mlops/cloud/ dir, refreshed
skills/mlops/models/DESCRIPTION.md and skills/mcp/DESCRIPTION.md to match
what's left, and synchronized both catalog pages (skills-catalog.md,
optional-skills-catalog.md).

website/docs/reference/optional-skills-catalog.md

@@ -83,6 +83,7 @@ hermes skills uninstall <skill-name>
 | Skill | Description |
 |-------|-------------|
 | **fastmcp** | Build, test, inspect, install, and deploy MCP servers with FastMCP in Python. Covers wrapping APIs or databases as MCP tools, exposing resources or prompts, and deployment. |
+| **mcporter** | The `mcporter` CLI — list, configure, auth, and call MCP servers/tools directly (HTTP or stdio) from the terminal. Useful for ad-hoc MCP interactions; for always-on tool discovery use the built-in `native-mcp` client instead. |
 
 ## Migration
 
@@ -98,6 +99,7 @@ The largest optional category — covers the full ML pipeline from data curation
 |-------|-------------|
 | **accelerate** | Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. |
 | **chroma** | Open-source embedding database. Store embeddings and metadata, perform vector and full-text search. Simple 4-function API for RAG and semantic search. |
+| **clip** | OpenAI's vision-language model connecting images and text. Zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. |
 | **faiss** | Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). |
 | **flash-attention** | Optimize transformer attention with Flash Attention for 2-4x speedup and 10-20x memory reduction. Supports PyTorch SDPA, flash-attn library, H100 FP8, and sliding window. |
 | **guidance** | Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance — Microsoft Research's constrained generation framework. |
@@ -106,15 +108,20 @@ The largest optional category — covers the full ML pipeline from data curation
 | **instructor** | Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, and stream partial results. |
 | **lambda-labs** | Reserved and on-demand GPU cloud instances for ML training and inference. SSH access, persistent filesystems, and multi-node clusters. |
 | **llava** | Large Language and Vision Assistant — visual instruction tuning and image-based conversations combining CLIP vision with LLaMA language models. |
+| **modal** | Serverless GPU cloud platform for running ML workloads. On-demand GPU access without infrastructure management, ML model deployment as APIs, or batch jobs with automatic scaling. |
 | **nemo-curator** | GPU-accelerated data curation for LLM training. Fuzzy deduplication (16x faster), quality filtering (30+ heuristics), semantic dedup, PII redaction. Scales with RAPIDS. |
+| **peft-fine-tuning** | Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Train <1% of parameters with minimal accuracy loss for 7B–70B models on limited GPU memory. HuggingFace's official PEFT library. |
 | **pinecone** | Managed vector database for production AI. Auto-scaling, hybrid search (dense + sparse), metadata filtering, and low latency (under 100ms p95). |
+| **pytorch-fsdp** | Expert guidance for Fully Sharded Data Parallel training with PyTorch FSDP — parameter sharding, mixed precision, CPU offloading, FSDP2. |
 | **pytorch-lightning** | High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks, and minimal boilerplate. |
 | **qdrant** | High-performance vector similarity search engine. Rust-powered with fast nearest neighbor search, hybrid search with filtering, and scalable vector storage. |
 | **saelens** | Train and analyze Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. |
 | **simpo** | Simple Preference Optimization — reference-free alternative to DPO with better performance (+6.4 pts on AlpacaEval 2.0). No reference model needed. |
 | **slime** | LLM post-training with RL using Megatron+SGLang framework. Custom data generation workflows and tight Megatron-LM integration for RL scaling. |
+| **stable-diffusion-image-generation** | State-of-the-art text-to-image generation with Stable Diffusion via HuggingFace Diffusers. Text-to-image, image-to-image translation, inpainting, and custom diffusion pipelines. |
 | **tensorrt-llm** | Optimize LLM inference with NVIDIA TensorRT for maximum throughput. 10-100x faster than PyTorch on A100/H100 with quantization (FP8/INT4) and in-flight batching. |
 | **torchtitan** | PyTorch-native distributed LLM pretraining with 4D parallelism (FSDP2, TP, PP, CP). Scale from 8 to 512+ GPUs with Float8 and torch.compile. |
+| **whisper** | OpenAI's general-purpose speech recognition. 99 languages, transcription, translation to English, and language ID. Six model sizes from tiny (39M) to large (1550M). Best for robust multilingual ASR. |
 
 ## Productivity