feat(gateway): skill-aware slash commands, paginated /commands, Telegram 100-cap (#3934)

* feat(gateway): skill-aware slash commands, paginated /commands, Telegram 100-cap

Map active skills to Telegram's slash command menu so users can
discover and invoke skills directly. Three changes:

1. Telegram menu now includes active skill commands alongside built-in
   commands, capped at 100 entries (Telegram Bot API limit). Overflow
   commands remain callable but hidden from the picker. Logged at
   startup when cap is hit.

2. New /commands [page] gateway command for paginated browsing of all
   commands + skills. /help now shows first 10 skill commands and
   points to /commands for the full list.

3. When a user types a slash command that matches a disabled or
   uninstalled skill, they get actionable guidance:
   - Disabled: 'Enable it with: hermes skills config'
   - Optional (not installed): 'Install with: hermes skills install official/<path>'

Built on ideas from PR #3921 by @kshitijk4poor.

* chore: move 21 niche skills to optional-skills

Move specialized/niche skills from built-in (skills/) to optional
(optional-skills/) to reduce the default skill count. Users can
install them with: hermes skills install official/<category>/<name>

Moved skills (21):
- mlops: accelerate, chroma, faiss, flash-attention,
  hermes-atropos-environments, huggingface-tokenizers, instructor,
  lambda-labs, llava, nemo-curator, pinecone, pytorch-lightning,
  qdrant, saelens, simpo, slime, tensorrt-llm, torchtitan
- research: domain-intel, duckduckgo-search
- devops: inference-sh cli

Built-in skills: 96 → 75
Optional skills: 22 → 43

* fix: only include repo built-in skills in Telegram menu, not user-installed

User-installed skills (from hub or manually added) stay accessible via
/skills and by typing the command directly, but don't get registered
in the Telegram slash command picker. Only skills whose SKILL.md is
under the repo's skills/ directory are included in the menu.

This keeps the Telegram menu focused on the curated built-in set while
user-installed skills remain discoverable through /skills and /commands.

skills/mlops/vector-databases/pinecone/references/deployment.md

@@ -1,181 +0,0 @@
-# Pinecone Deployment Guide
-
-Production deployment patterns for Pinecone.
-
-## Serverless vs Pod-based
-
-### Serverless (Recommended)
-
-```python
-from pinecone import Pinecone, ServerlessSpec
-
-pc = Pinecone(api_key="your-key")
-
-# Create serverless index
-pc.create_index(
-    name="my-index",
-    dimension=1536,
-    metric="cosine",
-    spec=ServerlessSpec(
-        cloud="aws",  # or "gcp", "azure"
-        region="us-east-1"
-    )
-)
-```
-
-**Benefits:**
-- Auto-scaling
-- Pay per usage
-- No infrastructure management
-- Cost-effective for variable load
-
-**Use when:**
-- Variable traffic
-- Cost optimization important
-- Don't need consistent latency
-
-### Pod-based
-
-```python
-from pinecone import PodSpec
-
-pc.create_index(
-    name="my-index",
-    dimension=1536,
-    metric="cosine",
-    spec=PodSpec(
-        environment="us-east1-gcp",
-        pod_type="p1.x1",  # or p1.x2, p1.x4, p1.x8
-        pods=2,  # Number of pods
-        replicas=2  # High availability
-    )
-)
-```
-
-**Benefits:**
-- Consistent performance
-- Predictable latency
-- Higher throughput
-- Dedicated resources
-
-**Use when:**
-- Production workloads
-- Need consistent p95 latency
-- High throughput required
-
-## Hybrid search
-
-### Dense + Sparse vectors
-
-```python
-# Upsert with both dense and sparse vectors
-index.upsert(vectors=[
-    {
-        "id": "doc1",
-        "values": [0.1, 0.2, ...],  # Dense (semantic)
-        "sparse_values": {
-            "indices": [10, 45, 123],  # Token IDs
-            "values": [0.5, 0.3, 0.8]   # TF-IDF/BM25 scores
-        },
-        "metadata": {"text": "..."}
-    }
-])
-
-# Hybrid query
-results = index.query(
-    vector=[0.1, 0.2, ...],  # Dense query
-    sparse_vector={
-        "indices": [10, 45],
-        "values": [0.5, 0.3]
-    },
-    top_k=10,
-    alpha=0.5  # 0=sparse only, 1=dense only, 0.5=balanced
-)
-```
-
-**Benefits:**
-- Best of both worlds
-- Semantic + keyword matching
-- Better recall than either alone
-
-## Namespaces for multi-tenancy
-
-```python
-# Separate data by user/tenant
-index.upsert(
-    vectors=[{"id": "doc1", "values": [...]}],
-    namespace="user-123"
-)
-
-# Query specific namespace
-results = index.query(
-    vector=[...],
-    namespace="user-123",
-    top_k=5
-)
-
-# List namespaces
-stats = index.describe_index_stats()
-print(stats['namespaces'])
-```
-
-**Use cases:**
-- Multi-tenant SaaS
-- User-specific data isolation
-- A/B testing (prod/staging namespaces)
-
-## Metadata filtering
-
-### Exact match
-
-```python
-results = index.query(
-    vector=[...],
-    filter={"category": "tutorial"},
-    top_k=5
-)
-```
-
-### Range queries
-
-```python
-results = index.query(
-    vector=[...],
-    filter={"price": {"$gte": 100, "$lte": 500}},
-    top_k=5
-)
-```
-
-### Complex filters
-
-```python
-results = index.query(
-    vector=[...],
-    filter={
-        "$and": [
-            {"category": {"$in": ["tutorial", "guide"]}},
-            {"difficulty": {"$lte": 3}},
-            {"published": {"$gte": "2024-01-01"}}
-        ]
-    },
-    top_k=5
-)
-```
-
-## Best practices
-
-1. **Use serverless for development** - Cost-effective
-2. **Switch to pods for production** - Consistent performance
-3. **Implement namespaces** - Multi-tenancy
-4. **Add metadata strategically** - Enable filtering
-5. **Use hybrid search** - Better quality
-6. **Batch upserts** - 100-200 vectors per batch
-7. **Monitor usage** - Check Pinecone dashboard
-8. **Set up alerts** - Usage/cost thresholds
-9. **Regular backups** - Export important data
-10. **Test filters** - Verify performance
-
-## Resources
-
-- **Docs**: https://docs.pinecone.io
-- **Console**: https://app.pinecone.io