Adding a lot of skills for Hermes Gerhard

agents/gerhard-hermes/skills/mlops/inference/obliteratus/SKILL.md

@@ -1,6 +1,6 @@
 ---
 name: obliteratus
-description: Remove refusal behaviors from open-weight LLMs using OBLITERATUS — mechanistic interpretability techniques (diff-in-means, SVD, whitened SVD, LEACE, SAE decomposition, etc.) to excise guardrails while preserving reasoning. 9 CLI methods, 28 analysis modules, 116 model presets across 5 compute tiers, tournament evaluation, and telemetry-driven recommendations. Use when a user wants to uncensor, abliterate, or remove refusal from an LLM.
+description: "OBLITERATUS: abliterate LLM refusals (diff-in-means)."
 version: 2.0.0
 author: Hermes Agent
 license: MIT
@@ -13,10 +13,21 @@ metadata:
 
 # OBLITERATUS Skill
 
+## What's inside
+
+9 CLI methods, 28 analysis modules, 116 model presets across 5 compute tiers, tournament evaluation, and telemetry-driven recommendations.
+
 Remove refusal behaviors (guardrails) from open-weight LLMs without retraining or fine-tuning. Uses mechanistic interpretability techniques — including diff-in-means, SVD, whitened SVD, LEACE concept erasure, SAE decomposition, Bayesian kernel projection, and more — to identify and surgically excise refusal directions from model weights while preserving reasoning capabilities.
 
 **License warning:** OBLITERATUS is AGPL-3.0. NEVER import it as a Python library. Always invoke via CLI (`obliteratus` command) or subprocess. This keeps Hermes Agent's MIT license clean.
 
+## Video Guide
+
+Walkthrough of OBLITERATUS used by a Hermes agent to abliterate Gemma:
+https://www.youtube.com/watch?v=8fG9BrNTeHs ("OBLITERATUS: An AI Agent Removed Gemma 4's Safety Guardrails")
+
+Useful when the user wants a visual overview of the end-to-end workflow before running it themselves.
+
 ## When to Use This Skill
 
 Trigger when the user:

agents/gerhard-hermes/skills/mlops/inference/outlines/SKILL.md

@@ -1,6 +1,6 @@
 ---
 name: outlines
-description: Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library
+description: "Outlines: structured JSON/regex/Pydantic LLM generation."
 version: 1.0.0
 author: Orchestra Research
 license: MIT

agents/gerhard-hermes/skills/mlops/inference/vllm/SKILL.md

@@ -1,6 +1,6 @@
 ---
 name: serving-llms-vllm
-description: Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.
+description: "vLLM: high-throughput LLM serving, OpenAI API, quantization."
 version: 1.0.0
 author: Orchestra Research
 license: MIT
@@ -13,6 +13,10 @@ metadata:
 
 # vLLM - High-Performance LLM Serving
 
+## When to use
+
+Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.
+
 ## Quick start
 
 vLLM achieves 24x higher throughput than standard transformers through PagedAttention (block-based KV cache) and continuous batching (mixing prefill/decode requests).