feat(skills): vLLM and Ollama deployment plus LlamaFactory fine-tuning skills (#19)

Co-authored-by: Alice <alice@prismshadow.com>
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>

packages/docs/content/skills.en.md

@@ -65,6 +65,9 @@ The built-in Skills, by group (the group manifest is `SKILL_GROUPS` in `packages
 | AI App Development | `penguin-sdk` | Build AI and RAG apps on the SDK: the createSession/run streaming loop plus a complete retrieval recipe with chunk-revealing citations |
 | | `penguin-cli` | Manage model API keys, default models and per-agent Vault secrets with the penguin CLI |
 | | `agenthub-models` | Call model APIs through `@prismshadow/agenthub`: streaming text, image generation, speech synthesis and embeddings |
+| | `vllm` | Deploy and serve LLMs with vLLM behind an OpenAI-compatible endpoint, with tool calling enabled for agent workloads |
+| | `ollama` | Deploy and serve local models with Ollama: pull and run them, then expose the OpenAI-compatible endpoint to apps and agents |
+| | `llamafactory` | Fine-tune LLMs with LlamaFactory: register datasets, train via YAML configs, merge LoRA adapters and serve the result |
 | Agent Tuning | `agent-creation` | Turn a user requirement into a concrete agent: write the target agent's AGENTS.md and install the skills it needs |
 | | `benchmark-design` | Design and calibrate a multi-Case capability Benchmark with repeated independent evaluations and a traceable baseline |
 | | `agent-evaluation` | Run and score exactly one Benchmark Case run, with CLI execution, Trace provenance checks and private Rubric isolation |

packages/docs/content/skills.zh.md

@@ -65,6 +65,9 @@ Skill 库以 npm 包 `@prismshadow/penguin-skills` 发布，tarball 直接携带
 | AI 应用开发 | `penguin-sdk` | 基于 SDK 构建 AI 与 RAG 应用：createSession/run 流式循环，外加带可溯源引用的完整检索配方 |
 | | `penguin-cli` | 用 penguin CLI 管理模型 API Key、默认模型与各 Agent 的 Vault 密钥 |
 | | `agenthub-models` | 经 `@prismshadow/agenthub` 调用模型 API：流式文本、图像生成、语音合成与 Embedding |
+| | `vllm` | 用 vLLM 部署与服务 LLM，提供 OpenAI 兼容端点，并为 Agent 负载启用工具调用 |
+| | `ollama` | 用 Ollama 部署与运行本地模型，把 OpenAI 兼容端点接入应用与 Agent |
+| | `llamafactory` | 用 LlamaFactory 微调 LLM：注册数据集、以 YAML 配置训练、合并 LoRA 适配器并部署产物 |
 | Agent 调优 | `agent-creation` | 把用户需求变成具体的 Agent：撰写目标 Agent 的 AGENTS.md 并安装所需 Skill |
 | | `benchmark-design` | 设计并校准多 Case 的能力评测 Benchmark，含重复独立评测与可追溯基线 |
 | | `agent-evaluation` | 隔离执行并评分单个 Benchmark Case:CLI 执行、Trace 溯源检查、Rubric 私有隔离 |