local-llm 9

Native Rapid-MLX provider for DeepSeek Harness — teaches DSH what the local server already knows (memory-fitted context via max_model_len), plus model-management tools and a /rapid-mlx overview.

Local-model (Ollama) integration for DeepSeek Harness: discover, pull, remove, and inspect local models, route requests to them by task type or keyword with automatic fallback to the cloud, and get a one-shot status overview via /ollama.

Use AItelier as a subagent from DeepSeek Harness: generate, edit, run, export and import SkillFlow pipelines. Client only — requires a running AItelier backend (Docker; see README).

Context Guardian as a native DeepSeek Harness (DSH) bundle: deterministic, seq-pointer-preserving compaction with model-facing recall and search. Companion to dsh-tool-guardian.

Tool Guardian as a native DeepSeek Harness (DSH) bundle: keeps tool definitions out of the context window by fronting your MCP servers behind a small native router. A bridge to the Python tool-guardian, not a rewrite.

Live health pane for local LLM endpoints (llama.cpp slots + Ollama models) in the DeepSeek Harness web UI.

Cross-project experience recall for DeepSeek Harness: when the running work matches something your memory store already learned elsewhere, that lesson is injected before the next step. Runs fully locally, judged by a local small model.

Unsloth for DeepSeek Harness: local LLM fine-tuning and dataset generation. Bundles a zero-dependency Python MCP server and mounts DSH's own MCP client, exposing model inventory, Data Recipe dataset generation, LoRA/QLoRA training, GGUF export and inferen

DeepSeek Harness plugin: keep every model request inside an allowed set per provider, redirect or refuse the rest, and warn when a request would make a llama.cpp router reload models