rabbit-rewriteslisted

Rewrite the prose the engine flagged using a small local model over an OpenAI-compatible endpoint, instead of spending a frontier model on it. Use when the user wants to de-slop or humanize a draft offline, on a Raspberry Pi, in CI, or in a pre-commit hook, mentions llama.cpp, llama-server, Ollama, LM Studio, vLLM, or OpenRouter for writing work, asks which local model is good enough to clean up their writing, wants rewriting that costs no API tokens, or asks to benchmark or compare models on a rewriting task. Covers endpoint setup, planning what would be sent, applying gated rewrites in place, and measuring a model's pass rate.
whit3rabbit/rabbit-writes · ★ 0 · AI & Automation · score 73

Install: claude install-skill whit3rabbit/rabbit-writes

# Model-backed rewriting Detection in this plugin needs no model. `scan.py` is pure Python and runs on a Pi today. Rewriting is the part that needs one, and this is the path that uses a small local model for it rather than a frontier model. **Paths.** `${CLAUDE_PLUGIN_ROOT}/skills/` means the directory holding this skill and its siblings (`rabbit-writes`, `voice-setup`, `rabbit-readme-improver`, `rabbit-reads`, `rabbit-rewrites`, `rabbit-claude-md`). Claude Code expands the variable. On a host that doesn't, such as Codex, resolve it that way by hand. The design rests on three core principles: targeted chunking, persistent settings reuse, and gated execution. ## 1. Targeted chunking and context Rather than sending entire documents (which exceed small model context windows and cause hallucinated edits), the engine chunks flagged prose into focused, contextual units: - **Sentence units with local context**: A tell sitting in a sentence is chunked alongside its preceding sentence context. The model receives the target sentence, the specific problem to remove, and surrounding context to resolve pronoun referents, antecedents, and narrative tone without being asked to rewrite the context itself. - **Passage and block units**: Structural and density tells (`uniformity`, `tier2-cluster`, `tier3-density`) are chunked at the full paragraph level, sized against the endpoint's input budget. - **Unit merging**: Multiple findings landing within the same sentence are merged into a sin