nlp_deterministiclisted

Classify and extract from text WITHOUT an LLM — intent classification (lexical overlap + local embedding), entity extraction (regex/gazetteers for urls/emails/ips/paths/env vars/flags/numbers), and stopword-filtered keyword frequency. Deterministic, instant, 0 cloud tokens. Use instead of asking a model to classify text or extract entities, and as the routing/intent layer that keeps cheap language decisions off the LLM.
zedarvates/botte-secrete · ★ 1 · AI & Automation · score 65

Install: claude install-skill zedarvates/botte-secrete

# nlp_deterministic — classify & extract without a model Many "ask the model to classify / extract" calls don't need a model: a rule + gazetteer + local-embedding pass is **deterministic, instant, 0 cloud tokens**. This is the NLP half of the determinism program (alongside the combinatorial solvers): replace LLM reasoning for structured language decisions with exact, repeatable computation. ```bash python -m skills.nlp_deterministic.cli classify "speed up my SQL" perf=fast,optimize,slow auth=login,token python -m skills.nlp_deterministic.cli entities "GET https://x.io from 10.0.0.1 with $TOKEN --json" python -m skills.nlp_deterministic.cli keywords "cache the cache so queries stay fast" ``` - **classify(text, intents)** — `intents = {label: [keywords]}`. Score = keyword recall blended with a local hash-embedding cosine ([[ingest]] embedding + [[vector_protocol]] cosine, offline). Returns the best label + per-label scores. - **extract_entities(text)** — regexes for urls, emails, IPs, file paths, env vars (`$VAR`/`%VAR%`), CLI flags (`--flag`), and numbers. - **keywords(text)** — stopword-filtered (EN+FR) frequency keyphrases. Exposed via [[llm_mcp]] as `nlp_classify` and `nlp_extract`. Pairs with [[auto_router]] (effort/intent before routing) and [[skill_finder]]. Deterministic classification is the cheapest possible routing/triage layer — 0 tokens, always the same answer.