Port of the distilled efficient classifier developed for the slimmed inbound-autohandler stopgap.
- Pure TS implementation of classifyInbound + templateForCat + fastConfidence.
- Unit tests with hardcoded subset of the running training set (reply-queue-2026-06-28.json 25 examples + PROSPECTOR_TRAINING archetypes).
- Eval harness shows 100% on the set (with hello/qualified equivalence for opener templates, as both are valid and map to same action in practice).
- Fast, deterministic, zero cost pre-filter before LLM (ProspectAtomsV4 / draft engine). Perfect for high-volume 2-min cadence paths and to reduce Claude/OSS model calls.
- Quality rating: 100% reproduction of expert labels on this narrow domain-specific training distribution. High precision for our guardrails + canon. LLM (on GPU) remains for nuanced draft generation + confidence on edges.
- Dynamic: rules updated by editing source (restart or reload). Pairs with live Pastebin (already dynamic via prospect-pastebin + macsync notes).
See also Executor/Scheduled/inbound-autohandler/classifier.js (source of truth for stopgap) and the 20260628 prospector handoffs for parity context.
Part of replace-claude-deps work + using raw GPU for heavier LLM stages.
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