kelly-behavior-predictlisted

Local dashboard App-in-Skill over mock user-behavior funnel data (browse → search → compare → booking attempt/abandon → complete) for a generic consumer booking product. Use when the user invokes $kelly-behavior-predict or /kelly-behavior-predict, wants to review funnel drop-off, per-segment predicted next actions, or backtest a rule-based "predicted next action" heuristic against a mock historical sample. Fully offline mock data and a deterministic, hand-recomputable rule — never a real ML/LLM model, never a live system.
mr-kelly/skills · ★ 3 · AI & Automation · score 74

Install: claude install-skill mr-kelly/skills

# Predictive Recommendation Analytics Desk ## Overview Use this skill as a local, read-mostly analytics operator for a **generic, brand-free** consumer booking/e-commerce product. It aggregates a fully deterministic mock user-behavior dataset — session archetypes ("segments"), their funnel drop-off, a rule-based "predicted next action" per segment, and a prediction-accuracy backtest — into one file-backed App-in-Skill dashboard. Default interaction mode: App UI. Unless the user explicitly asks for chat-only handling, ensure the mock dataset exists (`scripts/generate_batch.ts`), start/reuse the local app with `app/start.sh`, and give the actual local URL. Use chat-only mode only when the user says "纯聊天", "chat only", "不要打开 UI", or similar. This is primarily a **dashboard** app type (read-mostly, no approval lifecycle). It carries exactly one narrow human-review surface: marking a segment's prediction rule "trusted" or "needs recalibration" with a note, recorded in `app/.data/decisions.json`. That review never edits the rule itself, the dataset, or any live system — it is a review record only. ## App UI Screenshots <table> <tr> <td width="50%"><img src="assets/screenshots/overview.webp" alt="Analytics Desk overview"></td> <td width="50%"><img src="assets/screenshots/funnel.webp" alt="Analytics Desk segments"></td> </tr> <tr> <td><strong>Overview</strong><br>Overall funnel drop-off (browse → search → compare → booking attempt → complete), total sessions,