kelly-behavior-predict

name

kelly-behavior-predict

description

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.

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

Overview Overall funnel drop-off (browse → search → compare → booking attempt → complete), total sessions, overall backtest accuracy, and how many segments still need a trust decision.	Segments Per-segment cards: session count, dominant predicted action, backtest accuracy/F1, and the current trusted / needs-recalibration badge.
Segment detail Segment funnel, predicted-action distribution, sample sessions (predicted vs. mock actual), the matched/unmatched rule triggers that drove the prediction, and the trusted / needs-recalibration review panel.

Boundary

• Fully mock, fully offline. There is no real user data, no live product integration, and no real ML/LLM call anywhere in this skill — every "predicted next action" comes from a fixed if/else rule in lib/predict.ts.
• The app reads and writes local files only. It must not call any external service.
• The one human action (mark trusted / needs recalibration + note) writes app/.data/decisions.json only. It never changes the rule, regenerates the dataset, or triggers any other system.
• Do not name any real company, brand, or product. Keep the product profile generic ("Example Booking Co.", configurable in config.local.json).

First Run

On invocation, ensure the mock dataset exists:

node skills/kelly-behavior-predict/scripts/generate_batch.ts
node skills/kelly-behavior-predict/scripts/validate_ui_schema.ts

This writes app/.data/dataset.json (deterministic — same seed always produces byte-identical output) and an empty app/.data/decisions.json if one doesn't already exist. There is no external onboarding required (no credentials, no live connection) — the dataset generator IS the setup step.

Local App

Start the dashboard with:

skills/kelly-behavior-predict/app/start.sh

The app uses local HTTP on 127.0.0.1, preferring port 3000 through 4000, or KELLY_BEHAVIOR_PREDICT_UI_PORT when set. First run installs hono and @hono/node-server; the frontend is zero-build vanilla.

Demo Mode

• ?demo=1 opens the same deterministic mock dataset the app always uses (it has no live data source to demo against) at the Overview.
• ?demo=segments, ?demo=backtest, and ?demo=detail select a starting route for screenshots/docs (segments grid, backtest view, or the price_sensitive_browser detail pane).
• lang=en or lang=zh forces UI chrome language for screenshots.

Data Model

Read references/ui-schema.md before editing the app, scripts, or lib/. Primary local files:

• app/.data/dataset.json: the generated mock funnel/segment/backtest dataset.
• app/.data/decisions.json: human review decisions, keyed by segment id.
• app/.data/onboarding.json, app/.data/agent.lock: standard App-in-Skill scaffolding, present for consistency even though this skill needs no external onboarding.

Data provider: lib/data-provider/local-file-provider.ts (default, env-selected via KELLY_BEHAVIOR_PREDICT_DATA_PROVIDER=local). The Hono app and scripts reach storage only through lib/data-provider/, never node:fs directly, so the same app stays platform-neutral.

The Rule (not a model)

lib/predict.ts documents and implements the entire prediction rule: a short, ordered list of if/else triggers over four mock session signals (cart_abandon_count, price_check_count, days_since_last_visit, session_length) plus reached_stage. The first matching trigger determines predicted_action; the segment detail view shows every trigger and whether it matched, so "why this prediction" is always inspectable. lib/backtest.ts computes a standard precision/recall/F1 confusion-matrix summary comparing predicted_action against a seeded mock actual_action per session — this is what the Backtest view renders, at both the overall and per-segment level.

Views

• #/overview: overall funnel drop-off, total sessions, overall backtest accuracy, and how many segments still need a trust decision.
• #/segments: per-segment cards (funnel size, dominant predicted action, backtest accuracy/F1, decision badge).
• #/segments/<id>: segment detail — funnel, predicted-action distribution, sample sessions (predicted vs. actual), rule triggers, and the decision panel (mark trusted / needs recalibration + note).
• #/backtest: prediction-accuracy backtest — overall and per-segment precision/recall/F1 tables.
• #/settings: sanitized config summary — data provider, product profile, target-precision note, and dataset seed.

Safety

• Never invent real user data or a real ML/LLM call; keep the rule in lib/predict.ts the single source of truth for every prediction shown.
• Do not commit app/.data/, config.local.json, or any local env file.
• The decision panel is a review record only — it must never regenerate the dataset, edit the rule, or reach any external system.

Execution reports

Re-read the active provider's decisions immediately before any approved execution. Record each concrete operation, target, status, timestamp, and error in the provider-backed execution report; keep app actions local-only.