--- name: kelly-deal-scorer license: MIT description: Review-queue App-in-Skill that scores candidate SME financing deals (revenue-based/RBF-style credit) with a deterministic, fully auditable rule-based rubric — never an LLM or API call. Use when the user invokes $kelly-deal-scorer or /kelly-deal-scorer, wants to review a deal-underwriting queue, score financing candidates, compute a composite score breakdown, see a suggested revenue-share rate range, or record approve/send-back/reject decisions for a private-credit or RBF-style lending pipeline. --- # Deal Scoring Desk ## Overview Use this skill as a local review-queue operator for a generic SME financing deal desk (private-credit / revenue-based-financing style). It ingests a mock queue of candidate businesses (name, category, city, monthly revenue history, requested principal, red flags) and computes a deterministic composite score (0-100) per candidate with a full, hand-recomputable breakdown: each sub-factor's raw score, weight, and contribution, plus a suggested revenue-share rate range. This is a generic, brand-free tool — it does not reference any specific real company, lender, or fund. **The scoring rubric is plain arithmetic in `lib/scoring.ts`, not an LLM or API call.** Every number the app shows can be recomputed with a calculator from the candidate's raw fields and the rubric weights in `config.json`. Default interaction mode: App UI. Unless the user explicitly asks for chat-only handling, check onboarding/config, seed or reuse the local batch, 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. ## App UI Screenshots
Deal Scoring Desk overview Deal Scoring Desk candidate detail
Overview
Queue-level summary header — score distribution, counts needing review vs. high-confidence — plus the candidate list.		 Candidate detail
Revenue history, red flags, requested principal, and the decision row (approve for term sheet / send back for more data / reject).
Deal Scoring Desk score breakdown
Score breakdown
Per-factor raw score, weight, and contribution with an arithmetic trace for every sub-factor, plus the suggested revenue-share rate range.
## Boundary - Review-only. The skill prepares/scores a candidate queue and writes local handoff files; it never wires money, signs a term sheet, or contacts a business. - NEVER treat the composite score as legal or financial advice, and never auto-approve: a human decision (`approve_term_sheet` / `send_back_for_data` / `reject`) is always required before `scripts/execute_decisions.ts` marks anything `done`. - The app reads and writes local files only. Treat candidate financials as sensitive; do not commit `config.local.json`, env files, or `app/.data/`. ## First Run And Onboarding On invocation, check `app/.data/onboarding.json`. If absent/incomplete, ask the user to confirm the rubric weights and category risk tiers in `config.local.json` (copy from `config.example.json`) match their fund's underwriting policy, then write the completion marker. Private config priority: 1. `KELLY_DEAL_SCORER_CONFIG=/absolute/path/to/config.json` 2. `skills/kelly-deal-scorer/config.local.json` 3. `~/.config/kelly-deal-scorer/config.json` 4. `skills/kelly-deal-scorer/config.example.json` as template only When setup is complete and the user confirms, write `app/.data/onboarding.json`: ```json { "completed": true, "completed_at": "ISO timestamp", "config_version": "1" } ``` ## Local App ```bash skills/kelly-deal-scorer/app/start.sh ``` First run installs `hono` + `@hono/node-server`, seeds a mock candidate queue (`scripts/generate_batch.ts`) if none exists, and starts the server on `127.0.0.1`, preferring port `3000` through `4000`, or `KELLY_DEAL_SCORER_UI_PORT` when set. The frontend is zero-build vanilla. ## Demo Mode - `?demo=1` opens a deterministic, fully offline mock queue (8 candidates across F&B/Retail/Fitness/Education) for documentation and screenshots. - `?demo=overview`, `?demo=detail` select named mock scenes. - `lang=en` or `lang=zh` forces UI chrome language for screenshots. - Demo API responses never read or write real candidate/queue files. UI language: English and Chinese chrome with `Auto` default. ## Data Provider - Provider selector env: `KELLY_DEAL_SCORER_DATA_PROVIDER=local` (default). App code reaches storage only through `lib/data-provider/` — see `lib/data-provider/provider-interface.ts` for the contract every future provider (postgres/aitable/notion/busabase) must implement. - Read `references/scoring-schema.md` before editing the batch shape, the rubric, the app, or the scripts. Primary local files: - `app/.data/current_batch.json`: latest scored candidate queue. - `app/.data/decisions.json`: human decisions keyed by candidate id. - `app/.data/execution_report.json`: latest execution run (term-sheet prep / close). - `app/.data/onboarding.json`: onboarding completion marker. - `app/.data/agent.lock`: temporary lock while writing. Use `scripts/generate_batch.ts` to (re)seed the mock queue, `scripts/validate_ui_schema.ts app/.data/current_batch.json` before trusting a batch in the UI, and `scripts/execute_decisions.ts` to apply approved/blocked decisions. ## Views - `#/overview`: queue-level summary — score distribution (high-confidence / needs review / low-confidence), workflow counts, and the candidate list. - `#/candidates/`: candidate detail — revenue history, red flags, requested principal, score breakdown, suggested revenue-share range, and the decision row. - `#/settings`: sanitized rubric summary (weights, thresholds), active data provider, and onboarding state. ## Safety - Deterministic scoring only: never call an LLM or external API to produce a candidate's score — `lib/scoring.ts` is plain arithmetic so every number is auditable. - Never auto-execute a decision the human has not made. - Keep local exports minimal and use stable candidate ids so repeated runs are idempotent.