Deal Scoring Desk

Overview

Use this skill as a Busabase-backed review-queue desk for a generic SME financing deal desk (private-credit / revenue-based-financing style). It holds 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 content/kelly-deal-scorer-app/app/js/scorer-model.js, 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 the settings Base.

Default behavior is AirApp-first. Unless the user explicitly asks only for explanation, ensure the mock queue exists (run the seed script below if the candidates Base is empty) and give the user the clickable AirApp URL (or the local preview URL when local preview is explicitly requested). Use chat-only mode only when the user says "纯聊天", "chat only", "不要打开 UI", or similar.

App UI Screenshots

<table> <tr> <td width="50%"><img src="assets/screenshots/overview.webp" alt="Deal Scoring Desk overview"></td> <td width="50%"><img src="assets/screenshots/candidate-detail.webp" alt="Deal Scoring Desk candidate detail"></td> </tr> <tr> <td><strong>Overview</strong><br>Queue-level summary header — score distribution, counts needing review vs. high-confidence — plus the candidate list.</td> <td><strong>Candidate detail</strong><br>Revenue history, red flags, requested principal, and the decision row (approve for term sheet / send back for more data / reject).</td> </tr> <tr> <td width="50%"><img src="assets/screenshots/score-breakdown.webp" alt="Deal Scoring Desk score breakdown"></td> <td width="50%"></td> </tr> <tr> <td><strong>Score breakdown</strong><br>Per-factor raw score, weight, and contribution with an arithmetic trace for every sub-factor, plus the suggested revenue-share rate range.</td> <td></td> </tr> </table>

Mandatory Dependencies

1. Read and follow $kelly-app-skill-creator for product behavior, visual quality, responsive layout, and the complete canonical content/kelly-deal-scorer-app/ artifact.
2. Read and follow $busabase for connection, target Space, node discovery, ChangeRequests, review, and merge behavior.
3. Read and follow $busabase-app-creator for resource modeling, AirApp runtime limits, security, validation, and deployment.

If a dependency is unavailable, preserve this skill's local artifact and product contracts, stop before the unavailable Busabase operation, and report the exact missing dependency. Do not invent a second data backend.

Boundary

• Review-only. The skill scores a candidate queue and records human decisions in Busabase; 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.mjs marks a candidate done.
• The AirApp reads and writes its own two Busabase Bases only.
• Treat candidate financials as sensitive review data; the composite score and every intermediate number are always recomputed client-side from the candidate's raw fields, never fabricated.

Busabase Resources

Two Bases under one application Folder (kelly-deal-scorer), declared in content/kelly-deal-scorer-app/app/js/config.js and the generated template sidecars under content/:

• candidates: one row per candidate business — raw underwriting fields (category, city, requested principal, monthly revenue history, red flags) plus the reviewer's decision (decision-action/decision-comment/ decided-at) and workflow status, all written directly onto the same row. The composite score breakdown is never stored — it is recomputed client-side from the raw fields on every read.
• settings: up to two rows, keyed by record-id/kind: config (base currency + an optional rubric override for the fund's underwriting policy) and run (the current queue's batch id + generated-at).

Resources provision lazily through an idempotent Busabase ChangeRequest the first time the app runs in a Space; see references/scoring-schema.md for exact field shapes.

First Run And Onboarding

On invocation, check the candidates Base. If it's empty, run the trusted seed script to generate the fixed mock queue:

node skills/kelly-deal-scorer/scripts/generate_batch.mjs --apply

There are no credentials to collect beyond Busabase itself — onboarding is just confirming the rubric weights and category risk tiers in the settings Base's config row match the fund's underwriting policy (defaults apply if omitted).

Local App

Default behavior is AirApp-first — give the user the clickable AirApp URL. Start pnpm --dir content/kelly-deal-scorer-app dev only when local preview/debugging is explicitly requested.

Required app views (hash routes):

• #/overview: queue-level summary — score distribution (high-confidence / needs review / low-confidence), workflow counts, and the candidate list.
• #/candidates and #/candidates/<id>: the full queue, filterable by status; detail shows revenue history, red flags, requested principal, score breakdown, suggested revenue-share range, and the decision row. Decisions write directly onto the candidate record through busabase-sdk.
• #/settings: sanitized rubric summary (weights, thresholds), active data provider, and onboarding state.

Demo Mode

• ?demo=1 opens a deterministic, fully offline mock queue (8 candidates across F&B/Retail/Fitness/Education) for documentation and screenshots. Demo mode never reads or writes Busabase.
• lang=en or lang=zh forces UI chrome language for screenshots.

UI language: English and Chinese chrome with Auto default.

Workflow

1. node scripts/generate_batch.mjs --apply (dry run without --apply) writes the fixed 8-candidate mock queue to the candidates Base (resetting every candidate's decision fields to needs_review) and refreshes the settings Base's run row — run it once at setup, and again any time the demo queue needs resetting.
2. Open the app. Overview shows the score distribution and workflow counts; Candidates lists every candidate, filterable by status.
3. For each candidate, open the detail view, review the revenue history chart, red flags, and the full score breakdown (every sub-factor's raw score/weight/contribution with an arithmetic trace), then record Approve for term sheet / Send back for more data / Reject with an optional note — written straight onto the candidate record.
4. node scripts/execute_decisions.mjs --apply (dry run without --apply) re-reads Busabase and marks every approved candidate done, preparing the local term-sheet-draft artifact at the suggested revenue-share rate. It performs no external side effect — no wiring, no signing, no contacting the business.

Read references/scoring-schema.md before editing the app, scripts, or content/kelly-deal-scorer-app/app/js/scorer-model.js.

The Rubric (not a model)

content/kelly-deal-scorer-app/app/js/scorer-model.js documents and implements the entire scoring rubric: five weighted 0-100 sub-factors (revenue stability, growth trend, category risk tier, principal-to-revenue ratio, track record & scale), each with a human-readable arithmetic trace in detail. computeScore() is a pure function — same inputs always produce the same composite score and suggested revenue-share range, so a human reviewer can check every number with a calculator. It backs the trusted seed script (scripts/generate_batch.mjs), the live Busabase read path (content/kelly-deal-scorer-app/app/js/providers/busabase-provider.js), and the offline ?demo= scenario (content/kelly-deal-scorer-app/app/js/providers/demo-provider.js), so all three always agree on scoring.

Safety

• Deterministic scoring only: never call an LLM or external API to produce a candidate's score — content/kelly-deal-scorer-app/app/js/scorer-model.js is plain arithmetic so every number is auditable.
• Never auto-execute a decision the human has not made.
• Do not invent candidates outside the fixed seed set; if the user wants a different queue, add to content/kelly-deal-scorer-app/app/js/scorer-model.js's CANDIDATE_SEEDS and re-run the seed script.

Useful Commands

node skills/kelly-deal-scorer/scripts/generate_batch.mjs --apply
node skills/kelly-deal-scorer/scripts/execute_decisions.mjs --apply
pnpm --dir skills/kelly-deal-scorer/content/kelly-deal-scorer-app dev