deal-qualification

Use this playbook to turn what you found on the public web into a dealFitScore (0–100), a payingLikelihood (low/medium/high), and a short recommendation.

Signals that make a company a GOOD paying prospect (each is positive):

• Revenue / profitability — meaningful and growing revenue, or a public statement of profitability.
• Funding — a recent raise, a well-known investor, or a public listing (durable ability to pay).
• Headcount momentum — growing team, open roles, or a hiring spree (budget and expansion).
• Enterprise customers — recognizable logos or case studies (they buy, and they pay real money).
• Budget / pricing fit — evidence they already pay for comparable tools or services.

Red flags that lower the score (each is negative):

• Layoffs, hiring freezes, or shrinking headcount.
• Missed payments, lawsuits over unpaid bills, insolvency, or a poor payment reputation.
• A long silence — no verifiable public activity for a year or more.
• No sign of budget: tiny team with no revenue, funding, or paying customers.

Turn the signals into a score with the score script (invoke it via run_skill_script with skillId: "deal_qualification", scriptName: "score"). It takes 0..1 signal strengths and returns a dealFitScore (0–100), payingLikelihood, and a rationale. Feed it your honest read of each signal:

• revenueStrength — how strong the revenue/profitability evidence is (0 = none, 1 = large and growing).
• fundingStrength — funding / public-company strength (0 = none, 1 = well-funded or public).
• growthStrength — headcount / hiring / customer momentum (0 = shrinking, 1 = clearly scaling).
• riskLevel — severity of red flags (0 = none, 1 = severe distress).

Map the size you estimated to companySizeBucket: roughly micro (<10), small (10–50), mid_market (50–1000), enterprise (>1000) employees; use unknown when you cannot ground it.

Always sanity-check the script's number against your own judgement, and justify the score in recommendation with the specific signals you relied on. When evidence is thin, prefer a conservative score and a low or medium payingLikelihood rather than guessing high.