/account-fit-rank

name: account-fit-rank
description: "Lead scoring and buying signals skill for Claude Code and Codex: rank a list of accounts by ICP fit, buying intent, real-time trigger events, and workforce momentum. Returns composite score (0-100), tier (A/B/C), and a 'why now' signal per account. Use for account-based selling, ABM list prioritization, territory planning, signal-based selling, and buyer-intent ranking. Triggers on 'score these accounts', 'rank by ICP fit and intent', 'prioritize this account list', 'which accounts have the strongest buying signals', 'ICP scoring', 'account prioritization'. Works in Claude Code, Codex, Hermes-Agent, OpenClaw, and Claude Cowork."

Account Fit Rank

Score and tier a list of accounts on four axes (fit, intent, trigger, workforce) using firmographics, technographics, intent topics, events, and workforce trends, then apply a transparent weighted composite the calling model computes from the returned data.

Input

• Accounts (required): list of business IDs, company names, domains, or a mixed CSV.
• Use case (default `prospecting`): one of `prospecting`, `abm`, `territory_planning`, `pipeline_acceleration`. Shifts tier thresholds and recommended actions.
• ICP definition (required): industries, employee buckets, revenue buckets, country or region, optional tech-stack vendors, optional intent topics. Capture inline; no persisted ICP exists.
• Weight overrides (optional): `{fit, intent, trigger, workforce}` summing to 100. Default `45 / 25 / 25 / 5`.
• Tier thresholds (optional): `{A, B}`. C is the remainder. Default `A>=75, B 50-74`.

Workflow

1. Lock the ICP and intent topics. Restate the ICP from the user. Discover canonical values for every free-text dimension (industry, technology, intent topic, city). Resolve intent topics one term at a time: fuzzy multi-term queries fail silently. If a tag does not resolve, drop it and flag that axis as configuration-gap, not signal-absent.

2. Resolve identifiers. Route inputs by shape: existing business IDs pass through; domains and names resolve via business match, with an optional country tiebreaker for names. Never silently pick a winner: surface top candidates for ambiguous rows and ask for confirmation. For high-collision names, require domain confirmation before scoring. Sanity-check the resolved firmographics: if a major-brand input returns 1-50 employees and Corporate-Managing-Offices category, the match likely routed to a shell entity. Retry with the alternate domain or the name string. Every input ends as auto-resolved, verified, ambiguous, or failed.

3. Pre-flight relationship context. Tag each resolved account against any user-supplied competitor / customer / partner lists before scoring so a "pursue this competitor" line is never produced silently.

4. Fetch firmographic, technographic, and signal data in small chunks end-to-end (resolve, enrich, score, write row, discard raw payloads). Per chunk: enrich with firmographics, technographics, recent LinkedIn posts, funding and acquisitions, workforce trends, strategic insights, and website changes; then fetch business events scoped to the last 90 days for funding rounds, leadership changes, product launches, and expansions. If the ICP includes intent, size intent-topic exposure separately; if no topics resolved in step 1, set intent weight to zero and redistribute. Drop raw payloads after extracting the per-axis inputs and the single winning signal for "why now".

5. Score each axis (calling model computes from the fetched data):

• Fit (0-100): banded firmographic match. Industry primary = 25, adjacent = 10; employee bucket in band = 20, one off = 12, two off = 4; revenue bucket same banding, max 20; geography country = 15, region = 8; company age in window = 10; vendor match if specified = 10.
• Intent (0-100): 90 for 3+ resolved topics active, 70 for 2, 50 for 1, 0 for none. Record the strongest topic. If no topics resolved, axis = 0 with `configuration-gap` and the weight redistributes.
• Trigger (0-100): `event_score = type_weight * recency_factor`. Type weights: M&A / funding / new CEO = 95; product launch, hiring surge, major website change = 75; partnership, new facility = 55; generic announcement = 25. Recency: 0-14d = 1.0, 14-30d = 0.7, 30-60d = 0.4, 60-90d = 0.2, older = 0. Account trigger = max event_score, capped at 100. Verify the event headline actually mentions the target: industry-wide articles can cross-attribute.
• Workforce (0-100): headcount up 10%+ in 90d or target-department hiring surge = 80-100; modest growth = 40-70; flat or shrinking = 10-30; no data = null and the weight redistributes.

6. Composite, tier, and "why now". Composite = round(weighted sum / 100). Cap any axis with no data at null and redistribute proportionally; surface the redistribution. Assign tier from thresholds (use-case overrides: `abm` A=80 / B=55, `pipeline_acceleration` A=65 / B=40). "Why now" is one sentence anchored on the strongest underlying signal, never the composite restated. For strong trigger with low fit, be explicit ("Do not pursue: fresh CEO change but the revenue bucket mismatch keeps this in C.").

7. Iterate. Offer: adjust weights and recompute from cached axes; tighten thresholds; drop tier C; swap the ICP; drill into one account with deeper enrichment (challenges, competitive landscape, ratings); add accounts and rescore. Only "add accounts" or "swap ICP" require new calls.

Output Format

TL;DR

Account Fit Rank, N accounts. Use case, weights, thresholds. Resolution counts (resolved / ambiguous / failed; flag if confirmation required). Tier distribution. Top 3 accounts each with a one-line "why now".

Resolution Summary

Table: Input, Resolved To, Business ID, Confidence, Status (auto-resolved, verified, ambiguous, failed). For each ambiguous row, list candidates with industry, headcount, revenue bucket, country and ask the user to pick.

Ranked Accounts

Sorted by composite descending. Use `-` in any