/decision-makers-map

name: decision-makers-map
description: "Decision-makers mapping skill for Claude Code and Codex: map the buying committee at a target account — economic buyers, champions, technical evaluators, and influencers. Prioritizes who to engage first, surfaces coverage gaps and single-thread risk, and flags recently appointed leaders. Use for outbound prospecting, deal acceleration, and multi-thread selling strategies. Identify accounts by domain or company name. Works in Claude Code, Codex, Hermes-Agent, and Claude Cowork."

Decision-Makers Map

Map the buying committee at one target account: who matters, what role they play, and who to engage first.

Input

`$ARGUMENTS` is an account identifier plus optional context.

• Account identifier (required): a company domain (preferred) or a company name.
• Research context (strongly recommended): why this map is being pulled and what decision it supports. Capture as much as is true: deal stage and value, offering in play, recent activity or stalls, who is already engaged, who is missing, competitive pressure, persona priorities, timing, and any working hypothesis to test.

Example phrasings:

• "Map the buying committee at acme.com. Stage 3 deal, $400K ARR, security platform. We talked to the Director of SecOps but the CISO is quiet: find the economic buyer and any procurement blockers."
• "Decision-makers at Globex for a Data Platform pitch. Cold prospecting, likely Eng or Data leadership, flag CFO if cost story applies."
• "Renewal in 90 days at initech.com, champion left last quarter. Find the new owner and anyone who could block renewal."

If only a bare token is supplied with no context, ask once. If declined, default to general committee mapping for prospecting and state that assumption in the TL;DR.

Workflow

1. Anchor on purpose. Restate the research context in 1-2 sentences as the map purpose. Derive priority personas (3-6 titles or functions), priority seniority levels, priority departments, and any named hypotheses ("find the economic buyer").

2. Resolve the account: **match a business** from domain or name, then **enrich a business** with firmographics for company name, headcount, revenue range, industry, and HQ. Domain-variant sanity check: if a major-brand input resolves to a tiny headcount with a "Corporate Managing Offices" or "Hotels and motels" classification, the match likely routed to a registered-agent shell entity. Re-try with the alternate domain or company-name string. If no confident match, surface the ambiguity rather than guessing.

3. Discover canonical values for the committee search. For every priority title, discover canonical job-title strings the filter surface accepts. Title-only filters are NOT enforced as strict exact-match: combine title with a seniority filter to tighten.

4. Size the committee. Run a count (statistics, not retrieval) scoped to the resolved company plus the priority title and seniority filters. Country caveat: country scoping is approximate at the statistics layer; read the per-location category breakdown and sum the requested ISO-2 codes. If the total is very large (above ~500), tighten with department or higher seniority; if very small (under ~10), loosen by dropping seniority or adding adjacent titles.

5. Sample the committee broadly (retrieval, not filtering). Sample a small slice of prospects matching the resolved account plus the title and seniority filters, using presence-of-email as a contact-quality proxy. Treat the preview as a sample, not a ranked top-N. Run a second sample for adjacent functions named in the context (Procurement, Legal, Finance) so coverage gaps are visible.

6. Account-context signals. Fetch business events for the resolved account scoped to the last 90 days. There is no executive-move or departure enum: closest signals are an undifferentiated `employee_joined_company` and department-level hiring events. Flag this as a capability gap in the brief. Also triage funding and strategy signals that change the buying dynamic. Optionally enrich the account with strategic insights, challenges, and competitive landscape when a named hypothesis ties to a strategic theme. Event-attribution sanity check: confirm any event row actually names the target account before quoting it.

7. Enrich and deep-research the top contacts. Merge prospects from step 5, dedupe, and rank by seniority, persona fit, hypothesis fit, and presence of email or LinkedIn URL. Enrich the top contacts with contacts and profiles, defaulting to email-only (cheaper; switch on phone only for SDR dialer flows). For the top 3-5 stakeholders, surface recent activity from the business-side LinkedIn-posts enrichment (per-prospect LinkedIn-posts is not exposed).

8. Synthesize. Each retrieval is raw context; decide what makes the map.

• Events triage: keep new-hire, executive-move, and promotion signals at director-level and above, especially in priority or adjacent functions. For each newly-named person, **match a prospect** if not already enriched and add to the committee with a RECENTLY APPOINTED flag and event date.
• Role classification (conservative): Champions require explicit engagement evidence (CRM activity, demo, prior emails); title alone is never sufficient, default to Potential Champions under Influencers. Technical Evaluators are director-and-above in Engineering, IT, or the function being sold to. Economic Buyers hold budget: C-Level Finance, the sponsoring function head, or CEO for strategic deals. Influencers use named sub-buckets (Procurement, Legal / Compliance, Operations, Adjacent Marketing, HR / Talent, Potential Champions).
• Hypothesis check: address each named hypothesis as confirmed, contradicted, or unresolved.
• Source tagging: tag every contact with where it came from (committee sample, event-derived, freshly matched).

9. Write the exec summary last. Re-read the body, then write the TL;DR at the top, framed by the map purpose.

Output Format

Decision-Ma