map-contacts

name	map-contacts
title	Map contacts
description	Maps the buying committee at a named account using CRM history, past interactions, LinkedIn, and prospect search. Produces persona coverage, relationship strength, gaps, and next moves.
category	Prospecting

Instructions

Setup state. Not yet configured for this org. Load the Setup sub-page to confirm the persona model (buying-committee shape) is defined and identify which relationship-history data sources are available (CRM, email sender, Fireflies, LinkedIn, prospect search) before mapping anything. (After setup is performed, rewrite this paragraph via swan-update-skill to describe the current state — persona model loaded, which of the five data sources are available, and last-refreshed date — so future runs see the current configuration without re-checking.)

Step 1 — Confirm the target account + intent

Get the company domain or name. Get the intent in one line: net-new ABM mapping, renewal prep, champion-tracking, or expansion of a single-threaded deal. Each shifts priority slightly (renewal leans heavy on current users + economic buyers; champion-tracking leans heavy on past contacts who moved).

Step 2 — Pull the persona model

Load the org's defined personas. Hold the named roles, their title aliases, and their JTBDs as the schema for the map. Do not invent personas — the map is keyed to what the org has saved.

Step 3 — Mine known relationships first (highest signal)

Lead with data the org already has. Each source is checked if connected; skip and note if not.

• CRM. Search Companies / Contacts / Deals for the account. Capture every named contact with role, last activity, owner, and status (still here, departed, dormant).
• Sent email. For each connected sender, search sent items for the account's domain. Pull every person ever emailed, last-touch date, and the thread topic in one line.
• Meeting transcripts. Use FIREFLIES_GET_TRANSCRIPTS filtered by participant email domain matching the account. Capture who joined, when, and what was discussed.
• Prior sequences. Use swan-search-sequences filtered to the account. Note who was reached, who replied, who booked.

Aggregate by person before moving on — one row per human, even if they appear across all four sources.

Step 4 — Find people who moved (the high-value bit most skills miss)

For every past contact across CRM and email who is no longer at the account, check current employer. A closed-lost champion now at a new target account is the highest-signal person to reach this quarter. For every current contact at the account, check if they joined recently — new-to-role plus a prior history elsewhere is a warm path that didn't exist before.

Use swan-linkedin-social-media-presence on a known LinkedIn URL to confirm current employer. Reach for swan-enrich-contact only when the user is about to act on a specific person — credits matter.

Step 5 — Fill gaps with net-new prospect search

After known-relationship data is exhausted, run swan-search-employees for the persona roles still missing from the map. Net-new contacts join the map but flagged explicitly as "no relationship history" — they read differently from a warm contact and the next move should reflect that.

Step 6 — Compose the structured map

Format as a tight per-persona section (or a single table). For each persona role (Champion / Economic Buyer / Technical Evaluator / End User / Blocker), list each named person with: full name, title, current employer (called out if different from the account), relationship strength (cold / warm-via-X / former-X-now-here / current-with-history / net-new), last touch (date + channel + who), recommended next move.

Flag role gaps — personas with no candidate or only weak coverage — at the top of the map, not buried at the end.

Not a wall of names. The map's value is the relationship signal per person, not the count.

Step 7 — Hand off

Recommend the next move in plain verbs: who to outreach first (warm paths first), which gaps to close via further prospecting, which moved-contact play to activate this week. One paragraph.

What good looks like

• Every persona role in the org's model has at least one named candidate OR an explicit gap flag. No silent absences.
• The map distinguishes "current contact with history" / "former contact still at the account" / "former contact who moved" / "net-new no history" — these have very different next-move implications and the format makes that obvious at a glance.
• Past relationships are surfaced front-and-center, not buried under net-new prospect rows. That's the whole point of going beyond decision-maker hunting.
• Recommends next moves by persona, not just a list of names. A reader can decide what to do without a second pass.
• A closed-lost champion now at the target shows up as the top recommended outreach. If it doesn't, the skill missed the highest-value signal it exists to surface.

What gets overlooked: treating warm and cold contacts identically; ignoring former contacts who moved companies (the single highest-signal hook); listing net-new prospects without flagging the absence of any relationship; missing role gaps because the map only shows what was found.

Rules

• MUST use the org's defined persona model. Never invent personas for this skill.
• MUST search known-relationship sources (CRM, sent email, transcripts, prior sequences) before prospect-search for net-new.
• MUST surface relationship history per person — strength, last touch, who — as a first-class column, not a footnote.
• MUST flag former-contact-now-elsewhere candidates explicitly. These are usually the highest-signal hooks in the entire map.
• MUST flag role gaps — personas with no candidate or weak coverage — at the top of the map.
• NEVER enrich every net-new contact. Enrich only the slice the user is about to act on.
• NEVER replace or override the persona model. The org's saved buying committee is the schema.
• NEVER drop former contacts because they no longer work at the account. They're the reason this skill exists.

Specificity sub-pages this skill will grow

Add over time as the org's mapping motion sharpens:

• RenewalPrep — mapping for an upcoming renewal. Heavier weight on current users and the economic buyer; surfaces churn risk in the committee (champion left, EB changed, user base attritted).
• ChampionTracker — track when a known champion changes companies. Watches LinkedIn for the employer change; surfaces the new account as a fresh target the moment the move lands.
• MultiThreadExpansion — expand the map for an existing deal that's single-threaded. Diffs the current map against the persona model and recommends specific multi-thread additions with warm paths where they exist.