account-sourcing

Account Sourcing

Turn an ICP from a strategy document into pipeline: a ranked list of real accounts worth pursuing, each with public evidence for why, and a visible reject pile. Two rules govern everything here: an account belongs on the list only if you can point to the evidence, and a list that shows no rejects is hiding its judgment.

The first deliverable companies expect from go-to-market engineering is exactly this: the target account list, mapped to ICP criteria, scored, and refreshed. Outreach comes later and depends on it.

What this builds (and what it doesn't)

• It builds the account layer: a ranked, evidenced account list (account-list.md), a machine-readable handoff (accounts.json) shaped for Clay, a CRM, or a sequencer, and the scoring rubric used (rubric.yaml).
• It does not draft outreach. Signal-to-message work belongs to outbound-engine, which consumes this skill's output. It also does not harvest individuals: sourcing stays at the account level, with a suggested entry role per account taken from the ICP's buying committee. People come later, at outreach time, at role level.

Fit and limits

Built for products bought by organizations (a team or committee buys). For individual-buyer products, "accounts" is the wrong unit: sourcing there means finding channels and communities, which the ICP's journey section already maps. Say so and stop rather than forcing an account frame.

Inputs (ask for what is missing)

Input	Why it matters	If missing
Account-level ICP	The criteria, disqualifiers, and committee come from here	Run icp-research first, or gather a minimal version: what they sell, who buys, 2-3 disqualifiers
Target list size	Sets depth vs. breadth; default 25-50 evidenced accounts over thousands of thin rows	Default to 25-50
Geography / segment constraints	Bounds the universe	Ask
Suppression list	Existing customers, partners, open opportunities must be excluded	Ask; note as a gap if none provided
Seed lists (optional)	Competitor customers, event exhibitors, association rosters accelerate Phase 3	Proceed without

The single most useful ask: "Name your 5 best current customers, and name who should never be on this list." The first seeds lookalikes; the second sharpens disqualifiers and suppression.

The workflow

Six phases. Show the user the query spec and source plan (end of Phase 2) and the scored draft with its disqualifications (end of Phase 4) before packaging.

Phase 1: Compile the query spec

Read the ICP and extract, verbatim, the account-level criteria: firmographics, technographics, behavioral signals, and disqualifiers. Convert each into a machine-actionable statement with a target value ("employee count 1,000+", "regulated sector: finance, government, health, telecom", "hiring for AI platform roles"). Criteria that no public source can observe get downgraded to verify-later, never guessed.

Phase 2: Map criteria to sources

For each criterion, pick the public source that can actually answer it, using references/source-map.md. The output of this phase is a source plan: criterion, source, query, and the evidence tier the source provides. Job postings answer the most (stack, initiative, timing) and are the default first stop. Show the query spec and source plan to the user before sourcing.

Phase 3: Build the universe

Assemble candidate accounts from seed strategies: lookalikes of best customers, competitor customer pages, category and association lists, event exhibitor and speaker lists, registry and filing sweeps, and job-board sweeps for the ICP's signature roles. Dedupe by domain. Apply the suppression list. Cap the universe at roughly 3-4x the target list size so scoring stays evidence-grade.

Phase 4: Enrich, score, disqualify

For each candidate, fill the rubric fields with evidence: value, source, and date per field. Unknown stays unknown. Then score:

• Judgment lives with you: whether an account meets a criterion, and on what evidence.
• Arithmetic lives with the script: scripts/score_accounts.py reads accounts.json plus rubric.yaml, applies weights, hard disqualifiers, and tier thresholds deterministically, and emits the ranked list. Same inputs, same output, auditable.

Disqualifications are output, not noise: every rejected account keeps its reason. A list with no rejects has not been judged. Show the scored draft to the user.

Phase 5: Signal-tag the survivors

For the qualified tier, attach dated why-now signals: hiring (live postings for ICP-relevant roles), funding or financial events, leadership changes, announced initiatives, regulatory shifts. scripts/hiring_signal.py checks public ATS job boards (Greenhouse, Lever, Ashby JSON endpoints, no keys) for ICP-relevant postings live. A signal without a date and source does not count.

Phase 6: Package, route, and set the cadence

Assemble the deliverable per references/output-template.md: the ranked table with receipts, the disqualified table with reasons, gaps and verify-laters, and accounts.json for handoff. Then route: author owner_team and next_action per account, because a list without owners and actions is notes, not work product, and render the executive briefing (scripts/render_briefing.py, brand-flavored accent only, disclaimer always on). Set the refresh cadence: signals decay in weeks, firmographics in quarters. A list without a refresh rhythm is a snapshot, not a system.

The receipts rule

Every scored field cites its source and date. Evidence tiers (defined in the source map): primary sources outrank press, press outranks directories. Load-bearing decisions need primary or reputable secondary evidence. No account is ever invented, and no fact is ever assumed. An honest "verify" beats a confident guess.

Scripts

Script	What it does	Network	Keys
scripts/score_accounts.py	Deterministic scoring: accounts + rubric in, ranked tiers and disqualifications out	None	None
scripts/hiring_signal.py	Live check of public ATS JSON boards for ICP-relevant postings per company	Public endpoints only	None
scripts/render_briefing.py	Executive briefing from accounts.json: lanes by owning team, next action per card, rejects with reasons, self-contained HTML	None	None

The split is deliberate: judgment and evidence-gathering are reasoning work; scoring math and live checks are deterministic work that should be repeatable and auditable.

Composes with other skills

• icp-research (upstream): its account-level ICP section is this skill's query spec, and its buying committee supplies the entry role per account.
• outbound-engine (downstream): consumes the qualified accounts and their signals to draft committee-aware, voice-matched outreach.
• market-prioritization (sibling): ranks markets and segments; this skill ranks accounts within them.

Failure modes to avoid (the quality bar)

• Invented accounts. The cardinal sin. Every account exists; every fact cites.
• Unanswerable criteria. A criterion no public source can observe produces guesses; downgrade it to verify-later in Phase 1.
• No rejects. A list with no disqualifications has not been judged; it has been collected.
• Undated evidence. A signal without a date is trivia; last year's funding round is not a why-now.
• Volume worship. 500 thin rows lose to 30 evidenced ones; the cap exists for a reason.
• Suppression neglect. Sourcing your own customers embarrasses everyone downstream.
• One-and-done. No refresh cadence means the list is already decaying.

Bundled references

• references/source-map.md: which public source answers which ICP criterion, evidence tiers, query patterns, tool mapping, and access ethics. Read at Phase 2.
• references/output-template.md: the exact deliverable structure, the accounts.json schema, and the rubric.yaml format the scorer consumes. Read at Phase 4 and 6.
• examples/cohere/: a worked example sourcing real accounts for Cohere from public evidence, scored against the Cohere ICP from icp-research, rejects included.