Anysite Crm Score

CRM Score

Deterministic-ish prioritization: explicit rubric, evidence per company, score written to exactly one mapped field.

Prerequisites

Active CRM connection. Profile must map a score target field with mode: overwrite (scores are re-computed by design). Not mapped → offer to store nothing and just report, or send the user to re-run /anysite-crm-setup. The Writing rules in anysite-crm-setup apply to every write. Cap a scoring run at ~50 companies and state the credit estimate (evidence calls × price) before fetching; more → propose tiers or a narrower list.

Flow

1. Fix the rubric BEFORE fetching data

Get ICP criteria from the user, or derive them with anysite-crm-lookalikes logic from closed-won records. Turn them into a written rubric with weights, e.g.:

industry match (0-3), size band (0-2), geo (0-1), funding stage (0-2),
hiring in buyer function (0-1), tech/context signal (0-1)  → 0-10

Show the rubric, get a nod. The rubric goes into the report verbatim — scores must be explainable and reproducible.

2. Fetch evidence (cheap-first)

crm_query_records(object_type="companies", ...) → record_id, name, domain, existing fields

• Base firmographics: search_sql_companies by website — default one domain per call; OR-DSL batching ({website: "a.com|b.com|...", count: 10× domains}) is an optimization with a verification tax (see the anysite-mcp resolve recipe). Never count: 1 — the search is substring match, and a common-token domain comes back with only look-alikes even in a single-domain call. Verify the exact website match per domain via query_cache with an explicit limit (default is 10 — a 20-domain batch needs more). Unverified match = no evidence, score that criterion "unknown"; a domain that never comes back exact-matched is resolved via webparser/parse on the site itself, per the same recipe.
• Stage/funding (only if the rubric needs it): take the alias from crunchbase_link, which the domain-resolve above ALREADY returned — free, no lookup. Only when it is empty and the company is plausibly venture-backed, fall back to the live crunchbase/search (20cr, fuzzy — verify name+domain) → crunchbase/company. Skip entirely for obviously non-venture companies. Note leadership_hires[] is unusable as an ICP criterion for SMB/startup targets — measured empty on 6 of 6 live accounts, including a 281-person one.
• Hiring probe (only if in rubric): prefer the numeric id from organizational_urn of the domain-resolve you already did → search_jobs {company: [{"type": "company", "value": "<id>"}], count: 20}. No resolve → search_companies {keywords: name, count: 5} + verify by name/industry (its urn is already the {type, value} object).
• Team-shape evidence (great for "engineering-led vs sales-led" criteria): linkedin/company/company_employee_stats (1cr, needs company URN) — absolute headcounts by function (verified: Engineering 26 / Sales 14 on a 79-person company). Don't sum its locations array (nested buckets: US ⊃ state ⊃ metro); cross-check totals against employee_count.

Company size in the rubric: use employee_count, never employee_count_range — the two can contradict each other in one record (verified: 1465 vs "201-500"), and the range would misfile the size band silently. Range only as fallback when the count is empty, noted.

Skip any evidence source whose rubric weight is zero. State per-company data gaps — a company with missing data gets a confidence note, not a silently low score.

3. Score

Apply the rubric in-session. For every company keep one line of evidence per criterion. No evidence → that criterion scores 0 with an "unknown" marker, never a guessed value.

4. Write and report

crm_upsert_companies(records=[{domain: "<domain>", properties:{<score field>: <value>}}],
                     allow_create=false, overwrite_properties=[<score field>],
                     dry_run=true)  → confirm → write → run_id

Company upserts match ONLY by domain — pull domain when querying records; companies without one get a score in the report but no write. Write ONLY the score field (plus scored_at if mapped). Report: top-N with evidence lines, distribution summary, gaps. Contacts scoring (persona fit) works the same way against contact records with linkedin/user evidence — same rubric-first discipline.

Boundaries

• Score ≠ routing: never touch owner/stage/status based on a score.
• Re-scoring overwrites by design — that's why the profile must explicitly mark the field.
• Intent-level signals (fresh funding, exec hires) belong to anysite-crm-signals; this skill measures fit. The two compose: fit × recency of signals = priority.