| name | anysite-crm-score |
| description | Score CRM companies or contacts against the user's ICP using anysite data (firmographics, funding stage, hiring, tech signals) and write the score into the single mapped score field. Use when the user asks to score leads, rank accounts, prioritize the pipeline, or apply ICP criteria to CRM records. Requires an active CRM connection and a profile with a score field marked overwrite. |
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.
