From coldiq
Build and run scoring models that tier raw company lists into campaign-ready segments, and point each scored data point at the ColdIQ endpoint that sources it. Use when building a scoring model, tiering a TAM, defining signal groups and point allocations, setting tier thresholds, cleaning a list before scoring, or running a Python scorer on an export. Triggers on "score companies", "tier the list", "scoring model", "TAM scoring", "ICP fit score", "tier thresholds", "100-point model", "qualify accounts". Do NOT use for enrichment/search to BUILD the list (see apollo-search / coldiq-search-enrich), signal sourcing (see signal-detection), or list dedup (see list-dedup).
How this skill is triggered — by the user, by Claude, or both
Slash command
/coldiq:tam-scoringThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Assign every company a numeric score from observable signals, then bucket into tiers. ColdIQ
Assign every company a numeric score from observable signals, then bucket into tiers. ColdIQ models run 80–100 points across 4–6 signal groups. The math is local; this skill notes which ColdIQ endpoint provides each scored data point so the model runs on fresh data.
| Scored data point | Method | Path | Credits | Endpoint ID | Notes |
|---|---|---|---|---|---|
| Firmographics (size, revenue, industry, funding) | POST | /v1/coldiq/enrich/company | 1 | coldiq.enrich.company | One call for most groups |
| Large-scale company list | POST | /v1/ai-ark/companies | per result | ai_ark.companies.search | Build the list to score |
| Tech-stack signal | POST | /v1/builtwith/domain | flat | builtwith.domain | Complexity/specialization group |
| Funding / PE backing | GET | /v1/signalbase/funding-signals | unknown | signalbase.funding_signals | Ownership/bonus groups |
Score from enriched data — don't pay to enrich rows you'll DQ. Filter obvious DQs first, then:
→ POST /v1/coldiq/enrich/company · 1 cr · coldiq.enrich.company
| Tier | Score | Action |
|---|---|---|
| Tier 1 | 65+ | Top priority, send first, best personalization |
| Tier 2 | 50–64 | Strong fit, second wave |
| Tier 3 | 35–49 | Marginal, volume plays only |
| DQ | <35 | Do not send |
BOGUS_NAMES = {"local my business", "google ai plugin", "auto-entrepreneur"}
def is_bogus(name):
l = name.strip().lower()
return l in BOGUS_NAMES or any(s in l for s in ["follow us", "test account"])
# Remove slug franchises (keller-williams-realty-dpr), flag extreme values, dedup by name (keep top score).
def score_company(row):
total, breakdown = 0, []
for label, fn, field in [("Scale", score_scale, "# Employees"),
("Revenue", score_revenue, "Annual Revenue"),
("Industry", score_industry, "Industry"),
("Custom", score_custom, None)]:
pts, reason = fn(row if field is None else parse_number(row.get(field, 0)))
total += pts; breakdown.append(f"{label}: {pts} ({reason})")
if any("EXCLUDED" in b for b in breakdown): return -1, "DQ", breakdown
tier = "Tier 1" if total>=65 else "Tier 2" if total>=50 else "Tier 3" if total>=35 else "DQ"
return total, tier, breakdown
# load → clean(is_bogus) → score → dedup by name(keep highest) → sort desc → export CSV with Score/Tier/Breakdown
npx claudepluginhub cold-iq/coldiq-marketplace-skills --plugin coldiqGuides creation, editing, and verification of skills for AI coding agents using test-driven development with subagent scenarios. Use when authoring or debugging skills.