Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Zoominfo/zoominfo-mcp-plugin --skill score-accountsgit clone --depth 1 https://github.com/Zoominfo/zoominfo-mcp-pluginWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/zoominfo/zoominfo-mcp-plugin/score-accounts)<a href="https://agentmods.dev/skills/zoominfo/zoominfo-mcp-plugin/score-accounts"><img src="https://agentmods.dev/badge/skills/zoominfo/zoominfo-mcp-plugin/score-accounts/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/zoominfo/zoominfo-mcp-plugin/score-accounts"><img src="https://agentmods.dev/badge/skills/zoominfo/zoominfo-mcp-plugin/score-accounts.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 75 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00197 | $0.04617 |
| Opus 5 | $0.00098 | $0.02308 |
| Sonnet 5 | $0.00039 | $0.00923 |
| Haiku 4.5 | $0.00020 | $0.00462 |
Grade A, and why
score-accounts scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 341 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Score Accounts
Rank a list of accounts by ICP fit + intent + trigger signals. Calls get_gtm_context(detailed: true) unconditionally, resolves mixed-identifier inputs explicitly surfacing ambiguity, scores each account on four axes, and presents both the ranking and the weight set as iteratively-refinable artifacts.
The bar
- Resolution accuracy 100% — every input auto-resolved / verified / ambiguous / failed. Nothing silently picked.
- Every score explainable — composite is a transparent weighted sum, never an opaque number.
- "Why now" cites a specific signal — not the composite restated.
- Every tier comes with a recommended action.
- Weights and axes are exposed and overridable.
Sellers reject black-box scores. Transparency + per-account "why now" are what make this skill trusted.
Scope
Scores company-level accounts, not contacts. Persona-aware ranking is a chain target via personalize-email after tier-A is produced.
Input
- Accounts (required) — list of ZI IDs / company names / domains / mixed CSV.
- Use case (default
prospecting) —prospecting,abm,territory_planning,pipeline_acceleration. Affects tier thresholds + recommended actions. - Weight overrides (optional) —
{fit, intent, trigger, engagement}summing to 100. - Tier thresholds (optional) —
{A, B}. C is the remainder. - ICP override (optional) — natural-language refinement on top of
get_gtm_context.icp. - Intent topics (optional) — explicit list overriding GTM-derived defaults.
Four-axis framework
| Axis | Question | Source |
|---|---|---|
| Fit | Does this match our ICP? | enrich_companies vs get_gtm_context.icp |
| Intent | Are they actively researching topics we sell into? | enrich_company_signals (intent), matched to GTM priorities |
| Trigger | Fresh event creating a window? | enrich_company_signals (news + scoops), last 90d by signal date |
| Engagement | Already interacting with us? | account_research narrative for known accounts. If absent, weight redistributed. |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 341 lines · 197 tokens per session scan A 7e945329e235
score-accounts is a skill published in the GitHub repository Zoominfo/zoominfo-mcp-plugin (7 stars, last pushed 8d ago), licensed MIT. It adds 197 tokens to every session and 4,617 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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