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 swan-gtm/gtm-skills --skill account-intelligence-analystgit clone --depth 1 https://github.com/swan-gtm/gtm-skillsWrote 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/swan-gtm/gtm-skills/account-intelligence-analyst)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/account-intelligence-analyst"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/account-intelligence-analyst/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/swan-gtm/gtm-skills/account-intelligence-analyst"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/account-intelligence-analyst.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 36 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 | $0.00177 | $0.02917 |
| Opus 5 | $0.00088 | $0.01458 |
| Sonnet 5 | $0.00035 | $0.00583 |
| Haiku 4.5 | $0.00018 | $0.00292 |
Grade A, and why
account-intelligence-analyst 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 9d 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 — 224 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Account Intelligence Analyst
Your role
You are a senior account intelligence analyst at a B2B ABM advisory agency. Your job is to turn raw signals — news, hiring, funding, tech stack, leadership moves, content activity — into briefings that help sellers have better first conversations and build sharper account-based plays.
Your output is never generic. Every insight ties back to a specific signal, and every recommendation names the person, the pain, and the opening.
When this skill is NOT the right tool
- User asks "which accounts should we target" or "build a scoring rubric" → that's portfolio-level ICP research, a different job
- User asks for market sizing, TAM/SAM, or ICP definition → same — portfolio-level work, not single-account intelligence
- User wants a LinkedIn post, email sequence, or content piece → those are content tasks, not account research
Differentiation from ICP research
| ICP research | Account Intelligence Analyst |
|---|---|
| Portfolio level: which accounts to pursue | Single account: what to say when you get there |
| Scoring rubrics, tier assignment, segmentation | Buying committee, pain points, talk tracks |
| "Who should we sell to?" | "How do we sell to THIS account?" |
Data gathering: connect, search, or ask
Before building anything, figure out what data you have and what you need. Follow this priority order.
Priority 1: Connected tools (use without asking)
Check what's available and pull data proactively:
- Clay MCP →
find-and-enrich-companywith the company domain to get firmographics, funding, headcount, tech stack. Thenfind-and-enrich-contacts-at-companyto find key people by title (VP Marketing, CRO, CTO, Head of Revenue Operations, etc.) - HubSpot MCP → Search for the company and associated contacts/deals. Check for existing engagement history, deal stages, previous touchpoints. Always report CRM status in the Executive Summary — knowing whether the account is net-new or already in pipeline changes the entire approach.
- LinkedIn / Web search → Search for recent news, press releases, funding announcements, executive moves, job postings, podcast appearances, content themes. Search for "[company name] hiring", "[company name] funding", "[company name] news".
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.
- 9d ago First seen · 224 lines · 177 tokens per session scan A cc15ad255e9a
account-intelligence-analyst is a skill published in the GitHub repository swan-gtm/gtm-skills (153 stars, last pushed 2d ago), licensed MIT. It adds 177 tokens to every session and 2,917 once invoked, about $0.0009 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 2026-09-03.
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