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 customer-expansion-scoringgit 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/customer-expansion-scoring)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/customer-expansion-scoring"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/customer-expansion-scoring/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/customer-expansion-scoring"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/customer-expansion-scoring.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00075 | $0.01448 |
| Opus 5 | $0.00037 | $0.00724 |
| Sonnet 5 | $0.00015 | $0.00290 |
| Haiku 4.5 | $0.00007 | $0.00145 |
Grade A, and why
customer-expansion-scoring 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 13d 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 — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Template placeholders
Replace every {{...}} before enabling. See the setup checklist reference for the full setup list.
{{PRODUCT}}— Your product name{{PARENT_SCORING_SKILL}}— The parent lead-scoring skill this returns to (runs ACV assessment before this){{CRM}}— Your CRM (e.g. HubSpot){{DEPTH_SIGNALS}}— Your product's REAL usage-depth indicators (see Signal Stack #2 — must be replaced){{AUTO_CREATED_ARTIFACTS}}— Objects your onboarding auto-creates for every account (excluded from depth){{FOUNDER_CONTENT}}— Whose LinkedIn content signals post-conversion advocacy (usually a founder){{TIER_SCALE}}— Tiers, low→high (default: Bronze / Silver / Gold / Diamond){{INACTIVITY_CAP}}— Inactivity window that caps the tier (default: 30 days → cap at Silver)
Use when the account is a paying customer (Closed Won stage).
Goal: How deeply are they using {{PRODUCT}}, and how much bigger could this account get?
The parent skill ({{PARENT_SCORING_SKILL}}) has already run the ACV assessment and applied the ACV tag. Reference those results here — do not re-research ACV dimensions covered there.
Signal Stack (highest to lowest weight)
Gather all available signals from account memory, product event history, and {{CRM}}.
-
Meeting — a completed call signals active relationship depth. Extract: topics discussed (expansion, new use cases, additional team members mentioned), questions and objections, action items, attendee roles. A meeting that surfaces new departments, budget conversations, or advanced use cases = strong expansion signal.
-
Product depth — the real indicators of depth for {{PRODUCT}} are {{DEPTH_SIGNALS}}.
Reference implementation (an AI GTM platform) used:
- Integrations/tools connected beyond defaults — each = deliberate setup effort
- Automations/triggers created beyond the default one — variety and count signal how much the user is actually building on the product
- Usage credits consumed (volume = active usage)
- Multiple team members active
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 13d ago First seen · 152 lines · 75 tokens per session scan A 36c9428f1ea7
customer-expansion-scoring is a skill published in the GitHub repository swan-gtm/gtm-skills (150 stars, last pushed 2d ago), licensed MIT. It adds 75 tokens to every session and 1,448 once invoked, about $0.0004 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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