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 buying-groupgit 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/buying-group)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/buying-group"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/buying-group/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/buying-group"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/buying-group.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 | $0.00072 | $0.01364 |
| Opus 5 | $0.00036 | $0.00682 |
| Sonnet 5 | $0.00014 | $0.00273 |
| Haiku 4.5 | $0.00007 | $0.00136 |
Grade A, and why
buying-group 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 — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Map the full buying group at a target account. Classify every contact by role, verify each one live via Lusha, audit coverage gaps by function, and tie next actions to specific people and gaps — shaped by the deal stage the user provides.
Input
The user will provide via $ARGUMENTS:
- Company (required) — name, domain, or Lusha company ID.
- Deal stage or research context (required) — where you are in
the deal and what decision this map supports. Examples:
- "Stage 2 discovery — mapping the full buying group for the first time"
- "Stage 3 — procurement and IT now involved, auditing coverage"
- "Pre-close — validating every key role before sending contract"
- "CS renewal — mapping who's actually in the account today"
If deal stage is missing, ask once. If declined, default to general buying group mapping and state that assumption at the top of the output.
Workflow
-
Anchor on deal stage. Read the deal stage and research context from $ARGUMENTS. Restate it in one sentence as the map purpose. Derive the functions most relevant to this stage. Examples:
- Early discovery → Champion, Economic Buyer, key Influencers
- Mid-stage → add Technical Evaluator, Procurement, IT
- Pre-close → full audit across all functions, Blocker check
- CS renewal → current contacts only, flag any departures
-
Resolve the account via Lusha.
- If the user supplied a Lusha company ID, use it directly.
- Otherwise call companies_search with the company name or domain. Confirm the match before continuing. If ambiguous, surface the top two options and ask the user to confirm.
-
Pull contacts in parallel by function. Search for VP+ and Director+ contacts across every function relevant to the deal stage. Run searches concurrently:
- Sales / Revenue
- Finance / Procurement
- IT / Engineering / Security
- Operations
- Marketing (if relevant to the deal)
- C-suite (CEO, CFO, CTO, COO — for deals above $50K ACV)
For each contact found: verify email and direct dial via Lusha. Return title, seniority, department, tenure in current role, and LinkedIn URL if available.
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 · 169 lines · 72 tokens per session scan A e50bcd79ec92
buying-group is a skill published in the GitHub repository swan-gtm/gtm-skills (150 stars, last pushed 2d ago), licensed MIT. It adds 72 tokens to every session and 1,364 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 2026-09-03.
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