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 ai-gtm-transformation-auditgit 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/ai-gtm-transformation-audit)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/ai-gtm-transformation-audit"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/ai-gtm-transformation-audit/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/ai-gtm-transformation-audit"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/ai-gtm-transformation-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00174 | $0.04543 |
| Opus 5.5 | $0.00070 | $0.01817 |
| Sonnet 5.5 | $0.00035 | $0.00909 |
| Haiku 4.5 | $0.00017 | $0.00454 |
Grade A, and why
ai-gtm-transformation-audit 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 yesterday.
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 — 238 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Applies when a leader asks "where are we on AI in go-to-market, and what is stopping us from getting more out of it?" Produces a level placement, a picture of where each part of the org sits against what good looks like, and a 30-day plan.
Most teams overrate their level. They have connected AI to their CRM and call it transformation, yet the numbers have not moved. The audit separates three things leaders tend to blur together: using AI, connecting AI to your data, and having AI do the work in a way the whole team can build on.
The point of the audit is to show the leader what good looks like, concretely, next to where they are today. Every placement, gap and move in the readout carries an example. There are no scores: a number invites false precision, and two leaders with the same number can be in completely different places. Evidence and examples do not have that problem. It is a diagnostic, not a pitch: no vendor recommendations unless the leader asks for them.
The four levels
Classify against these definitions exactly, and use these names, in this order, everywhere: in the interview, the placement and the readout.
| # | Level | Definition |
|---|---|---|
| 01 | Mostly manual | Research, list building and outreach are done by hand or in traditional tools. |
| 02 | AI as an answer engine | AI is connected to some systems and answers questions when someone asks. |
| 03 | AI as an action engine | Agents run defined workflows and skills when something triggers them. |
| 04 | Shared AI layer | Several teams build and run agents on one foundation with permissions, monitoring and shared context. |
One motion at each level
The same inbound demo request, handled at each level. Use this example, or one like it from the leader's own motion, whenever a level needs explaining.
- 01 Mostly manual. The form fill lands in the CRM overnight. Next morning an SDR googles the company, checks LinkedIn, picks a template and sends it. Four hours of the day go to this kind of prep. Two reps paste account notes into ChatGPT to help them write; the rest do not.
- 02 AI as an answer engine. Claude is connected to the CRM and the call recorder. The SDR asks "what do we know about this account?" and gets a good brief in seconds. Then they still decide whether it qualifies, route it, write the email and log the activity themselves. Prep is faster; the number of leads worked per day barely moves.
- 03 AI as an action engine. The form fill triggers an agent: it researches the company and the person, checks ICP fit, drafts a first touch that cites one real account fact, and posts it to the owning rep for approval within ten minutes. It works. But the GTM engineer built it alone, scoring still lives in the marketing automation tool and routing in the CRM, and when marketing wants the same research for event follow-up, they file a ticket and wait.
- 04 Shared AI layer. The inbound agent, the event follow-up agent and the renewal-prep agent all read the same account context: ICP definition, approved messaging, history, open promises. Last week the demand gen lead changed the qualification rule herself, inside the permissions RevOps set, and the monitoring view showed the approval rate the next day. CS reused the same research step for expansion signals without rebuilding it.
What ships with it
3 files 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.
- yesterday First seen · 238 lines · 174 tokens per session scan A 777c6eeb7629
ai-gtm-transformation-audit is a skill published in the GitHub repository swan-gtm/gtm-skills (181 stars, last pushed yesterday), licensed MIT. It adds 174 tokens to every session and 4,543 once invoked, about $0.0007 per session on Opus 5.5. 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 2026-10-16.
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