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 multi-region-gtm-playbookgit 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/multi-region-gtm-playbook)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/multi-region-gtm-playbook"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/multi-region-gtm-playbook/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/multi-region-gtm-playbook"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/multi-region-gtm-playbook.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 | $0.00066 | $0.00592 |
| Opus 5.5 | $0.00026 | $0.00237 |
| Sonnet 5.5 | $0.00013 | $0.00118 |
| Haiku 4.5 | $0.00007 | $0.00059 |
Grade A, and why
multi-region-gtm-playbook 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 6d 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 — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Design a go to market model across regions that keeps one strategy while letting each market win on its own terms.
Inputs to load
- The list of regions with team size, revenue contribution and pipeline contribution as shares of the total.
- Market maturity per region: brand awareness, customer base, competitive intensity.
- Sales cycle, deal size and buying behavior per region.
- Language, regulation and channel differences that change how buyers research and buy.
Define the global core
Keep global only what must be identical: positioning, ideal customer profile, core offer, naming of products, measurement definitions and reporting format. Everything else is a candidate for local adaptation.
Define the local layer
Each region adapts proof (local customers, local references), language and tone, channel mix, pricing presentation, events and compliance. Translation alone is not localization. Check that examples, objections and competitors are regional.
Tier the regions
- Build: low awareness, small base. Invest in presence and proof, expect slow payback.
- Grow: proven demand, rising pipeline. Scale what already works.
- Harvest: strong base and brand. Protect efficiency, expand through existing accounts.
Allocate budget and effort by tier and expected payback, not by headcount or by the size of last year's budget.
Set governance
Agree a fixed cadence: one shared dashboard with common definitions, a regional review, and a rule for when a region may deviate from the global core. Deviations are tested in one region first, then scaled if the result holds.
What good looks like
- The expert's tell: the places where a local team ignored the global plan and outperformed it. These deviations are the source of the next global improvement.
- The common mistake: splitting budget equally across regions, or translating global content and calling it localized.
- A good output states what is global and what is local in one table, assigns each region a tier with a budget logic, and defines who decides on deviations.
- One global average hides the regions that matter. Results are always shown by region next to the total.
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.
- 6d ago First seen · 51 lines · 66 tokens per session scan A 5338092e3a8c
multi-region-gtm-playbook is a skill published in the GitHub repository swan-gtm/gtm-skills (220 stars, last pushed 7d ago), licensed MIT. It adds 66 tokens to every session and 592 once invoked, about $0.0003 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 2026-10-21.
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