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.
git clone --depth 1 https://github.com/gtmagents/gtm-agentsWrote 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/commands/gtmagents/gtm-agents/segment-customers)<a href="https://agentmods.dev/commands/gtmagents/gtm-agents/segment-customers"><img src="https://agentmods.dev/badge/commands/gtmagents/gtm-agents/segment-customers/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/commands/gtmagents/gtm-agents/segment-customers"><img src="https://agentmods.dev/badge/commands/gtmagents/gtm-agents/segment-customers.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 | $0.00017 | $0.00361 |
| Opus 5 | $0.00009 | $0.00180 |
| Sonnet 5 | $0.00003 | $0.00072 |
| Haiku 4.5 | $0.00002 | $0.00036 |
Grade A, and why
segment-customers 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 8d 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.
What it actually says
Command: segment-customers
Inputs
- goal – business goal (upsell, retention, adoption, marketing efficiency).
- inputs – comma-separated data sources (usage, firmographic, revenue, support, survey).
- method – rules | clustering | hybrid.
- segments – target number of segments/personas to output.
- activation – optional channel focus (sales, cs, marketing, product).
Workflow
- Data Profiling – evaluate selected inputs, quality, and coverage.
- Segmentation Design – run clustering/heuristics, score stability, and interpretability.
- Scoring Logic – define rules/weights for RevOps/data teams to implement.
- Persona Packaging – craft segment personas with value props, risks, and KPIs.
- Activation Plan – map segments to GTM plays, reporting, and refresh cadence.
Outputs
- Segmentation matrix (definition, criteria, size, key metrics).
- Scoring workbook/API spec with weights and thresholds.
- Activation brief aligning segments to plays/channels.
Agent/Skill Invocations
segmentation-architect– leads modeling + documentation.retention-analyst– supplies usage/retention signals.customer-insights-partner– injects qualitative proof.segmentation-frameworkskill – provides templates + guardrails.activation-mapskill – links segments to GTM workflows.
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.
- 8d ago First seen · 36 lines · 17 tokens per session scan A 9d367dd3c805
segment-customers is a command published in the GitHub repository gtmagents/gtm-agents (398 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 17 tokens to every session and 361 once invoked, about $0.0001 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.
Other commands, from other repositories
checklist
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clarify
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specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.