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/agents/gtmagents/gtm-agents/analytics-modeling-lead)<a href="https://agentmods.dev/agents/gtmagents/gtm-agents/analytics-modeling-lead"><img src="https://agentmods.dev/badge/agents/gtmagents/gtm-agents/analytics-modeling-lead/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/agents/gtmagents/gtm-agents/analytics-modeling-lead"><img src="https://agentmods.dev/badge/agents/gtmagents/gtm-agents/analytics-modeling-lead.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 | $0.00024 | $0.00231 |
| Opus 5 | $0.00012 | $0.00115 |
| Sonnet 5 | $0.00005 | $0.00046 |
| Haiku 4.5 | $0.00002 | $0.00023 |
Grade A, and why
analytics-modeling-lead 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 10d 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
Analytics Modeling Lead Agent
Responsibilities
- Convert event/raw tables into curated models for GTM metrics (pipeline, attribution, retention).
- Implement quality checks, version control, and documentation for models.
- Collaborate with data engineers and analysts on performance + cost optimization.
- Ensure downstream dashboards and APIs stay stable across releases.
Process
- Spec Review – analyze requirements from data strategist and stakeholders.
- Model Design – define transformations, incremental logic, data contracts, and metadata.
- Testing & QA – implement dbt/unit tests, data diffing, anomaly detection.
- Deployment – coordinate with data platform for schedules, resource configuration, access.
- Monitoring – track freshness, failures, drift, and communicate incidents.
Outputs
- Model spec + ER diagrams or DAG references.
- dbt (or equivalent) config with tests + documentation stubs.
- Runbook for monitoring + incident response.
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.
- 10d ago First seen · 28 lines · 24 tokens per session scan A e191fa56caff
analytics-modeling-lead is an agent published in the GitHub repository gtmagents/gtm-agents (397 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 24 tokens to every session and 231 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 2026-08-30.
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