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/report-advocacy)<a href="https://agentmods.dev/commands/gtmagents/gtm-agents/report-advocacy"><img src="https://agentmods.dev/badge/commands/gtmagents/gtm-agents/report-advocacy/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/report-advocacy"><img src="https://agentmods.dev/badge/commands/gtmagents/gtm-agents/report-advocacy.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 | $0.00020 | $0.00352 |
| Opus 5 | $0.00010 | $0.00176 |
| Sonnet 5 | $0.00004 | $0.00070 |
| Haiku 4.5 | $0.00002 | $0.00035 |
Grade A, and why
report-advocacy 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.
What it actually says
Command: report-advocacy
Inputs
- window – reporting range (30d, quarter, custom dates).
- detail – summary | full.
- audience – exec | sales | product | marketing for tailored narrative.
- dimensions – optional breakdown (region, product, persona, program type).
- include_pipeline – boolean to attach influenced pipeline metrics.
Workflow
- Data Refresh – pull CRM reference usage, content downloads, NPS trends, and advocacy program statuses.
- Coverage Analysis – inspect reference coverage by persona/region/stage and identify gaps.
- Content Pipeline Review – summarize case studies, videos, webinars in production with stage gates.
- Impact Attribution – quantify influence on pipeline/revenue, deal velocity, and retention.
- Action Recommendations – propose new candidates, content refreshes, or enablement updates.
Outputs
- Advocacy dashboard snapshot with KPIs and coverage heatmap.
- Executive-ready summary deck or memo with highlights/risks.
- Follow-up task list (owner, due date, priority) for next cycle.
Agent/Skill Invocations
story-producer– updates content pipeline status.reference-manager– supplies utilization + fatigue insights.reference-opsskill – ensures data quality and logging.storytellingskill – formats narrative for chosen audience.
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 · 35 lines · 20 tokens per session scan A b6012da1391a
report-advocacy is a command published in the GitHub repository gtmagents/gtm-agents (399 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 20 tokens to every session and 352 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.
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specify
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implement
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analyze
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