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/measure-engagement)<a href="https://agentmods.dev/commands/gtmagents/gtm-agents/measure-engagement"><img src="https://agentmods.dev/badge/commands/gtmagents/gtm-agents/measure-engagement/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/measure-engagement"><img src="https://agentmods.dev/badge/commands/gtmagents/gtm-agents/measure-engagement.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 | $0.00018 | $0.00700 |
| Opus 5 | $0.00009 | $0.00350 |
| Sonnet 5 | $0.00004 | $0.00140 |
| Haiku 4.5 | $0.00002 | $0.00070 |
Grade A, and why
measure-engagement 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.
How it starts
The opening of the file, as written. The whole thing — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Command: measure-engagement
Inputs
- window – time horizon (7d, 30d, 90d) for analysis.
- detail – summary | full report depth.
- dimensions – optional breakdown (channel, persona, program, cohort).
- experiments – optional list of programs to analyze.
- alert_threshold – optional metric threshold for escalations.
GTM Agents Pattern & Plan Checklist
Mirrors GTM Agents orchestrator blueprint @puerto/plugins/orchestrator/README.md#112-325.
- Pattern selection: Engagement measurement typically runs pipeline (data → diagnostics → sentiment → experiment readouts → actions). If diagnostics + experiment analysis can run in parallel, log a diamond segment with merge gate in the plan header.
- Plan schema: Save
.claude/plans/plan-<timestamp>.jsoncapturing window, data feeds, task IDs, dependency graph (analytics, CRM, sentiment tools), error handling, and success metrics (engagement %, advocacy, risk volume). - Tool hooks: Reference
docs/gtm-essentials.mdstack—Serena for schema diffs, Context7 for platform documentation/conversation exports, Sequential Thinking for insights review cadence, Playwright for verifying dashboard/report embeds. - Guardrails: Default retry limit = 2 for failed data pulls or sentiment processing; escalation ladder = Community Analyst → Community Lead → CS/Product leadership.
- Review: Run
docs/usage-guide.md#orchestration-best-practices-puerto-paritybefore execution to confirm inputs, dependencies, deliverables.
Workflow
- Data Pull – aggregate platform analytics, CRM attribution, sentiment scores, and support signals.
- Health Diagnostics – compute growth, activation, engagement, retention, and advocacy metrics by dimension.
- Sentiment Review – scan community conversations, surveys, and NPS for emerging themes.
- Experiment Readouts – evaluate running pilots against guardrails + KPIs.
- Action Recommendations – produce prioritized playbook (content tweaks, ambassador outreach, escalations).
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 · 49 lines · 18 tokens per session scan A 06c827a836d4
measure-engagement is a command published in the GitHub repository gtmagents/gtm-agents (398 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 18 tokens to every session and 700 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
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
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.