Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/matteotitta/genesys-skillsnpx agentmods add skills/matteotitta/genesys-skills/product-pulseWrote 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/matteotitta/genesys-skills/product-pulse)<a href="https://agentmods.dev/skills/matteotitta/genesys-skills/product-pulse"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/product-pulse/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/matteotitta/genesys-skills/product-pulse"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/product-pulse.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00148 | $0.01535 |
| Opus 5 | $0.00074 | $0.00767 |
| Sonnet 5 | $0.00030 | $0.00307 |
| Haiku 4.5 | $0.00015 | $0.00153 |
Grade A, and why
product-pulse 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.
How it starts
The opening of the file, as written. The whole thing — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product pulse — single-page metrics report
Produce a daily or weekly pulse report that measures the locked product strategy's metrics. Adapted from /ce-product-pulse in EveryInc/compound-engineering-plugin v3.5.0 (MIT).
The pulse closes the strategy↔pulse↔ship loop: strategy declares metrics, pulse measures them, learnings flow back to strategy refreshes.
When to run
Invoke when the user says:
- "Run product pulse for [product]"
- "Daily pulse"
- "Weekly pulse"
- "How's [product] doing?"
- Cron-scheduled (default: 8am daily — via the
/scheduleskill or Trigger.dev cron)
Do NOT invoke when:
- User wants a marketing dashboard →
/dashboard - User wants a deep analytics investigation →
data:analyze - User wants a competitive analysis →
/competitor-research - No locked
strategy-docexists for the product → run/strategy-docfirst; pulse is meaningless without locked metrics
Inputs
Required:
- Locked
strategy-doc(defines the metrics this pulse measures) - Product name / ship slug
Optional but valuable:
- MCP connections: PostHog / Mixpanel / Amplitude (usage), Datadog / Sentry / Logfire / Honeycomb (system perf), Stripe / Paddle (revenue), GSC (search), custom DB read replicas
- Latest user conversation / interview note (for the K3 qualitative pairing)
- Previous pulse for delta comparison
If MCP connection missing for a metric: flag the data gap explicitly; don't fabricate a number. Per .claude/rules/financial-data.md — never invent metrics.
Steps
- Phase 1 — Load locked strategy. Read
strategy-doc(the upstream dependency). Extract the metrics section. These are what we measure. - Phase 2 — Pull metrics. Query each MCP for the named metrics. Compare to previous period (default 7 days). Flag anomalies (>20% delta or threshold cross).
- Phase 3 — Pull system signals. Query infra MCPs (Datadog / Sentry / etc.) for errors, latency, regressions.
- Phase 4 — Pull qualitative. Find at least one user conversation, support ticket, or interview note from the period. If none: flag as a data gap and suggest a user call.
- Phase 5 — Compose pulse. Apply 4-section structure. Apply 30-40 line discipline. Cut anything that doesn't move strategy thinking.
- Phase 6 — Self-roast. Run checks below.
- Phase 7 — Push. GDoc / Notion + Slack notification (if cron-scheduled).
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 · 141 lines · 148 tokens per session scan A f3d90c2ae9b8
product-pulse is a skill published in the GitHub repository matteotitta/genesys-skills (36 stars, last pushed 1mo ago), licensed MIT. It adds 148 tokens to every session and 1,535 once invoked, about $0.0007 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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