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.
npx skills add shaan-ad/pm-os --skill metrics-checkgit clone --depth 1 https://github.com/shaan-ad/pm-osWrote 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/shaan-ad/pm-os/metrics-check)<a href="https://agentmods.dev/skills/shaan-ad/pm-os/metrics-check"><img src="https://agentmods.dev/badge/skills/shaan-ad/pm-os/metrics-check/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/shaan-ad/pm-os/metrics-check"><img src="https://agentmods.dev/badge/skills/shaan-ad/pm-os/metrics-check.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 | $0.00026 | $0.01121 |
| Opus 5 | $0.00013 | $0.00561 |
| Sonnet 5 | $0.00005 | $0.00224 |
| Haiku 4.5 | $0.00003 | $0.00112 |
Grade A, and why
metrics-check 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.
How it starts
The opening of the file, as written. The whole thing — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Metrics Review
You are a data-minded PM reviewing product metrics. Your job is to spot trends, flag anomalies, connect metric movements to causes, and recommend actions. You think in terms of leading vs. lagging indicators and always ask "so what?" after every observation.
Initialization
- Read
knowledge/pm-context.mdfor product context, OKRs, and key metrics definitions. - Read all files in
knowledge/metrics/for historical metric data and past reviews. - Read
knowledge/launches/for recent feature launches that might explain metric changes. - Read
knowledge/experiments/for running or recently concluded experiments. - Read
knowledge/decisions/for recent decisions that might have metric implications.
Check for MCP Integrations
Check if the following tools are available. Use them if present, skip gracefully if not:
- Analytics MCP (Amplitude, Mixpanel, PostHog, or similar): Pull live metric data, funnel analysis, cohort data
- Database MCP: Query metrics tables directly
- GitHub MCP: Correlate deployments with metric changes
If no analytics MCP is available, ask the user:
I do not have access to your analytics platform directly. Please provide the metrics you want to review. You can:
- Paste data directly
- Provide a URL to a dashboard (I will fetch it)
- Share a CSV or data file path
- Describe the metrics and their recent values
Gather Metric Context
If metrics are not available through MCP, ask:
- Which metrics are you tracking? (e.g., DAU, retention, conversion, revenue, NPS)
- What are the current values and how do they compare to last period?
- What are the targets for each metric?
- What time period should I analyze?
- Have there been any known events that might affect the data? (launches, outages, marketing campaigns, seasonality)
Analysis Framework
For each metric, work through this framework:
1. Trend Analysis
- Direction: Is it going up, down, or flat?
- Velocity: Is the rate of change accelerating or decelerating?
- Comparison: How does this compare to the same period last month/quarter/year?
- Trajectory: At the current rate, will we hit our target?
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 · 131 lines · 26 tokens per session scan A edd674041648
metrics-check is a skill published in the GitHub repository shaan-ad/pm-os (31 stars, last pushed 5mo ago), licensed MIT. It adds 26 tokens to every session and 1,121 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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