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 robisson/build-like-amazon-agent-skills --skill metrics-reviewgit clone --depth 1 https://github.com/robisson/build-like-amazon-agent-skillsWrote 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/robisson/build-like-amazon-agent-skills/metrics-review)<a href="https://agentmods.dev/skills/robisson/build-like-amazon-agent-skills/metrics-review"><img src="https://agentmods.dev/badge/skills/robisson/build-like-amazon-agent-skills/metrics-review/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/robisson/build-like-amazon-agent-skills/metrics-review"><img src="https://agentmods.dev/badge/skills/robisson/build-like-amazon-agent-skills/metrics-review.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 | $0.00021 | $0.01926 |
| Opus 5 | $0.00010 | $0.00963 |
| Sonnet 5 | $0.00004 | $0.00385 |
| Haiku 4.5 | $0.00002 | $0.00193 |
Grade A, and why
Metrics Review 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 11d 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 — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Metrics Review
Overview
Metrics review is the practice of regularly examining operational and business metrics to identify trends, detect degradation early, validate improvements, and drive data-informed decisions. Unlike incident response (reactive), metrics review is proactive—you're looking for problems before they become incidents and confirming that improvements are having the desired effect. Good metrics review distinguishes signal from noise and leads to concrete actions.
When to Use
- Weekly or monthly as a recurring service health review
- After deploying a significant change (did it improve what we expected?)
- When planning capacity or architecture changes
- When evaluating the effectiveness of past improvements
- When investigating customer experience trends
- When preparing for a launch or traffic event
Amazon Context
Amazon is deeply metrics-driven. The phrase "in God we trust, all others bring data" captures the culture. Anecdotes and opinions don't drive decisions—metrics do. But metrics can also mislead if you look at the wrong ones, use the wrong aggregation, or ignore confounding factors. A skilled metrics review requires understanding what the metric actually measures, what can influence it, and what actions the metric should trigger. Amazon engineers are expected to know their service's metrics cold and explain any movement.
The Process
Metric Categories
Customer Experience Metrics (primary — these matter most)
- Availability: percentage of requests served successfully
- Latency: p50, p90, p99, p99.9 — each tells a different story
- Error rate: 5xx (your fault), 4xx anomalies (possibly your fault)
- Throughput: requests per second (traffic health)
- Business success rate: conversions, completions, deliveries
Operational Health Metrics
- MTTD: Mean Time to Detect problems
- MTTR: Mean Time to Recover from problems
- Deployment frequency: How often you deploy (higher = smaller, safer changes)
- Rollback rate: What percentage of deploys rollback (lower = better pipeline)
- Change failure rate: Deploys that cause incidents
- On-call page count: Toil indicator
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.
- 11d ago First seen · 163 lines · 21 tokens per session scan A aa9ada4cb677
Metrics Review is a skill published in the GitHub repository robisson/build-like-amazon-agent-skills (15 stars, last pushed 3mo ago), licensed MIT. It adds 21 tokens to every session and 1,926 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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