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 tomzx/agents --skill review-metricsgit clone --depth 1 https://github.com/tomzx/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/skills/tomzx/agents/review-metrics)<a href="https://agentmods.dev/skills/tomzx/agents/review-metrics"><img src="https://agentmods.dev/badge/skills/tomzx/agents/review-metrics/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/tomzx/agents/review-metrics"><img src="https://agentmods.dev/badge/skills/tomzx/agents/review-metrics.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 | $0.00031 | $0.00490 |
| Opus 5 | $0.00015 | $0.00245 |
| Sonnet 5 | $0.00006 | $0.00098 |
| Haiku 4.5 | $0.00003 | $0.00049 |
Grade A, and why
review-metrics 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 6d 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
Review Metrics
Reads the actuals against the targets defined in goals.md and analytics-spec.md, and produces a health report. It judges both whether success metrics moved and whether guardrails held — a win that breaks a guardrail is not a win.
Prerequisites
- Apply the shared PDLC conventions in
skills/pdlc/references/shared.md. - A launched initiative with
analytics-spec.mdand instrumentation in place.
Steps
- Read the actuals for every success metric (
SM-N) and guardrail metric (GM-N) for the review window. - Compare to targets/floors. Classify each as
on-track,at-risk,missed(success) orbreached(guardrail). - Surface anomalies: unexpected drops, cohort differences, and changes that coincide with the launch.
- Distinguish signal from noise: is the change statistically meaningful given sample size and window?
- Produce the verdict:
healthy,mixed, orunhealthy. A guardrail breach forces at leastmixed, usuallyunhealthy. - Recommend actions: double-down (scale), iterate (tune), or roll back.
- Write
health-report.mdto the initiative directory.
Output Format
Use the template at skills/pdlc/templates/initiatives/health-report.md.
Outcome
If $OUTCOME_YAML is set:
| Verdict | When |
|---|---|
healthy |
Success metrics on track, no guardrail breach |
mixed |
Some metrics on track, others at-risk, or a guardrail near its floor |
unhealthy |
Success metrics missed or a guardrail breached |
Completion Checklist
- Every
SM-NandGM-Nread for the window - Each classified with a status
- Anomalies and cohort splits noted
- Signal-vs-noise considered (sample/window)
- Verdict and recommended action stated
Next Step
Load synthesize-feedback to combine the quantitative read with qualitative signal.
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.
- 6d ago First seen · 51 lines · 31 tokens per session scan A 850d3ed8f13c
review-metrics is a skill published in the GitHub repository tomzx/agents (6 stars, last pushed yesterday), licensed MIT. It adds 31 tokens to every session and 490 once invoked, about $0.0002 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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