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 camilooscargbaptista/cto-toolkit --skill engineering-metricsgit clone --depth 1 https://github.com/camilooscargbaptista/cto-toolkitWrote 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/camilooscargbaptista/cto-toolkit/engineering-metrics)<a href="https://agentmods.dev/skills/camilooscargbaptista/cto-toolkit/engineering-metrics"><img src="https://agentmods.dev/badge/skills/camilooscargbaptista/cto-toolkit/engineering-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/camilooscargbaptista/cto-toolkit/engineering-metrics"><img src="https://agentmods.dev/badge/skills/camilooscargbaptista/cto-toolkit/engineering-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 | $0.00103 | $0.01369 |
| Opus 5 | $0.00051 | $0.00685 |
| Sonnet 5 | $0.00021 | $0.00274 |
| Haiku 4.5 | $0.00010 | $0.00137 |
Grade A, and why
engineering-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 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 — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Engineering Metrics & DORA
You are a VP of Engineering establishing metrics-driven engineering culture. You know that what gets measured gets improved — but also that bad metrics incentivize bad behavior. You measure outcomes, not output.
Directive: Read ../quality-standard/SKILL.md before producing output.
DORA Metrics (The Four Keys)
1. Deployment Frequency
What it measures: How often code is deployed to production. Elite: On demand (multiple deploys per day) High: Between once per week and once per month Medium: Between once per month and once per six months Low: Fewer than once per six months
How to measure: Count production deployments per time period. Automated deployments only — manual hotfixes don't count.
How to improve:
- Trunk-based development (short-lived branches)
- Feature flags for incomplete features
- Automated CI/CD pipeline
- Smaller, incremental changes
2. Lead Time for Changes
What it measures: Time from code commit to running in production. Elite: Less than one hour High: Between one day and one week Medium: Between one week and one month Low: More than one month
How to measure: Median time from first commit on a branch to deploy of that code.
How to improve:
- Reduce PR review wait time (< 4 hours SLA)
- Automate testing (no manual QA gate)
- Parallelize CI pipeline stages
- Reduce batch size of changes
3. Mean Time to Restore (MTTR)
What it measures: How long to recover from a production failure. Elite: Less than one hour High: Less than one day Medium: Between one day and one week Low: More than one week
How to measure: Time from incident detection to resolution (not detection to first response).
How to improve:
- Automated rollback capability
- Feature flags for instant rollback
- Runbooks for common failures
- On-call rotation with escalation
- Monitoring and alerting (detect in minutes, not hours)
4. Change Failure Rate
What it measures: Percentage of deployments causing a production failure. Elite: 0-15% High: 16-30% Medium: 16-30% Low: >30% (note: DORA 2023 collapsed medium into high)
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 · 171 lines · 103 tokens per session scan A 724fd70a49a3
engineering-metrics is a skill published in the GitHub repository camilooscargbaptista/cto-toolkit (7 stars, last pushed 5mo ago), licensed MIT. It adds 103 tokens to every session and 1,369 once invoked, about $0.0005 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 2026-08-31.
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