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 caiaffa/claude-code-ultimate-engineering-system --skill dora-metricsgit clone --depth 1 https://github.com/caiaffa/claude-code-ultimate-engineering-systemWrote 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/caiaffa/claude-code-ultimate-engineering-system/dora-metrics)<a href="https://agentmods.dev/skills/caiaffa/claude-code-ultimate-engineering-system/dora-metrics"><img src="https://agentmods.dev/badge/skills/caiaffa/claude-code-ultimate-engineering-system/dora-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/caiaffa/claude-code-ultimate-engineering-system/dora-metrics"><img src="https://agentmods.dev/badge/skills/caiaffa/claude-code-ultimate-engineering-system/dora-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 | $0.00047 | $0.00510 |
| Opus 5.5 | $0.00019 | $0.00204 |
| Sonnet 5.5 | $0.00009 | $0.00102 |
| Haiku 4.5 | $0.00005 | $0.00051 |
Grade A, and why
dora-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 1mo 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
Mission
Make delivery performance visible and improvable. A system that ships and responds to incidents but measures nothing cannot tell if it is getting better.
When to use
- Periodic engineering-health review.
- After a quarter of releases/incidents, to find the bottleneck.
- When deciding where to invest in process or tooling.
The four metrics
1. Deployment frequency
How often code reaches production. Higher is healthier — it means small, low-risk batches. Signal of trouble: infrequent, large releases.
2. Lead time for changes
Time from commit to running in production. Shorter means a tighter feedback loop. Measure from first commit on a change to its production deploy.
3. Change failure rate
Percentage of deploys that cause a degradation requiring remediation (rollback, hotfix, incident). Elite is low single digits. Rising rate means review/test gates are too weak.
4. Time to restore (MTTR)
Time from incident start to user impact ended. Pairs with incident-response. Driven by detection speed, rollback ease, and runbook quality.
How to read them together
- High failure rate + slow restore → invest in gates and rollback safety.
- Low deploy frequency + long lead time → batches too big; invest in CI and smaller changes.
- Good speed metrics + high failure rate → moving fast and breaking things; tighten review. The goal is balance: speed AND stability move together in healthy teams.
Method
- Pull raw data: deploy log,
git logfor lead time, incident records for failures and MTTR. - Compute each metric for the period.
- Compare to the previous period — direction matters more than absolute value.
- Name the single biggest bottleneck and one concrete action to move it.
Output
# DORA REVIEW — [period]
Deployment frequency: [value] (trend)
Lead time: [value] (trend)
Change failure rate: [value] (trend)
Time to restore: [value] (trend)
Bottleneck: [the one metric to improve next]
Recommended action: [one concrete change]
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.
- 1mo ago First seen · 55 lines · 47 tokens per session scan A 903737d2d868
dora-metrics is a skill published in the GitHub repository caiaffa/claude-code-ultimate-engineering-system (17 stars, last pushed 1mo ago), licensed MIT. It adds 47 tokens to every session and 510 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-15.
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