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 d-o-hub/github-template-ai-agents --skill dora-reportgit clone --depth 1 https://github.com/d-o-hub/github-template-ai-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/d-o-hub/github-template-ai-agents/dora-report)<a href="https://agentmods.dev/skills/d-o-hub/github-template-ai-agents/dora-report"><img src="https://agentmods.dev/badge/skills/d-o-hub/github-template-ai-agents/dora-report/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/d-o-hub/github-template-ai-agents/dora-report"><img src="https://agentmods.dev/badge/skills/d-o-hub/github-template-ai-agents/dora-report.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 | $0.00050 | $0.00801 |
| Opus 5.5 | $0.00020 | $0.00320 |
| Sonnet 5.5 | $0.00010 | $0.00160 |
| Haiku 4.5 | $0.00005 | $0.00080 |
Grade A, and why
dora-report 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 25d 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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DORA Report
Generate and maintain DORA (DevOps Research and Assessment) metrics and agentic performance metrics to track project velocity, stability, and agent efficiency.
When to Use
Activate when:
- Monthly reporting is required (e.g., at the end of a calendar month).
- The user requests a performance audit or "DORA report".
- Analyzing the impact of new tools or workflows on delivery speed and quality.
Instructions
-
Calculate DORA Metrics:
- Deployment Frequency: How often code is successfully released to production (or merged to
mainin this template context). - Lead Time for Changes: The amount of time it takes a commit to get into production.
- Change Failure Rate: The percentage of deployments causing a failure in production (e.g., requiring a hotfix or revert).
- Time to Restore Service: How long it takes to recover from a failure in production.
- Deployment Frequency: How often code is successfully released to production (or merged to
-
Calculate Agentic Metrics:
- Tasks Completed: Total number of GOAP goals or atomic tasks finalized.
- Skill Invocations: Frequency and distribution of skill usage.
- Token Usage Trends: Efficiency of context usage over time.
- Self-Fix Success Rate: Ratio of auto-fixed CI failures vs. those requiring human intervention.
-
Generate Report:
- Create or append to
agents-docs/dora-reports/YYYY-MM.md. - Use standardized tables and charts (Mermaid where appropriate).
- Compare current metrics against the previous month's baseline.
- Create or append to
-
Identify Bottlenecks:
- Based on metrics, suggest one "Innovation Opportunity" using TRIZ principles to improve a lagging metric.
Instructions
- Run the automation script:
python3 .agents/skills/dora-report/scripts/generate_report.py - Verify the output in
agents-docs/dora-reports/YYYY-MM.md: every value must trace to git history,gh, or metrics logs — never placeholders. Hand-written Analyst Notes are preserved across regenerations. - Add any qualitative analysis or TRIZ-based innovation opportunities to the generated file.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 25d ago Changed · +2 lines · +6 tokens per session 32fe32dfecb4
- 1mo ago First seen · 77 lines · 44 tokens per session scan A 8be70c748b14
dora-report is a skill published in the GitHub repository d-o-hub/github-template-ai-agents (2 stars, last pushed yesterday), licensed MIT. It adds 50 tokens to every session and 801 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 2026-08-31.
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