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dora-report

Skill2 starsChanged 11 days agoMITView on GitHub

Generate monthly DORA and agentic metrics reports. Use this skill when the user asks for a DORA report, monthly metrics, or a monthly audit. Not for security-code-auditor, readme-best-practices.

1 other project uses a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

5 rules. About 0 are common, 2 are distinctive, and 3 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈2

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 3

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Create or append to agents-docs/dora-reports/YYYY-MM.md.

    line 36
  2. Distinctive:

    Use standardized tables and charts (Mermaid where appropriate).

    line 37
  3. Very specific:

    Run the automation script: python3 .agents/skills/dora-report/scripts/generate_report.py

    line 45
  4. Very specific:

    Verify the output in agents-docs/dora-reports/YYYY-MM.md: every value must trace to git history, gh, or metrics logs — never placeholders.

    line 46
  5. Distinctive:

    Add any qualitative analysis or TRIZ-based innovation opportunities to the generated file.

    line 49

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

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

  1. Calculate DORA Metrics:
    • Deployment Frequency: How often code is successfully released to production (or merged to main in 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.
  2. 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.
  3. 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.
  4. Identify Bottlenecks:
    • Based on metrics, suggest one "Innovation Opportunity" using TRIZ principles to improve a lagging metric.

Instructions

  1. Run the automation script: python3 .agents/skills/dora-report/scripts/generate_report.py
  2. 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.
  3. Add any qualitative analysis or TRIZ-based innovation opportunities to the generated file.

See Also

  • learn — Extract learnings into AGENTS.md
  • readme-best-practices — README and documentation best practices

Rationalizations

RationalizationReality
"Metrics are for managers, not agents"Metrics provide the feedback loop for agentic self-improvement and workflow optimization.
"It's too hard to track lead time manually"Agents can analyze git history and PR timestamps to automate this calculation.
"A monthly report is too frequent for a small repo"Regular snapshots prevent technical debt from accumulating unnoticed.

Red Flags

  • Reporting "zero" for failures without verifying revert history or hotfix commits.
  • Ignoring "agentic overhead" (e.g., extremely high token usage for simple tasks).
  • Metrics that lack a time-bound context (e.g., "total tasks" instead of "tasks per week").

References

  • agents-docs/WORKFLOW.md - Standard delivery process to measure against.
  • scripts/analyze-codebase.sh - Source for some raw performance data.

Voice & Context

  • Default: professional + blog
  • Reference: voice-profiles skill for definitions and auto-detection.

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