/dora-metrics

name: dora-metrics
description: Computes DORA delivery-performance metrics from git and GitHub API. Use when assessing deployment frequency, lead time, or change failure rate.
alwaysApply: false
category: governance
tags:
- dora
- metrics
- delivery
- engineering-management
- github
dependencies: []
tools: []
provides:
  governance:
  - dora-tier
  - delivery-bottleneck
  reporting:
  - dora-report
  - tier-classification
usage_patterns:
- delivery-retro
- agentic-pipeline-audit
- release-health-input
complexity: intermediate
estimated_tokens: 800
progressive_loading: true
modules:
- modules/thresholds.md
- modules/agentic-workflow-signals.md

DORA Metrics

Purpose

Compute the four DORA delivery-performance metrics (Deployment Frequency, Lead Time for Changes, Change Failure Rate, and Time to Restore Service) from local git history and the GitHub API. Classify each metric into Elite, High, Medium, or Low using thresholds from DORA's State of DevOps research, and surface the single weakest dimension as the next improvement target.

When To Use

• Engineering management retrospectives and quarterly reviews.
• Auditing whether agentic workflows (AI-assisted PRs, automated

deploys) improve velocity and stability or quietly regress them.

• Feeding a tier signal into `minister:release-health-gates`.

When Not to Use

• Single-team velocity tracking that needs story-point burndowns

rather than delivery-performance evidence.

• Repositories without a clear production branch or release cadence;

DORA assumes one.

Workflow

1. Run the helper script with the desired window:

   python3 -m minister.dora_metrics --window 30 --branch main

2. Read the output: per-metric value, tier classification, and the bottleneck pointer.

3. For agentic-workflow audits, run the same window twice. Once filtering to AI-authored PRs (e.g., `--failure-label ai-bug`), once across all PRs. Compare the CFR delta. See `modules/agentic-workflow-signals.md`.

4. Optionally pipe `--json` into the tracker so trend data persists alongside `release-health-gates` snapshots.

5. Optionally render trend charts with kuva when reviewing multiple windows or comparing before/after an agentic-workflow change:

   # Collect weekly snapshots into a TSV, then plot all four metrics
   # week<TAB>metric<TAB>value
   kuva line trends.tsv --x week --y value --color-by metric \
       --title "DORA trends (30-day windows)" -o dora-trends.svg

   # Quick terminal preview without writing a file
   kuva line trends.tsv --x week --y value --color-by metric --terminal

kuva reads TSV/CSV from stdin or a file path. Install once: `cargo install kuva --features cli`. No project source changes required. See [kuva](https://github.com/Psy-Fer/kuva) for the full plot-type reference.

Inputs

| Flag | Default | Meaning | |------|---------|---------| | `--window` | 30 | Measurement window in days | | `--branch` | HEAD | Production branch | | `--failure-label` | bug | GitHub label marking prod failures | | `--json` | off | Emit JSON instead of human-readable | | `--repo-path` | cwd | Repository directory |

Outputs

A short text report or JSON payload with:

• Per-metric numeric value (e.g., `4.2/day`, `2.1 hours`, `8%`).
• Per-metric tier (Elite, High, Medium, Low).
• Overall tier (the weakest of the four).
• Bottleneck key, identifying which metric to focus improvement on.

Tier Thresholds

See `modules/thresholds.md` for the complete table. Brief summary:

| Metric | Elite | High | Medium | Low | |--------|-------|------|--------|-----| | DF | >= 1/day | >= 1/week | >= 1/month | < 1/month | | LT | <= 1 day | <= 1 week | <= 1 month | > 1 month | | CFR | <= 15% | <= 30% | <= 45% | > 45% | | TRS | < 1 hour | < 1 day | < 1 week | >= 1 week |

Verification

Confirm a DORA report is real by re-running the script over a narrower window and checking that DF and LT scale predictably. For CFR and TRS, sample two or three of the contributing GitHub issues and verify the `bug` (or chosen) label is correct on each.

Testing

Unit tests live in `plugins/minister/tests/unit/test_dora_metrics.py`. Each tier boundary is exercised at the threshold, so future contributors who adjust an inequality (`>` vs `>=`) trigger a failure rather than a silent regression. Add new tests at the threshold when extending classification logic.

Exit Criteria

• [ ] DORA report generated for the requested window.
• [ ] All four metrics classified into a tier.
• [ ] Bottleneck dimension surfaced.
• [ ] Output is readable in a terminal or as a PR comment.