Connect your JIRA epic and GitHub repo. Get hours saved, AI token costs, PR quality and risk score in one interactive dashboard — in under 30 seconds.
ai-engineering-metrics analyze --mock
One pipx command puts the CLI on your PATH, isolated from your project dependencies. Works on macOS, Linux and Windows.
Drop a .env in your project root with your JIRA credentials. GitHub uses the gh CLI you already have.
Point it at any JIRA epic key. It fetches stories, linked pull requests and AI token usage automatically.
A reports/PROJ-42/ folder lands in your project with a dashboard, JSON and two CSVs ready to share.
analyze run writes a complete bundle to reports/<EPIC>/.Interactive charts & tables. Open in any browser — no server required. • Productivity timeline • Hours saved per story • PR quality scorecards • Risk breakdown
"productivity": { "hours_saved": 9.2, "savings_percent": 40.0 }, "ai_usage": { "total_tokens": 580000, "estimated_cost": 3.48 }, "risk": { "score": 19.5, "level": "low" }
key,status,sp,ai_tokens,saved_h
KAN-9,Done,8,210000,3.2
KAN-6,Done,5,142000,2.0
KAN-5,Done,3,85000,1.2
KAN-7,Done,3,60000,1.2
KAN-10,To Do,2,48000,0.8
KAN-8,Done,2,35000,0.8linked_to,#,status,+,−,cycles
KAN-9,#8,merged,2,2,0
KAN-8,#7,merged,2,2,0
KAN-5,#4,merged,39,0,0
KAN-10,#9,open,4,4,0
KAN-4,#3,open,1,1,0
KAN-4,#1,open,1,1,0gh). Here is exactly how to get both.Read epics and stories from your Atlassian workspace
Go to id.atlassian.com → Security → API tokens and log in with your JIRA email.
Click Create API token, give it a label like "engmetrics-ai" and click Create.
It is the domain you see in the browser when logged in, e.g. https://yourco.atlassian.net
Discover and enrich pull requests linked to each epic
Choose your OS:
All installers: cli.github.com
Download from python.org or use your package manager. Check with python --version.
pipx installs CLI tools in isolated environments so they never conflict with your project dependencies.
One command installs the CLI and adds ai-engineering-metrics to your PATH.
Run with --mock to see a full dashboard with synthetic data. No JIRA or GitHub required.
Run from inside your repo with a .env file. The tool reads it automatically and picks up the GitHub remote.
# 1. Install pipx $ pip install --user pipx $ python -m pipx ensurepath # 2. Install the CLI $ pipx install git+https://github.com/dennisrojaspereira/engmetrics-ai.git # 3. Try the demo $ ai-engineering-metrics analyze --mock # 4. Create .env in your project folder $ cat > .env <<EOF JIRA_BASE_URL=https://yourco.atlassian.net JIRA_EMAIL=[email protected] JIRA_API_TOKEN=your-jira-api-token GITHUB_ORG=your-org GITHUB_REPOSITORIES=your-repo EOF # 5. Analyze your epic $ cd your-repo $ ai-engineering-metrics analyze -e PROJ-42 --open ✓ Analysis complete: PROJ-42 9.2h saved (40%) · 580k tokens · Risk: 19.5 (low) reports/PROJ-42/dashboard.html ← opening...
| Flag | Default | Description |
|---|---|---|
--epic / -e | DEMO-1 (mock) | JIRA epic key to analyze, e.g. PROJ-123 |
--mock | off | Use synthetic data — no credentials required |
--output / -o | reports/ | Base output directory. A sub-folder named after the epic is created inside |
--format / -f | all | all · html · json · csv |
--open | off | Open the generated dashboard in your browser when done |
--stdout | off | Print metrics JSON to stdout — pipe to jq |
--repo / -r | auto | Override GitHub repo as owner/name |
--verbose / -v | off | Show every API call and PR-matching decision |
--version | — | Print installed version and exit |
# Pipe metrics to jq ai-engineering-metrics analyze -e PROJ-123 --stdout | jq .ai_usage # HTML only, to a custom folder ai-engineering-metrics analyze -e PROJ-123 -f html -o ./out # Override GitHub repo ai-engineering-metrics analyze -e PROJ-123 --repo acme/backend # Update to the latest version pipx upgrade ai-engineering-metrics