em

CLI tools for engineering managers to codify the mechanics of managing a team and prioritizing working on the hard stuff.

Features

JIRA Metrics

• Cycle time analysis — scatter plot with percentile lines (50th/85th/95th), business-day calculation, IQR-based outlier filtering
• Throughput tracking — weekly/daily/biweekly/monthly delivery frequency with trend line
• Monte Carlo forecasting — probability-based completion dates for epics using historical throughput
• Longest cycle time table — highlights recently completed issues that took the most time
• Combined JIRA report — cycle time, throughput, forecasting, and longest CT in a single HTML file

GitHub

• Deployment frequency — track release cadence per team from GitHub Actions workflow runs

Snyk

• Open vulnerability tracking — counts by severity (critical/high/medium/low) and fix category (fixable/unfixable/ignored)
• Exploitability highlighting — flags issues with Proof of Concept maturity or higher
• Weekly trend chart — visualize how the vulnerability backlog changes over time
• Standalone Snyk security report

Combined Engineering Report

• Executive Healthcheck — at-a-glance summary of cycle time, throughput, active epics, deploy frequency, and Snyk vulnerability counts
• Aggregates JIRA, GitHub, and Snyk data into a single HTML report per team

Multi-team support — configure multiple teams; commands run per-team with separate output files

Installation

curl -sSL https://raw.githubusercontent.com/danlafeir/em/main/scripts/install.sh | sh

This script will detect your OS and architecture, download the latest pre-built binary, and install it to ~/.local/bin. Ensure ~/.local/bin is in your PATH.

Try it with mock data

No credentials needed to see it in action. The --mock-upstream flag spins up in-process mock servers and runs commands against realistic sample data:

em metrics report --mock-upstream

This generates a full combined HTML report — JIRA cycle time, throughput, GitHub deployment frequency, and Snyk vulnerability counts — using pre-built mock datasets. Open the output file in a browser to see what a real report looks like before you configure anything.

Requirements

At least one data source must be configured:

• JIRA Cloud — API token with read access to your projects
• GitHub — personal access token with repo read scope; GitHub Actions workflows used for deployment frequency You might need a classic token in the Github Enterprise setting
• Snyk — API token and org ID from your Snyk account

The binary runs on macOS and Linux (amd64/arm64). No runtime dependencies.

Build and Test

make build          # Build for current platform
make build-all      # Cross-compile for all supported OS/ARCH
make install        # Install to ~/.local/bin
make test           # Run all tests

Configuration

Run a single command to configure everything interactively in sequence — JIRA, GitHub, and Snyk — including team setup:

em metrics config

This walks through each provider one at a time, verifies credentials against the live API, and stores tokens securely in the system keychain. You can also configure each provider separately if you only need one or want to update a specific one.

Each data source has an interactive setup command that walks you through the required values and stores credentials securely in the system keychain.

JIRA

em metrics jira config

• Domain — your Atlassian subdomain (e.g. mycompany for mycompany.atlassian.net)
• Email — the email address associated with your Atlassian account
• API token — a personal API token from id.atlassian.com/manage-profile/security/api-tokens; stored in the system keychain
• Project / JQL filter — the JIRA project key or a JQL query that scopes all metrics to your team's issues; saved per team. JQL resolution order: --jql flag > jira.jql_filter_for_metrics config > jira.project config
• Issues worked in parallel — how many issues the team works on simultaneously; used as a throughput multiplier in Monte Carlo forecasts (default: 1)
• Workflow stages — maps your JIRA status names to workflow stages (Backlog, In Progress, Review, Done, etc.) for cycle time calculation; uses sensible defaults if not set
• Cycle time boundaries — which stage name marks the start and end of cycle time measurement

GitHub

em metrics github config

• Organization — your GitHub organization name
• API token — a personal access token with repo scope; stored in the system keychain
• Team slug — the team name used to look up deployment workflows
• Workflows — one or more GitHub Actions workflow names whose successful runs count as deployments for frequency tracking

Snyk

em metrics snyk config

• Site — Snyk API hostname (default: api.snyk.io; change for EU/AU tenants)
• API token — a Snyk personal or service account token; stored in the system keychain
• Organization — the Snyk org ID and display name to pull vulnerability data from

Further reading

• Cycle time — how it is calculated and what is measured
• Throughput — delivery frequency aggregation and period anchoring
• Monte Carlo forecasting — simulation model and percentile interpretation
• Outlier filtering — IQR-based removal of anomalous cycle times
• GitHub deployment frequency — what counts as a deploy
• Snyk vulnerability metrics — severity, fixability, and weekly trend