Engineering Operating Metrics helps CTOs, engineering leaders, operating partners, and technology investors understand engineering effectiveness beyond vanity metrics.
The platform measures:
- Delivery Flow
- Review Quality
- Rework
- Engineering Cost
- AI Usage Cost
- Technical Risk
- Governance
Most engineering dashboards focus on activity.
Engineering Operating Metrics focuses on outcomes.
Pull request counts, lines of code, and comment volume do not tell a CTO whether engineering is improving delivery predictability, quality, cost discipline, or technical risk. This project provides an executive-facing operating view that connects engineering work to business outcomes.
- CTOs
- VP Engineering
- Engineering Managers
- Operating Partners
- Technology Due Diligence
- Boards
- median cycle time
- review cycles
- deployment and merge throughput
- blocked or aging work
- review quality score
- rework rate
- post-merge bug signal
- test and validation discipline
- estimated engineering cost
- cost by team or business area
- AI usage cost
- cost of rework
- technical risk score
- large change risk
- architectural impact
- high-risk service changes
- missing ownership
- missing linked issue or decision record
- risky changes without review
- policy and operating exceptions
Engineering Operating Metrics can be extended to track AI-related operating metrics such as external provider spend, local inference cost, retrieval usage, adoption trends, and AI-assisted delivery cost.
Metrics may include:
- external LLM provider spend
- local inference cost
- retrieval/search usage
- AI-assisted PR or workflow cost
- adoption trends by team or workflow
Demo Mode uses fictional PR data from data/sample-prs.json. It works without a GitHub token and is intended for executive demos, portfolio reviews, and product development.
Run:
pip install -r requirements.txt
streamlit run dashboard.pyThen select Demo Mode.
Live GitHub Mode uses GitHub API data from a repository you provide.
Set a token through the Streamlit sidebar or environment:
export GITHUB_TOKEN=your_token_here
streamlit run dashboard.pyTokens are used only for the local session. Do not commit tokens or .env files.
This repository is part of a broader Technology Leadership Portfolio: a practical system for assessing, operating, governing, implementing, and measuring technology organizations.
| Layer | Repository | Purpose |
|---|---|---|
| Methodology | CTO Operating System | Defines CTO, diligence, governance, board reporting, and operating partner frameworks |
| Assessment | Executive AI Advisor | Converts company documents into diligence reports, board briefs, CRA readiness assessments, AI governance assessments, and 100-day technology plans |
| Implementation | K8s Platform Blueprint | Provides implementation patterns for platform governance, FinOps, observability, policy controls, and compliance evidence |
| Measurement | Engineering Operating Metrics | Measures delivery flow, review quality, rework, engineering cost, AI usage cost, risk, and engineering governance |
This repository provides the measurement layer. See Technology Leadership Portfolio.
Engineering Operating Metrics is a lightweight executive analytics prototype. It intentionally avoids enterprise authentication, heavy data pipelines, and large frameworks so the operating model remains easy to inspect and extend.
Apache 2.0. See LICENSE.