What is Blamely AI?
Blamely AI is the local-first AI code attribution layer backed by Oobeya for teams that need to understand who or what wrote code as AI coding assistants become part of daily development.
Git history can show who committed a change, but it usually cannot explain whether the line was written by a human, accepted from an assistant, or generated by an AI coding workflow. Blamely records that origin line by line and stores it with the repository through git notes.
Oobeya connects Blamely AI attribution signals with delivery, review, quality, security, AI impact, and team-level metrics so leaders can evaluate generative AI impact with the right engineering context.
Why connect Blamely AI to Oobeya?
Blamely AI adds code-origin visibility close to the development workflow through a local CLI, editor extensions, git notes, and attribution reports. Oobeya turns that visibility into decision-ready reporting by connecting AI attribution with pull request flow, rework, defects, security findings, delivery performance, and governance conversations.
- How much of our code is AI-assisted, AI-generated, or human-authored?
- Which AI coding tools and IDE workflows are contributing to our codebase?
- Which repositories and teams are using AI coding workflows most heavily?
- Where does AI-assisted code create rework, review friction, or quality risk?
- How should governance teams set policy boundaries for AI-generated code?
- How can leaders measure AI value beyond adoption and usage counts?
What Oobeya analyzes from Blamely AI
| Blamely AI signal area | Oobeya visibility |
|---|---|
| Code-origin attribution | Line-level AI-assisted, AI-generated, and human-authored contribution context across code changes. |
| Assistant and editor context | Signals from Claude Code, Cursor, GitHub Copilot, Codex CLI, Gemini CLI, Devin, OpenCode, VS Code, JetBrains, Antigravity, and related editor workflows. |
| Git and report context | Attribution stored in git notes and summarized in local reports across commits, files, tools, models, and token usage. |
| Review flow | Review pass rates, rework, merge behavior, and collaboration patterns around AI-assisted changes. |
| Quality and security | Defects, vulnerabilities, code churn, and quality gates interpreted with attribution context. |
| AI governance | Team-level visibility for policy exceptions, usage boundaries, and responsible AI development. |
Engineering performance metrics from Blamely AI data
Blamely AI attribution becomes more valuable when local code-origin signals are evaluated with the engineering system around them. Use this page to compare AI-assisted contribution patterns with review, quality, rework, delivery, and governance context.
AI-assisted code share
Observe how much work is AI-assisted, AI-generated, or human-authored across teams, repositories, and pull requests.
Tool and model visibility
Understand which assistants, models, editors, and CLI workflows contributed to committed code.
Review and rework context
Analyze whether AI-assisted changes are associated with higher review pressure, code churn, rewrite patterns, or merge delays.
Quality and delivery correlation
Compare attribution signals with quality gates, defects, cycle time, and DORA context without claiming causality from attribution alone.
Governance and cost conversations
Use code-origin context alongside assistant usage and cost data to support responsible AI policy and investment reviews.
Blamely AI Integration Setup Guide
The Blamely AI documentation explains the local CLI, supported AI coding tools, editor extensions, git notes storage, reports, and privacy model for teams that want AI attribution without sending code to a cloud service.
Open Blamely AI integration documentation