AI-Engineering-Coach README

The project's README from github.com/microsoft/AI-Engineering-Coach. As one markdown file: README.md.

AI Engineer Coach

better agentic engineering.
Analyze your AI coding assistant usage — any harness, one dashboard.

License: MIT VS Code 1.115+


https://github.com/user-attachments/assets/9f0239bf-20e0-459f-b137-17cce0edd1b2

This video contains AI-generated content.


What it does

AI Engineer Coach reads your local AI session logs and turns them into actionable insights — no data leaves your machine.

Screenshots

Timeline

Code Output

Premium Request Consumption

Activity Patterns - Projects

Activity Patterns - Work Hours

Anti-Patterns

Skill Finder

Context Quality

Context Management

Learning Center

Achievements

Agentic SDLC

Share Your Stats


Installation

The extension is not published to a marketplace or Releases page, so you build the .vsix yourself and install it. Pick whichever build path fits your setup.

Path 1 -- Dev Container build (no local Node.js/npm)

Prerequisites:

Steps:

  1. Clone the repo and open it in VS Code.
  2. Reopen in container.
  3. Run:
npm ci
npm run package
  1. Install the generated .vsix (see Install the built VSIX below).

Path 2 -- Local build

Prerequisites:

Steps:

git clone https://github.com/microsoft/ai-engineering-coach.git
cd ai-engineering-coach
npm ci
npm run package

Then install the generated .vsix (see below).

Install the built VSIX

macOS / Linux

code --install-extension ai-engineer-coach-*.vsix

Windows / PowerShell

code --install-extension (Get-ChildItem . -Filter 'ai-engineer-coach-*.vsix' | Select-Object -First 1).FullName

If the CLI does not work, install it from the VS Code UI: press Ctrl+Shift+P, type Install from VSIX, then browse to the .vsix file and select it.

After install:

  1. Open the command palette (Cmd+Shift+P / Ctrl+Shift+P)
  2. Run AI Engineer Coach: Open Dashboard
  3. Navigate pages from the sidebar, filter by workspace or harness

Run as a canvas in the GitHub Copilot app

The same dashboard also runs as a canvas inside the GitHub Copilot app, so you do not need VS Code to use it.

A canvas is an interactive side panel in the GitHub Copilot app. Rather than replying only in chat, the agent can open a canvas to show rich, task-specific UI that you view and interact with directly while you keep working. Extensions register their own canvases, and this repo ships one named AI Engineer Coach under .github/extensions/ai-engineer-coach/. It reuses the exact webview bundle from the VS Code extension and parses your local session logs in process, so nothing leaves your machine.

To open it:

  1. Clone this repo and open it as a project in the GitHub Copilot app.
  2. Build the project once:
npm install && npm run build
  1. Open the AI Engineer Coach canvas. On a fresh clone it shows a setup card with the build command and reloads into the full dashboard once the build finishes. No manual reopen needed.

A few features depend on the local VS Code language model and are hidden in canvas mode: Skill Finder, Learning Center, the Level Up section, and the Context Health AI review. Everything driven by your on-disk logs (Dashboard, Timeline, Coding Moments, Output, Patterns, Anti-Patterns) works the same. App sessions show up as GitHub Copilot App and terminal sessions as GitHub Copilot CLI in the harness breakdown.


Pages

Observe

Page Description
Dashboard Practice scores with week-over-week trends, daily activity chart, top workspace stats
Timeline Gantt-style session timeline with per-day drill-down and overlap detection
Coding Moments Screenshot gallery from AI coding sessions with story reels and workspace filtering

Measure

Page Description
Output Generated code volume by language, model usage table (token breakdown temporarily hidden)
Burndown Monthly AI token budget progress with projections (temporarily disabled)
Patterns 7×24 activity heatmap and work-life balance signals

GitHub App

This section appears only when the local GitHub Copilot app is installed.

Page Description
Productivity Project sessions with and without issues, pull request and merge conversion, and a seven-day PR merge-ratio trend
Issue credits Rough relative AI spend per GitHub issue across linked workspace, alias, creator, and coordinating sessions

Issue credit percentages are rough relative estimates, not accurate AI Credit or billing figures. They normalize the locally recorded total_nano_aiu usage across linked issues to show approximately where AI usage was spent. The percentages depend on issue links inferred from local workspace and issue-reference data and must not be used for billing reconciliation. Explicit GitHub issue URLs pasted in either of the first two session turns also establish the issue link; URLs pasted later are excluded to avoid treating research links as the session's source issue. All reconciliation and aggregation is local and read-only. The small organization avatars on this page are loaded directly from GitHub using the repository owner name; no session content or usage data is included in these image requests.

Improve

Page Description
Anti-Patterns Five practice score cards with severity ratings, concrete actions, and example prompts. 45 editable markdown rules plus a coverage heatmap
Rule Editor Create, edit, and tune detection rules visually or as raw markdown. Live-test against your data
Rule Playground Interactive REPL for the rule DSL with field browser, function catalog, and metric list
Data Explorer Browse session fields, view distributions, run ad-hoc filters
Skill Finder Discover repeated prompt patterns and matching community skills from the open-source catalog
Context Health Overall context score, agentic readiness checklist, workspace context map, AI-powered instruction-file review

Level Up

Page Description
Learning Center Personalized quizzes and code-comparison rounds generated from your actual usage
Achievements XP-based progression with Bronze → Silver → Gold → Diamond tiers
Agentic SDLC How you use AI across the full software-development lifecycle
Share Generate a shareable stat card and export Markdown/JSON summaries

Privacy


Code of Conduct

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

License

MIT

Disclaimer

This project is an open-source community effort by Microsoft employees. It is not an official Microsoft product and is not part of any Microsoft service or support offering. It is provided as-is with no warranties or guarantees.

AI-Engineering-Coach (microsoft/AI-Engineering-Coach)

Searching Database…

What is this?AI Engineer Coach is a VS Code extension (and GitHub Copilot app canvas) that reads your local AI coding assistant session logs and turns them into dashboards covering practice scores, usage trends, anti-patterns, and skill suggestions, with all analysis happening on your machine.
Is this a VS Code extension or a standalone app?It's primarily a VS Code extension, but the same dashboard also ships as a canvas inside the GitHub Copilot app, so you can use it without VS Code at all.
Do I need Node.js, or can I avoid installing it locally?For the local build path you need Node.js and npm; if you'd rather not install those, the Dev Container build path only requires VS Code, the Dev Containers extension, and Docker or Podman.
Can I trust the 'issue credits' numbers for billing?No — the per-issue AI spend percentages are described as rough relative estimates based on locally inferred issue links, explicitly not accurate billing or AI Credit figures.
Does any of my coding session data get sent anywhere?No — everything is read-only and parsed locally; the extension has no proprietary telemetry and doesn't phone home, though a few optional AI features call VS Code's built-in Copilot model API only when you invoke them.
What kinds of anti-patterns does it actually flag?45 editable markdown rules covering prompt quality, session hygiene, code review, tool mastery, and context management, each with severity ratings, concrete fixes, and example prompts shown on a coverage heatmap.
Do I need to use GitHub Copilot specifically?No, it works with 'any harness, one dashboard' by reading whatever local session logs exist; only a handful of optional AI features (Skill Finder, Context Health review, rule compiler) require VS Code's built-in Copilot language model.
What does the Skill Finder page do?It scans your repeated prompt patterns and matches them against an open-source community skill catalog to suggest reusable skills you could adopt.
What's the Agentic SDLC page for?It's part of the Level Up section and visualizes how you use AI across the full software-development lifecycle, alongside XP-based Achievements and a personalized Learning Center.
Creator
microsoft
Size
3.8 MB
Stars
4.3K
Updated
CommitOct 16
Age
5mo
Commits · 90 Days