What I Did — GitHub Copilot Impact Report

One command. See everything you built with Copilot — and the leverage you're getting from your seat.

---

Try it now

Option A — Install as a Copilot CLI plugin (on GitHub CLI only)

/plugin install whatidid@awesome-copilot

Then just run:

whatidid                # defaults to a 7-day lookback

Option B — Clone the repo

git clone https://github.com/microsoft/What-I-Did-Copilot.git
cd What-I-Did-Copilot

copilot or code         # to open Copilot CLI or VS Code
python whatidid.py      # defaults to a 7-day lookback

That's it. A report opens in your browser showing your last 7 days with Copilot.

---

What you'll see

✅ Goals & leverage	Every project with human effort equivalents — see that a 10-min session replaced 3 hours of work. What did Copilot actually deliver?
📦 Artifacts produced	Scripts, reports, docs, configs — counted and categorized. What tangible output came out of your AI sessions?
🧠 Skills augmented	Hours mapped across 20+ roles — engineer, analyst, designer, architect. What skills did Copilot make accessible to you?
🎯 Collaboration style	Building, researching, designing, iterating — your AI signature. How are you directing AI across your work?
⏰ Activity heatmap	When you collaborate and how your day breaks down. When is AI most useful in your workflow?
📐 Estimation evidence	Transparent methodology grounded in 13 peer-reviewed sources. Why should anyone trust these numbers?

---

More options

whatidid --14D                        # last 14 days
whatidid --30D                        # last 30 days
whatidid --date 2026-03-19            # specific date
whatidid --from 2026-03-01 --to 2026-03-31   # date range
whatidid --7D --email                 # send via Outlook
whatidid --7D --email you@company.com # send to a specific address
whatidid --refresh                    # force re-analysis

🏗️ How It Works

~/.copilot/session-state/<uuid>/events.jsonl
                │
                ▼
           harvest.py    → scan sessions, extract messages, tools, files, intents
                │
                ▼
           analyze.py    → AI categorization via GitHub Models API (gpt-4o-mini)
                │         → calibrated effort estimation with quantitative signals
                ▼
           report.py     → HTML report: story arc, donut charts, heatmaps, ROI
                │
                ▼
         whatidid.py     → opens report in browser; --email sends via Outlook COM

See docs/architecture.md for session file formats, token cost model, and leverage calculation details.

See docs/effort-estimation-methodology.md for the research basis, signal definitions, and calibration logic behind effort estimates — grounded in 13 peer-reviewed sources including Alaswad et al. 2026, Cambon et al. 2023 (Microsoft Research), Ziegler et al. 2024 (CACM), and the SPACE framework (Forsgren et al. 2021).

🔒 Privacy

Your data stays on your machine. This tool is completely local-first:

• Reads only local files — session logs from ~/.copilot/session-state/ that already exist on your machine
• No telemetry, no tracking, no cloud uploads — the tool never phones home
• AI analysis is optional — uses GitHub Models API (authenticated via your own gh CLI token) to semantically interpret sessions. Without API access, a local heuristic fallback produces estimates with zero external calls
• Email is optional — the --email flag sends the report via your own Outlook client. If you don't use it, the HTML file stays on disk
• No one has access to your report unless you share it — the output is a standalone HTML file saved to your local project directory

The tool processes the same session data that GitHub Copilot already stores locally. It adds nothing new to disk beyond the HTML report and a small analysis cache in cache/.

📋 Requirements

Requirement	Why
Python 3.10+	Core runtime
GitHub CLI (gh)	Provides API token for AI analysis — run gh auth login
GitHub Copilot	Session data source — must have active sessions in ~/.copilot/session-state/
Microsoft Outlook	(Optional) For --email delivery via COM automation — auto-detects recipient from GitHub auth

No pip install needed — the core report generator (harvest.py, analyze.py, report.py, whatidid.py) uses only the Python standard library + GitHub Models API.

🤝 Copilot Agent

This tool ships as a Copilot CLI agent. Anyone who clones the repo gets it automatically — run /agent in Copilot CLI and select whatidid, or just ask naturally:

"What did I build this week?"

See .github/agents/whatidid.agent.md for the agent definition.

📄 License

MIT

---

⭐ If you find this useful, consider starring the repo — it helps others discover it and signals to the community that research-grounded AI productivity tools matter.

_{Keywords: GitHub Copilot ROI, Copilot usage report, Copilot activity tracker, AI productivity metrics, token usage analysis, Copilot impact measurement, developer productivity, AI-assisted development analytics}