🤖 What I Did — GitHub Copilot Impact Report

Turn invisible AI collaboration into a visible story of impact.

One command generates a polished report on what you built with Copilot, the skills it augmented, and the leverage it delivered.

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"In March, Copilot delivered $4,380 worth of professional services for a $39/mo seat — a 112× return on investment." — Real output from this tool

"Where did the tokens go? What am I actually building with Copilot? Am I accessing skills I didn't have before?"

If you can't answer these confidently, you're not alone. Most developers know Copilot helps — but can't show how much, at what, or why it matters. This tool turns your local session logs into a single report that makes the invisible visible — what got built, what skills were augmented, and what it would have cost to do it alone.

Built for anyone who works with Copilot — developers, PMs, analysts, vibe coders, and anyone building with AI. Use it to recap what you created, share progress with your team and manager, or generate evidence for performance reviews. If Copilot helped you build it, this tool makes sure the story gets told.

✅ What Got Accomplished — and the Leverage Behind It

Every project broken down into tasks with human effort equivalents — see that a 10-minute Copilot session replaced 3 hours of manual work. The ROI banner distills it to one number: how many multiples of your $39/mo seat Copilot delivered in professional services value.

📦 What Got Produced

Tangible artifacts — scripts, reports, documents, presentations, config files — categorized and counted. Not "Copilot helped me code" but "Copilot helped me ship 4 Python modules, 2 HTML reports, and a PowerShell deployment script."

🧠 Skills Copilot Augmented

Hours of assistance mapped across 20+ professional roles — Software Engineer, Data Analyst, UX Designer, Solutions Architect, and more. See exactly which disciplines Copilot staffed for you, on demand, at zero headcount cost.

🎯 How You Collaborated

Every interaction classified by intent — Building, Researching, Designing, Investigating, Iterating, Shipping. Discover your collaboration signature and whether Copilot is an always-on tax or a targeted force multiplier.

⏰ When You Collaborated

Time-of-day activity patterns with a daily heatmap — spot whether you're an early-morning builder or a late-night debugger, and whether AI assistance is concentrated or spread across your day.

📐 Complexity Evidence

Collapsible estimation detail showing the quantitative signals behind every effort number — tool invocations, conversation turns, iteration depth, and the deterministic formula. Evidence that Copilot isn't just handling boilerplate — it's tackling real complexity. Grounded in peer-reviewed research →

📸 Sample Report

Report generated with python whatidid.py --14D

🚀 Quick Start

1. Clone the repo

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

2. Open in GitHub Copilot CLI or VS Code

# Option A: Open in VS Code with Copilot
code What-I-Did-Copilot
   
# Option B: Use GitHub Copilot in the terminal
cd What-I-Did-Copilot
gh copilot

3. Run your first report

# Last 7 days (default)
python whatidid.py

# Lookback shortcuts — any number of days
python whatidid.py --7D
python whatidid.py --14D
python whatidid.py --30D

# Specific date
python whatidid.py --date 2026-03-19

# Date range (e.g., all of March)
python whatidid.py --from 2026-03-01 --to 2026-03-31

# Send report via Outlook (auto-detects your email from GitHub auth)
python whatidid.py --email

# Send to a specific address
python whatidid.py --14D --email you@company.com

# Force re-analysis (bypass cache)
python whatidid.py --refresh

4. (Optional) Set up a shortcut

Add this to your PowerShell profile ($PROFILE) so you can run whatidid from anywhere:

function whatidid { python "C:/path/to/What-I-Did-Copilot/whatidid.py" @args }

Then:

whatidid --14D --email

🏗️ 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

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_{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}