Our review
Generates structured project reports (initial report or session summary) using real data from the codebase and Git history.
Strengths
- Uses concrete data from code (package.json, git log, etc.)
- Produces well-formatted reports with tables and emojis
- Saves and versions reports in the Git repository
- Automatically detects mode (initial or session) based on request
Limitations
- Relies on existing Git history and configuration files
- May miss context if the project is poorly documented
- Does not capture non-codified decisions or discussions
To generate an initial project report when onboarding to a new project or a session summary after a work session.
For informal notes or real-time comments during development.
Security analysis
SafeThe skill only reads local project files (package.json, requirements.txt, .env.example) and git history to generate reports. No external network calls, no execution of arbitrary code, no destructive commands. Reports are saved locally and committed, so no data exfiltration.
No concerns found
Examples
/project-report/ai-data-report session summaryname: ai-data-report description: "Generates data-driven reports about the project. Use for initial project reports or session summaries."
Skill: AI Data Report
Description
Generates data-driven reports about the project. Use /ai-data-report to invoke.
Where reports are saved
- Location:
.claude/reports/ - Naming:
YYYY-MM-DD-[type].md(e.g.,2026-01-22-session.md,2026-01-22-initial.md) - Git: Reports are committed to the repo for history tracking
Modes
1. Initial Report (first time on project)
Generates a complete report with:
## 📊 Project Report
**Production URL:** [production URL]
**GitHub URL:** [repo URL]
**Development time:** [estimated hours and context]
### Services used:
| Service | Purpose |
|---------|---------|
| [Service 1] | [What it does] |
| [Service 2] | [What it does] |
...
### Flow when someone uses the app:
1. [Step 1]
2. [Step 2]
...
### Tech stack:
- Backend: [technology]
- Frontend: [technology]
- Database: [technology]
- Hosting: [technology]
### Deployment:
- [How it deploys]
- [Where env variables are stored]
2. Session Report (when finishing work)
Generates a session summary:
## 📝 Session Summary
**Date:** [date]
**Approximate duration:** [time]
### Changes made:
| Area | Change | Files |
|------|--------|-------|
| [area] | [description] | [files] |
### Commits:
- `[hash]` [message]
### Bugs found/fixed:
- [bug 1]
### Suggested next steps:
- [ ] [task 1]
- [ ] [task 2]
### Metrics:
| Metric | Value |
|--------|-------|
| Lines changed | +X / -Y |
| Files modified | N |
| Commits | N |
| **Time - Claude** | ~Xh Xmin (coding, debugging, testing) |
| **Time - Human** | ~Xmin (reviewing, testing, giving feedback) |
Instructions for Claude
When user invokes /project-report:
-
Detect mode:
- If first interaction or they ask for "initial report" → Mode 1
- If they ask for "session summary" or "what did we do" → Mode 2
-
Gather data:
- Read
package.json,requirements.txt,.env.exampleto detect services - Check
git logfor recent commits - Check
git remote -vfor URLs - Look for production URLs in README or configs
- Read
-
Be data-driven:
- Use real data from code, don't make things up
- If data is missing, indicate "[pending configuration]"
- Include specific numbers when possible
-
Format:
- Use tables for structured information
- Use emojis for main sections
- Be concise but complete
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