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Killer-Skills

ai-data-report — Categories.community

v1.0.0
GitHub

About this Skill

Perfect for Data Analysis Agents needing automated report generation capabilities with Markdown formatting and Git version control. Chatbot de hipotecas con Dify y Next.js

theam theam
[0]
[0]
Updated: 3/4/2026

Quality Score

Top 5%
46
Excellent
Based on code quality & docs
Installation
SYS Universal Install (Auto-Detect)
Cursor IDE Windsurf IDE VS Code IDE
> npx killer-skills add theam/hipoteca-findr-dify/ai-data-report

Agent Capability Analysis

The ai-data-report MCP Server by theam is an open-source Categories.community integration for Claude and other AI agents, enabling seamless task automation and capability expansion.

Ideal Agent Persona

Perfect for Data Analysis Agents needing automated report generation capabilities with Markdown formatting and Git version control.

Core Value

Empowers agents to generate comprehensive data-driven reports in Markdown format, utilizing Next.js and committing them to a Git repository for version tracking and history, leveraging file formats like .md and protocols like Git.

Capabilities Granted for ai-data-report MCP Server

Generating project reports with production URL analysis
Automating report commits to track project history
Creating data visualizations for session and initial reports

! Prerequisites & Limits

  • Requires access to the .claude/reports/ directory
  • Limited to Markdown report format
  • Dependent on Next.js and Git for functionality
Project
SKILL.md
2.3 KB
.cursorrules
1.2 KB
package.json
240 B
Ready
UTF-8

# Tags

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SKILL.md
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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:

  1. 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
  2. Gather data:

    • Read package.json, requirements.txt, .env.example to detect services
    • Check git log for recent commits
    • Check git remote -v for URLs
    • Look for production URLs in README or configs
  3. 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
  4. Format:

    • Use tables for structured information
    • Use emojis for main sections
    • Be concise but complete

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