ai-data-report — community ai-data-report, hipoteca-findr-dify, community, ide skills, Claude Code, Cursor, Windsurf

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: 3/5/2026

Agent Capability Analysis

The ai-data-report skill by theam is an open-source community AI agent skill for Claude Code and other IDE workflows, helping agents execute tasks with better context, repeatability, and domain-specific guidance.

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

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
Labs Demo

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Boot Container Sandbox

ai-data-report

Install ai-data-report, an AI agent skill for AI agent workflows and automation. Works with Claude Code, Cursor, and Windsurf with one-command setup.

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

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is ai-data-report?

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

How do I install ai-data-report?

Run the command: npx killer-skills add theam/hipoteca-findr-dify. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for ai-data-report?

Key use cases include: Generating project reports with production URL analysis, Automating report commits to track project history, Creating data visualizations for session and initial reports.

Which IDEs are compatible with ai-data-report?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for ai-data-report?

Requires access to the .claude/reports/ directory. Limited to Markdown report format. Dependent on Next.js and Git for functionality.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add theam/hipoteca-findr-dify. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use ai-data-report immediately in the current project.

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