greploop
0Greploop is designed for AI agents to facilitate efficient data querying and retrieval. It's suitable for developers looking to integrate data querying capabilities into their AI applications.
27200Updated 2mo ago
Data Querying
Quick InstallView Source
$ npx skills add https://github.com/greptileai/skills --skill greploopOverview
About This Skill
Greploop is an AI agent skill that automates the code review optimization process across GitHub, GitLab, and Perforce. It continuously triggers Greptile code reviews, identifies actionable comments, applies fixes, and re-submits changes in iterative cycles—capping at 5 iterations to prevent runaway loops. The skill detects your version control platform automatically, retrieves review feedback from multiple sources (PR descriptions, comments, and reviews), and parses Greptile's confidence scores and unresolved comments to determine when a perfect review is achieved. Developers and teams benefit by eliminating manual review-fix-resubmit cycles, ensuring code meets Greptile's standards consistently, and reducing the time spent addressing code quality feedback.
How to Use
Install using the command below and integrate into your project.
Use Cases
Streamline data retrieval processes
Enhance AI agents with querying capabilities
Automate data analysis tasks
Tags
Setup & Installation
Quick Install
Terminal
$ npx skills add https://github.com/greptileai/skills --skill greploopAlternative Install (Git Clone)
git clone https://github.com/greptileai/skillsRequirements
- Claude Code or compatible AI agent
Quick Start Guide
1
Install the Skill
Copy the install command above and run it in your terminal.
2
Open Your AI Agent
Launch Claude Code, Cursor, or your preferred AI coding agent.
3
Try It Out
Use the prompt template or examples below to test the skill.
4
Customize
Adapt the skill to your specific use case and workflow.
Usage Examples
Prompt Template
Query [DATA] from [COMPANY]'s [INDUSTRY] database using greploop. Filter for [SPECIFIC_CRITERIA] and return the results in a structured format. Ensure the query is optimized for performance and handles potential edge cases like missing data or large datasets.
Example Output
### Query Results for Customer Churn Analysis **Dataset:** [COMPANY] - SaaS Industry (Q3 2024) **Total Records Retrieved:** 1,247 #### Key Findings: - **High Churn Risk Customers (Churn Probability > 80%):** - Customer ID: CS-4567, Name: TechStart Inc., Last Interaction: 2024-09-15, Usage Score: 0.2 - Customer ID: CS-7890, Name: GreenEnergy Co., Last Interaction: 2024-09-12, Usage Score: 0.1 - **Top 5 Reasons for Churn (by Frequency):** 1. Lack of feature adoption (45%) 2. Pricing concerns (30%) 3. Poor onboarding experience (15%) 4. Competitor switching (8%) 5. Product bugs (2%) #### Recommendations: - Target **CS-4567** and **CS-7890** for retention campaigns with personalized offers. - Address **feature adoption gaps** highlighted in the "Lack of feature adoption" category. *Query optimized for 98% accuracy with 2.1s response time.*
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