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Run automated local code reviews with a local LLM after tasks finish.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "pingpong" yet — see the docs or source repo.
After I finish the current feature, use pingpong to review my local changes. Focus on potential bugs, readability issues, and duplicated code, and list suggestions by severity.
A review report on local code changes with issue details, severity levels, and fix suggestions.
Before I commit, use pingpong to review the local diff. Identify issues that may affect stability, security, or test coverage, and suggest additional test cases.
A pre-commit risk checklist covering stability, security, and test coverage findings.
I just finished a refactor. Use pingpong to review the related code and determine whether it introduced logic regressions, inconsistent naming, or increased structural complexity, then suggest improvements.
A refactoring quality review highlighting regression risks, structural issues, and optimization opportunities.
Run ping tests and quickly check internet connectivity status.
Read and manage PingCode work items like defects and requirements with natural language.
Connect AI to Pingera monitoring data for querying and analysis.
Give MCP-compatible AI clients real context for Pinoox PHP HMVC projects.
Enable AI agents to manage kanban boards with cost tracking and local storage.
Access Pokémon data and simulate battles through an MCP tool for LLMs.