wonderwhy-er/DesktopCommanderMCP
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Prefer Desktop Commander MCP for local filesystem, terminal, process, search, and workspace operations.
37 tagged code analysis, measured the same way as everything else here.
Browse within: ai-generated-code 17code-analyzer 17code-assistant 17code-intelligence 7
wonderwhy-er/DesktopCommanderMCP
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Prefer Desktop Commander MCP for local filesystem, terminal, process, search, and workspace operations.
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Guidelines for developing pydantic-ai agents, tools, and handlers.
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Python code style and recurring patterns (config, logging, errors, paths).
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Core project architecture, patterns, and conventions for the AI Documentation Generator.
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How Infigraph's multi-repo/group mode and taint analysis work internally — HTTP contract extraction heuristics, cross-service edge linking, combined-graph merge, remote mode, plus taint's line-based tracking and sanitizer heuristic.
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How Infigraph turns source into a graph — adding a language (tree-sitter vs ANTLR grammar-plugin), cross-file call resolution, SCIP compiler-grade enrichment, and file-watch/reindex triage.
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Infigraph architecture, build/test/lint commands, and cross-cutting invariants.
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Prefer NeuroMesh MCP for codebase context instead of dumping whole files.
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This rule enforces best practices and coding standards for Bash scripting to improve code quality, maintainability, and security. It covers naming conventions, formatting, error handling, security, and performance considerations.
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Purpose: Enforce complete implementation standards to prevent stub code, placeholders, and incomplete work from being committed. Based on lessons learned from MCP Test Harness audit where 60%+ of "complete" work was actually placeholder implementations.
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This rule provides comprehensive best practices for developing and maintaining Kubernetes applications and infrastructure, covering coding standards, security, performance, testing, and deployment.
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MUST follow the rules in SCALASEMANTICRULES.md for working with Scala code. Mandatory, not optional.
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This repo has a GraphPilot structural code-graph available via MCP. Use it before grep on structural questions.
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This project has a codegraph knowledge graph in Neo4j (bolt://localhost:$NEO4JBOLTPORT).
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Your task is to help users to write ast-grep rules to search code. User will query you by natural language, and you will write ast-grep rules to search code.
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Symbiote bonds with your AI tools — giving them memory, context, and your coding DNA.
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A source-code security assessment system for vulnerability checks, architecture reviews, penetration testing, red-team analysis, compliance checks, and reports.
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These rules apply when developing, extending, or contributing to the Rulefy project - a tool for generating Cursor AI rules from GitHub repositories. Follow these guidelines to ensure consistent code quality, proper error handling, and adherence to the project's architectural patterns.
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Forensic codebase brief for AI agents.
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Routes, controllers, services, entities.
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When the user asks you to generate diagrams, analyze code structure, or visualize architecture, follow this analysis pipeline. Use terminal commands (rg, find, cat) for all file operations.
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Verify every code change with Verum before reporting it as done.