cross-plugin-detective

The cross-plugin-detective skill serves as the foundational integration layer for connecting specialized AI agents to indexed memory investigation capabilities. By enforcing a consistent mapping between agent roles—such as developers, architects, testers, and reviewers—and their corresponding detective skills, this tool ensures that agents operate with a unified, high-performance investigative strategy. The primary purpose is to move beyond primitive discovery methods and leverage structured, indexed memory access for codebase navigation, pattern discovery, and architectural analysis. It is designed for engineering teams that require consistent, high-fidelity code discovery workflows across complex, multi-plugin repository environments.

• Standardized Agent-to-Skill mapping ensures that 'developer' roles automatically utilize developer-detective capabilities while 'architect' roles leverage architect-detective logic.

• Enforces a core principle of Indexed Memory Only, strictly mandating claudemem search over legacy methods like grep, find, or rg to improve speed and reduce token consumption.

• Provides a formal Decision Tree to guide agent selection, ensuring that technical debt audits, implementation lookups, and debugging sessions are handled by the optimal sub-agent.

• Includes support for multi-skill agent configurations, allowing senior reviewers and comprehensive analysis bots to stack diagnostic capabilities.

• Facilitates seamless integration across frontend, backend (Bun), and custom plugin architectures.

• Always verify claudemem status using the status command before performing complex investigations.

• Prefer semantic search via claudemem search for feature implementations or dependency mapping rather than traditional file scanning.

• Refer to the provided yaml frontmatter examples to correctly define agent skills in your project configuration.

• Adhere to the strict prohibition against using grep or glob tools to ensure data retrieval remains within the optimized indexed memory framework.

• Best suited for production environments where maintaining consistent developer experience and reducing AI API costs are critical success factors.