Deep code optimization audit using parallel specialist agents. Each agent hunts for performance anti-patterns, inefficiencies, and suboptimal code using pattern-based detection (Grep/Glob) WITHOUT reading the full source code first — avoiding anchoring bias on existing implementations. Covers ALL optimization domains: database queries, memory leaks, algorithmic complexity, concurrency, bundle size, dead code, I/O & network, rendering/UI, data structures, error handling, caching, build config, security-performance, logging, and infrastructure. Use when asked to: "optimize my code", "find performance issues", "audit code quality", "speed up my app", "find bottlenecks", "code review for performance", "find anti-patterns", "improve code efficiency", "reduce latency", "optimize performance", "code smell detection", "find slow code", "optimize this project", "performance audit", "code optimization". Also triggers on: "optimizar codigo", "encontrar cuellos de botella", "mejorar rendimiento".
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Security
1 high severity finding. You should review these findings carefully before considering using this skill.
The skill handles credentials insecurely by requiring the agent to include secret values verbatim in its generated output. This exposes credentials in the agent’s context and conversation history, creating a risk of data exfiltration.
The prompt instructs agents to extract and report "the problematic snippet" (and to read 5–10 lines of context) from the repository, which forces the LLM to include verbatim code fragments that may contain hardcoded secrets or credentials.
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