coverage

Use proactively whenever academic literature could improve answer quality, even when the user does not mention papers or research.

Audit the codebase for violations of the project's core principles — detect unintended system-imposed constraints that contradict the project's stated goals.

Optimize AI-consumed code and data for token efficiency — reduce token usage while preserving semantic fidelity.

Enforce the full delivery cycle for every implementation task.

Detect project context and automatically select, apply, and propose skills.

Document public code surfaces and API behavior using the project's native documentation conventions while avoiding redundant comments and private…

Design the runtime environment around AI agents — constraints, guardrails, verification, sandboxing, feedback loops, and recovery mechanisms that make agents…

Define contracts before implementations.

Detect and enforce layered architecture boundaries.

Implement Retrieval-Augmented Generation pipelines — ingestion, chunking, embedding, retrieval, and generation.

Coordinate multiple agents — routing, delegation, parallelism, state sharing, and result aggregation.

Detect documentation drift from recent code changes and synchronize docs to match the current codebase.