code-documentation

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

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

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

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

Enforce the full delivery cycle for every implementation task.

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

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.

Analyze test coverage and drive it to 80%+ by writing missing tests.

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