Pesquisa e Web

Builds or updates the code knowledge graph via tree-sitter AST and SQLite. Use when setting up the graph before search or blast-radius analysis.

Provisions the oracle ML inference daemon with onnxruntime via uv. Use when setting up local ONNX model inference for skill quality evaluation.

Applies microkernel architecture with minimal core and plugin extensibility. Use when building platforms where third parties extend core functionality.

Audits the DSA problem bank for coverage gaps and proposes new YAML entries. Use when refreshing the problem bank during update-plugins runs.

Provide reusable patterns for validation, error handling, scaffolding. Use for skill consistency.

Updates documentation after code changes with quality gates, slop detection, and accuracy checks. Use when code changes require corresponding doc updates.

Establishes CPU/GPU baselines before resource-intensive operations. Use before builds, training runs, or any task that pins cores or GPUs for over a minute.

Generates phased, dependency-ordered implementation tasks from specifications. Use after spec is complete and before starting implementation.

Applies stewardship virtues (Care, Curiosity, Humility, Diligence) to plugin work. Use when authoring plugins or reviewing code quality.

Processes external resources into stored knowledge with quality scoring and routing. Use when ingesting articles, papers, or docs into a memory palace.

Provides weighted scoring, rubrics, and decision-threshold patterns. Use when designing quality gates, evaluation systems, or decision frameworks.

Runs multi-source research across GitHub, HN, Reddit, arXiv, and Semantic Scholar. Use when surveying a technical topic across multiple channels.