Development

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

Manages digital garden notes, link structures, and health metrics. Use when curating a knowledge base, pruning stale notes, or tracking content maturity.

Defines testing quality metrics, coverage thresholds, and anti-patterns. Use when establishing test gates or validating a test suite's coverage targets.

Tracks quotas, monitors thresholds, and degrades gracefully for rate-limited APIs. Use when integrating external services that impose rate or cost limits.

Provides standardized pytest config, reusable fixtures, and CI integration patterns. Use when setting up or auditing a Python plugin's test infrastructure.

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

Provides auth patterns for API keys, OAuth, and token management. Use when implementing or reviewing service authentication and credential handling.

Formats review deliverables with consistent structure for comparable findings. Use when finalizing any review or analysis that must be shared or compared.

Scores feature worthiness and enforces branch-size limits against overengineering. Use when evaluating whether a feature belongs in the current scope or branch.

Analyzes changesets with risk scoring, categorization by type and impact, and release note preparation. Use when extracting insights from raw change data.

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.

Delegates tasks to Gemini CLI implementing delegation-core for Google's models. Use when delegation-core selects Gemini or 1M+ token context is needed.

Selects and routes to the right architecture paradigm. Use when choosing patterns for a new system or comparing trade-offs before making architecture decisions.

Applies data-grid architecture for high-traffic stateful workloads. Use when a single database cannot scale and in-memory partitioning is needed.

Applies coarse-grained service architecture for deployment independence. Use when independent deployment is needed but shared databases rule out microservices.

Applies serverless FaaS patterns for event-driven workloads. Use when designing bursty workloads with minimal infrastructure and pay-per-execution cost model.

Applies pipes-and-filters for sequential data transformations. Use when data flows through discrete stages like ETL, streaming analytics, or CI/CD pipelines.

Applies modular monolith with enforced internal boundaries. Use when teams want service-level autonomy without distributed system overhead.

Applies microservices for independent deployment and per-service scaling. Use when teams need autonomous release cycles with distinct capability scaling needs.

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

Applies layered n-tier architecture with enforced boundaries. Use when designing moderate systems needing clear presentation, domain, and persistence layers.

Applies hexagonal architecture isolating domain from infrastructure. Use when designing systems where testability and port/adapter separation are priorities.

Applies Functional Core, Imperative Shell to isolate logic from side effects. Use when business logic is entangled with I/O or unit tests are slow and brittle.

Applies event-driven async messaging to decouple producers and consumers. Use when designing real-time or multi-subscriber systems needing loose coupling.