Development

Builds session-scoped temporary memory palaces for extended conversations. Use when tracking state across interruptions in a multi-step project.

Designs memory palace structures with spatial layouts and domain organization. Use when creating a new palace or planning knowledge architecture by hand.

Searches and navigates stored knowledge in memory palaces. Use when looking for previously stored information or cross-referencing concepts across palaces.

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.

Employ space-based or data-grid architectures to scale high-traffic, stateful workloads across multiple nodes. Use when building high-scale systems with in-memory data grid patterns.

Employ a coarse-grained, service-based architecture (a lighter form of SOA) when microservices are not yet necessary but modular deployment is required. Use when building coarse-grained service-oriented systems.

Build serverless, Function-as-a-Service (FaaS) systems for event-driven or operations-light workloads. Use when building event-driven applications with minimal infrastructure management.

Compose processing stages using a pipes-and-filters model for ETL, media processing, or compiler-like workloads. Use when building data processing or transformation pipelines.

Maintain a single deployable artifact while enforcing strong internal boundaries between modules. Use when building systems that need module boundaries without distributed complexity.

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

Build a minimal, stable core system that loads plug-ins to provide feature variability and extensibility. Use when building extensible plugin-based systems.

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