單元測試

Use for bugs/test failures. Reproduce, isolate root cause, fix, and validate with evidence.

Testing procedures for Kalahari project. Use for running tests and analyzing results.

Audits skills for discoverability and triggering effectiveness. Use when analyzing skill descriptions, checking trigger phrase coverage, validating progressive disclosure, reviewing SKILL.md structure, ensuring skill discoverability, testing skill triggering, improving skill descriptions, or evaluating whether a skill will be invoked appropriately. Also triggers when user asks about skill best practices, wants to improve skill discoverability, or needs help with skill structure.

Comprehensive TAG system validator and policy enforcer that monitors, validates, and corrects TAG usage across code, tests, and documentation. Use when ensuring TAG compliance, validating TAG policy violations, analyzing TAG coverage, or when maintaining TAG system integrity and governance.

Test game via browser with Chrome DevTools. Navigate UI, play through game flow, observe behavior and report issues.

Unit testing, performance optimization, security implementation, Play Store deployment. Use for production-ready app development.

Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes - four-phase framework (root cause investigation, pattern analysis, hypothesis testing, implementation) that ensures understanding before attempting solutions

Upgrade React applications to latest versions, migrate from class components to hooks, and adopt concurrent features. Use when modernizing React codebases, migrating to React Hooks, or upgrading to latest React versions.

Validate code examples across the 4-Layer Teaching Method with intelligent strategy selection.Use when validating Python/Node/Rust code in book chapters. NOT for production deployment testing.

Produces comprehensive, well-sourced research reports through iterative refinement using Time-Tested Diffusion methodology. Triggers on "deep research", "thorough analysis", "comprehensive report", "investigate", "due diligence", or when multiple sources are needed to answer complex questions.

ALWAYS USE when working with dbt models, SQL transformations, tests, snapshots, or macros. MUST be loaded before editing any .sql files, dbt_project.yml, or profiles.yml. Enforces dbt owns SQL principle - never parse SQL in Python.

CI/CD pipeline best practices including GitHub Actions, testing, and deployment strategies.

Enforce Rust best practices including ownership patterns, error handling, testing, documentation, and code quality standards. Use when working with Rust code or when the user requests Rust-specific guidance. Includes patterns for cargo tooling, performance optimization, and idiomatic Rust conventions.

Set up GitHub Actions workflows for CI/CD with automated testing, linting, and deployment for Python/UV projects. Use when creating CI pipelines, automating tests, or setting up deployment workflows.

Sensitivity analysis frameworks and assumption-testing methods

Use when building professional installers for desktop applications - covers macOS DMG with app bundles, Windows MSI with WiX, Linux DEB packages, GitHub Actions automation, and SLSA attestations

Palantir 本地文档（en/latest）

Test-Driven Development workflow for .NET with xUnit, FluentAssertions, and NSubstitute. Use when implementing features using TDD practices.

Use when building features with TCA (The Composable Architecture), structuring reducers, managing state, handling effects, navigation, or testing TCA features. Covers @Reducer, Store, Effect, TestStore, reducer composition, and TCA patterns.