Unit Testing

Create clear, testable specifications with user stories and acceptance criteria. Triggers: spec writing, feature specification, requirements, user stories Use when: creating new specifications or writing acceptance criteria DO NOT use when: generating implementation tasks - use task-planning.

Reusable patterns and templates for Claude Code skill and hook development. Triggers: validation patterns, error handling, testing templates, workflow patterns, shared patterns, reusable templates, DRY patterns, common workflows Use when: creating new skills or hooks that need consistent patterns, implementing validation logic, setting up error handling, creating test scaffolding, referencing standard workflow structures DO NOT use when: pattern is specific to one skill only. DO NOT use when: pattern is still evolving - wait for stability. DO NOT use when: pattern is context-dependent requiring variations. Reference these patterns to validate consistency across the ecosystem.

Compose processing stages using a pipes-and-filters model for ETL, media processing, or compiler-like workloads. Triggers: pipeline architecture, pipes and filters, ETL, data transformation, stream processing, CI/CD pipeline, media processing, batch processing Use when: data flows through fixed sequence of transformations, stages can be independently developed and tested, parallel processing of stages is beneficial DO NOT use when: selecting from multiple paradigms - use architecture-paradigms first. DO NOT use when: data flow isn't sequential or predictable. DO NOT use when: complex branching/merging logic dominates. Consult this skill when designing data pipelines or transformation workflows.

Python testing with pytest, fixtures, mocking, and TDD workflows. Triggers: pytest, unit tests, test fixtures, mocking, TDD, test suite, coverage, test-driven development, testing patterns, parameterized tests Use when: writing unit tests, setting up test suites, implementing TDD, configuring pytest, creating fixtures, async testing DO NOT use when: evaluating test quality - use pensive:test-review instead. DO NOT use when: infrastructure test config - use leyline:pytest-config. Consult this skill for Python testing implementation and patterns.

Enforces "prove before claim" discipline - validation, testing, and evidence requirements before declaring work complete. Triggers: completion, finished, done, working, should work, configured, ready to use, implemented, fixed Use when: claiming ANY work is complete, recommending solutions, stating something will work, finishing implementations DO NOT use when: explicitly asking questions or requesting clarification DO NOT use when: work is clearly in-progress and not claiming completion CRITICAL: This skill is MANDATORY before any completion claim. Violations result in wasted time and eroded trust.

Guide to effective Claude Code skill authoring using TDD methodology and persuasion principles. Triggers: skill authoring, skill writing, new skill, TDD skills, skill creation, skill best practices, skill validation, skill deployment, skill compliance Use when: creating new skills from scratch, improving existing skills with low compliance rates, learning skill authoring best practices, validating skill quality before deployment, understanding what makes skills effective DO NOT use when: evaluating existing skills - use skills-eval instead. DO NOT use when: analyzing skill architecture - use modular-skills instead. DO NOT use when: writing general documentation for humans. YOU MUST write a failing test before writing any skill. This is the Iron Law.

Functional Core, Imperative Shell: isolate deterministic logic from side effects for testability. Triggers: functional core, imperative shell, pure functions, testability Use when: business logic is entangled with I/O or tests are brittle DO NOT use when: simple scripting without complex logic.

detailed development workflow with modular patterns for git, code review, testing, documentation, and deployment

Standardized pytest configuration for plugin development with shared test patterns. Triggers: pytest configuration, conftest, fixtures, test setup Use when: setting up pytest for plugin development or creating fixtures

Monitor and optimize CPU/GPU usage with load measurement and cost-effective validation strategies. Triggers: CPU usage, GPU usage, performance, load monitoring, build performance, training, resource consumption, test suite, compilation Use when: session starts (auto-load with token-conservation), planning builds or training that could pin CPUs/GPUs for >1 minute, retrying failed resource-heavy commands DO NOT use when: simple operations with no resource impact. DO NOT use when: quick single-file operations. Use this skill BEFORE resource-intensive operations. Establish baselines proactively.

Shared testing quality metrics and standards for cross-plugin use. Referenced by pensive:test-review and parseltongue:python-testing. Triggers: testing standards, quality metrics, coverage thresholds, test quality, anti-patterns, testing best practices, quality gates Use when: evaluating test quality, setting coverage thresholds, identifying testing anti-patterns, establishing quality standards DO NOT use when: simple scripts without quality requirements. Consult this skill when establishing testing quality standards.

Employ the Hexagonal (Ports & Adapters) pattern to decouple domain logic from infrastructure, maximizing flexibility and testability. Triggers: hexagonal architecture, ports and adapters, infrastructure independence, dependency inversion, clean architecture, domain isolation, adapter pattern, infrastructure abstraction, database independence, framework independence Use when: designing systems with strong business logic separation, anticipating infrastructure changes, needing easy mocking for tests, building portable domain code DO NOT use when: selecting from multiple paradigms - use architecture-paradigms first. DO NOT use when: building simple CRUD apps without complex domain logic. Consult this skill when implementing hexagonal patterns or migrating to port-based design.

System architecture: modules, project structure, ADRs, and testing. Use when designing or reviewing systems.

Systematically uncover and fix bugs using language-specific expertise and reproducible evidence. Triggers: bug hunting, defect detection, debugging, fix verification, bug fix, regression check, error investigation, defect documentation Use when: deep bug hunting needed, documenting defects, verifying fixes, systematic debugging required DO NOT use when: test coverage audit - use test-review instead. DO NOT use when: architecture issues - use architecture-review. Use this skill for systematic bug hunting with evidence trails.

Evaluate and upgrade test suites with TDD/BDD rigor, coverage tracking, and quality assessment. Triggers: test audit, test coverage, test quality, TDD, BDD, test gaps, test improvement, coverage analysis, test remediation Use when: auditing test suites, analyzing coverage gaps, improving test quality, evaluating TDD/BDD compliance DO NOT use when: writing new tests - use parseltongue:python-testing. DO NOT use when: updating existing tests - use sanctum:test-updates. Use this skill for test suite evaluation and quality assessment.

Go conventions for hexagonal architecture, project structure, error handling, testing, and observability. Use when writing Go services.

Create distributable Python packages with proper structure and publishing. Triggers: Python packaging, pyproject.toml, uv, pip, PyPI, distribution, CLI tools, entry points, package structure, publishing Use when: creating Python packages, configuring pyproject.toml, setting up entry points, publishing to PyPI, CI/CD for packages DO NOT use when: testing packages - use python-testing instead. DO NOT use when: optimizing package performance - use python-performance. Consult this skill for Python package creation and distribution.

Transform project brief into detailed, testable specifications using spec-driven development methodology

Ensuring software correctness and reliability by writing automated tests, using quality assurance tools, and systematically debugging issues.

Evaluate Clojure code via nREPL using clj-nrepl-eval. Use this when you need to test code, check if edited files compile, verify function behavior, or interact with a running REPL session.