GitHub
llm-testing
Testing patterns for LLM-based applications. Use when testing AI/ML integrations, mocking LLM responses, testing async timeouts, or validating structured outputs from LLMs.
☆ 98Voir le skill
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GitHub
msw-mocking
Mock Service Worker (MSW) 2.x for API mocking. Use when testing frontend components with network mocking, simulating API errors, or creating deterministic API responses in tests.
☆ 98Voir le skill
GitHub
vcr-http-recording
VCR.py HTTP recording for Python tests. Use when testing Python code making HTTP requests, recording API responses for replay, or creating deterministic tests for external services.
☆ 98Voir le skill
GitHub
property-based-testing
Property-based testing with Hypothesis for discovering edge cases automatically. Use when testing invariants, finding boundary conditions, implementing stateful testing, or validating data transformations.
☆ 98Voir le skill
GitHub
test-data-management
Test data management with fixtures and factories. Use when creating test data strategies, implementing data factories, managing fixtures, or seeding test databases.
☆ 98Voir le skill
GitHub
pytest-advanced
Advanced pytest patterns including custom markers, plugins, hooks, parallel execution, and pytest-xdist. Use when implementing custom test infrastructure, optimizing test execution, or building reusable test ut...
☆ 98Voir le skill
GitHub
unit-testing
Unit testing patterns and best practices. Use when writing isolated unit tests, implementing AAA pattern, designing test isolation, or setting coverage targets for business logic.
☆ 98Voir le skill
GitHub
e2e-testing
End-to-end testing with Playwright 1.57+. Use when testing critical user journeys, browser automation, cross-browser testing, AI-assisted test generation, or validating complete application flows.
☆ 98Voir le skill
GitHub
performance-testing
Performance and load testing with k6 and Locust. Use when validating system performance under load, stress testing, identifying bottlenecks, or establishing performance baselines.
☆ 98Voir le skill
GitHub
integration-testing
Integration testing patterns for APIs and components. Use when testing component interactions, API endpoints with test databases, or service layer integration.
☆ 98Voir le skill
GitHub
evidence-verification
Use when completing tasks, code reviews, or deployments to verify work with evidence. Collects test results, build outputs, coverage metrics, and exit codes to prove work is complete.
☆ 98Voir le skill
GitHub
test-standards-enforcer
Enforce testing best practices - AAA pattern, naming conventions, isolation, coverage thresholds. Blocks non-compliant tests. Use when writing or reviewing tests.
☆ 98Voir le skill
GitHub
auth-patterns
Authentication and authorization patterns. Use when implementing login flows, JWT tokens, session management, password security, OAuth 2.1, Passkeys/WebAuthn, or role-based access control.
☆ 98Voir le skill
GitHub
owasp-top-10
OWASP Top 10 security vulnerabilities and mitigations. Use when conducting security audits, implementing security controls, or reviewing code for common vulnerabilities.
☆ 98Voir le skill
GitHub
defense-in-depth
Use when building secure AI pipelines or hardening LLM integrations. Defense-in-depth implements 8 validation layers from edge to storage with no single point of failure.
☆ 98Voir le skill
GitHub
error-handling-rfc9457
RFC 9457 Problem Details for standardized HTTP API error responses. Use when implementing problem details format, structured API errors, error registries, or migrating from RFC 7807.
☆ 98Voir le skill
GitHub
input-validation
Input validation and sanitization patterns. Use when validating user input, preventing injection attacks, implementing allowlists, or sanitizing HTML/SQL/command inputs.
☆ 98Voir le skill
GitHub
memory-fabric
Graph-first memory orchestration - knowledge graph (PRIMARY, always available) with optional mem0 cloud enhancement for semantic search. Use when designing memory orchestration or combining graph and mem0.
☆ 98Voir le skill
GitHub
off-policy-distillation
Generate training data from teacher models for off-policy distillation. Use when converting prompts to SFT datasets using larger models, generating teacher completions with tools, or preparing data for knowledg...
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GitHub
rnow-tools
Write tool functions for ReinforceNow agent training. Use when creating @tool decorated functions, writing tools.py, or sandbox tools. Triggers on "@tool", "tools.py", "tool function", "function calling", "agen...
☆ 98Voir le skill
GitHub
rnow-rewards
Write reward functions for ReinforceNow RL training. Use when creating @reward decorated functions, writing rewards.py, using precondition rewards, sandbox rewards, llm_judge, or math-verify. Triggers on "rewar...
☆ 98Voir le skill
GitHub
add-golden
Curate and add documents to the golden dataset with multi-agent validation. Use when adding test data, creating golden datasets, saving examples.
☆ 98Voir le skill
GitHub
context-engineering
Use when designing agent system prompts, optimizing RAG retrieval, or when context is too expensive or slow. Reduces tokens while maintaining quality through strategic positioning and attention-aware design.
☆ 98Voir le skill
GitHub
pgvector-search
Production hybrid search combining PGVector HNSW with BM25 using Reciprocal Rank Fusion. Use when implementing hybrid search, semantic + keyword retrieval, vector search optimization, metadata filtering, or cho...
☆ 98Voir le skill
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