testing-llm-features
De la autor
Builds an offline eval suite for LLM-powered features: golden reference cases per prompt template, deterministic scoring where possible, a regression gate on prompt, model and retrieval changes, and CI wiring with promptfoo, Vitest or deepeval. Use when a product ships prompts, agents or RAG, when a model or prompt upgrade needs a regression check, when LLM output is currently verified by someone eyeballing it, or when asked how to test a feature whose output is non-deterministic.
Instalare
npx skills add jaktestowac/awesome-copilot-for-testers --skill testing-llm-features -a claude-code
Se instalează în .claude/skills/testing-llm-features/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
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De ce 81
aiqbench calculează scorul de încredere din semnale publice. Nu este un audit de securitate, așa că citește un skill înainte să-l instalezi.
Skill-uri similare
Cele mai apropiate de acesta prin ce fac și când sunt folosite.
- LLM Output TestingPramodDutta/qaskillsTesting LLM-powered features including output quality validation, hallucination detection, token usage monitoring, and prompt regression testing.Testare★ 244
- ai-system-testingpetrkindlmann/qa-skillsTest AI/LLM features that ship in your product. Covers prompt regression testing, response quality evaluation, tool-call validation, hallucination and RAG grounding checks, nondeterministic-output strategies, red-team/safety scans, eval frameworks, and agent-as-target injection (indirect injection via tool output / RAG / scan reports, self-propagating payloads, data exfiltration via an agent) plus a bundled detector for untrusted content. Use when: "test our LLM feature," "prompt regression test," "eval framework," "hallucination test," "RAG grounding," "nondeterministic output," "AI feature testing," "red-team our chatbot," "indirect prompt injection," "agent reading untrusted tool output," "production AI quality." Not for: using AI to generate your own test code — use ai-test-generation. Not for: classifying CI failures with AI — use ai-bug-triage. Not for: EU AI Act / GDPR conformity of an AI feature — use compliance-testing. Not for: canary/flag rollout of an AI feature — use testing-in-production. RelateTestare★ 235
- testing-llm-guardrailsjaktestowac/awesome-copilot-for-testersTests the runtime validation around model output - schema conformance, PII leakage, moderation, refusal behaviour, tool-call safety - and the adversarial suite that tries to defeat it: direct prompt injection, jailbreaks, and indirect injection through retrieved documents, tool results and user content. Use when an LLM feature reaches real users, when a model can call tools or read untrusted content, when an AI feature needs a security-style test suite, or when asked whether a chatbot or agent can be manipulated.Testare★ 118
- prompt-engineerJeffallan/claude-skillsWrites, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot learning, creating system prompts with personas and guardrails, building JSON/function-calling schemas, or developing prompt evaluation frameworks to measure and improve model performance.Testare★ 12.1K
- Prompt TestingPramodDutta/qaskillsComprehensive prompt testing and LLM output evaluation skill covering hallucination detection, response quality scoring, regression testing for prompts, A/B testing, and building evaluation pipelines for AI-powered applications.Testare★ 244
- eval-llm-judgeCalibre-Labs/reforge-ai-evalsWrite LLM-as-judge evaluator prompts for AI products. Use this skill whenever you need to evaluate semantic properties of AI outputs that cannot be checked programmatically — output quality, tone, factual accuracy, edge case handling, reference alignment. Also use when auditing existing judge prompts for bias, vagueness, or compound criteria. Output is a judge prompt string and a Python runner function, saved to the project's evaluators file.Redactare★ 94
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