nw-rigor
De la autor
Selects a quality-vs-token-consumption profile (lean, standard, thorough, exhaustive, custom, inherit) and persists it globally (~/.nwave/global-config.json) or per-project (.nwave/des-config.json). Use when tuning how much rigor wave commands apply.
Instalare
npx skills add nWave-ai/nWave --skill nw-rigor -a claude-code
Se instalează în .claude/skills/nw-rigor/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
Identic octet cu octet cu commit-ul scanat, cu licența depozitului și un fișier SOURCE.txt.
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Verificări de siguranță pe SKILL.md
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De ce 84
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.
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- nw-roadmapnWave-ai/nWaveCreates a phased roadmap.json for a feature goal with acceptance criteria and TDD steps. Use when planning implementation steps before execution.Planificare★ 622
- rag-evalagentscope-ai/OpenJudgeUse when the user has a RAG (Retrieval-Augmented Generation) system and wants to evaluate its quality — separating retrieval issues from generation issues. Also use when the user mentions RAG evaluation, faithfulness checking, hallucination detection in RAG, retrieval quality, chunking optimization, or "is my RAG pipeline working." Outputs a diagnostic matrix that pinpoints whether problems are in retrieval or generation.Altele★ 887
- nw-distillnWave-ai/nWaveAcceptance test creation methodology for the DISTILL wave. Domain knowledge for the acceptance designer agent: port-to-port principle, prior wave reading, wave-decision reconciliation, graceful degradation, and document back-propagation.Testare★ 622
- finetuning-method-selectionwshobson/agentsDecide whether to fine-tune at all, and route to the right method (SFT, DPO/ORPO/KTO, GRPO/RLVR, continued pretraining) and base model. Use when starting any fine-tuning effort, when unsure whether RAG or prompting would suffice, or when choosing between preference-optimization and reinforcement methods.Altele★ 41.6K
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