{"product_id":"optimize-ai-decisions-with-jev-evidence-evaluation-tool","title":"Optimize AI Decisions with Jev Evidence Evaluation Tool","description":"\u003ch3\u003eOptimize AI Decisions with Jev Evidence Evaluation Tool\u003c\/h3\u003e\n\n\u003cp\u003eThe Jev Evidence Evaluation Tool is designed to measure and optimize the accuracy and efficiency of AI decision-making workflows. This skill focuses on evaluating the extent to which a Jev decision or cited claim is supported by the provided text. It assesses factors like accuracy, review rate, coverage, abstention, latency, usage, and cost using metrics such as source receipts, missing evidence, and contradictions in the test set. This tool is ideal for developers seeking to verify the reliability of decisions made by AI agents within platforms like Claude Code, Cursor, and Codex.\u003c\/p\u003e\n\n\u003ch3\u003eWhat this skill does\u003c\/h3\u003e\n\n\u003cul\u003e\n  \u003cli\u003eMeasures a TypeSafe Jev evidence classification workflow for accuracy and coverage.\u003c\/li\u003e\n  \u003cli\u003eEvaluates whether a decision or claim is substantiated by the given text, using binary and choice-based assessments.\u003c\/li\u003e\n  \u003cli\u003eFlags missing evidence without defaulting to a negative conclusion.\u003c\/li\u003e\n  \u003cli\u003ePreserves contradictory passages and ensures explicit review outcomes are documented.\u003c\/li\u003e\n  \u003cli\u003eProvides verbatim receipts from source spans in the code to trace the origin of evaluated text.\u003c\/li\u003e\n  \u003cli\u003eHandles long document retrieval coverage separately, ensuring comprehensive evaluation.\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch3\u003eWho it is for\u003c\/h3\u003e\n\n\u003cp\u003eThis skill is designed for developers and teams who integrate AI coding agents such as Claude Code, Cursor, and Codex into their workflows. It benefits those involved in AI model evaluation, accuracy verification, and workflow optimization.\u003c\/p\u003e\n\n\u003ch3\u003eUse cases\u003c\/h3\u003e\n\n\u003cul\u003e\n  \u003cli\u003eDetermining whether AI-generated decisions are based on adequately supported evidence.\u003c\/li\u003e\n  \u003cli\u003eEvaluating and optimizing classification workflows within AI systems for improved consistency.\u003c\/li\u003e\n  \u003cli\u003eComparing Jev workflows against other classification models or large language models (LLMs).\u003c\/li\u003e\n  \u003cli\u003eAdjusting confidence thresholds for accepting or flagging citations in AI decision-making processes.\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch3\u003eTechnical details\u003c\/h3\u003e\n\n\u003cul\u003e\n  \u003cli\u003eRelies on the TypeSafe API and confidence guide for implementation requests.\u003c\/li\u003e\n  \u003cli\u003eUtilizes agent-plugins, aiapplication, and jev-evidence-eval tools.\u003c\/li\u003e\n  \u003cli\u003eIntegrates with the broader capabilities offered by the official typesafe-ai skill available on GitHub.\u003c\/li\u003e\n  \u003cli\u003eEnsures that expected labels remain independent of the model state for unbiased evaluation.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c!-- mcpcart:static-blocks:start --\u003e\n\u003chr\u003e\n\u003ch3\u003eSource \u0026amp; Licence\u003c\/h3\u003e\n\u003cp\u003eThis package is built on open-source work published by \u003cstrong\u003elaguagu\u003c\/strong\u003e (\u003ca href=\"https:\/\/github.com\/laguagu\/jev-skills\" rel=\"nofollow noopener\" target=\"_blank\"\u003elaguagu\/jev-skills\u003c\/a\u003e) and distributed under \u003cstrong\u003eMIT\u003c\/strong\u003e. The original licence text and copyright notice are included in your download.\u003c\/p\u003e\n\u003cp\u003ePersonal and commercial use, modification and redistribution are permitted, provided the original copyright and licence notice are retained.\u003c\/p\u003e\n\u003cp\u003eYour purchase covers curation, licence verification, packaging, documentation and instant delivery. It does not grant exclusive rights to the underlying open-source code, which remains available under its original licence.\u003c\/p\u003e\n\u003ch3\u003eDelivery \u0026amp; Support\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eDelivery:\u003c\/strong\u003e instant — a secure download link is emailed to you as soon as payment is confirmed.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eFormat:\u003c\/strong\u003e ZIP archive containing the skill files, documentation and the original licence.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSupport:\u003c\/strong\u003e \u003ca href=\"mailto:support@mcpcart.com\"\u003esupport@mcpcart.com\u003c\/a\u003e — we aim to reply within 2 business days.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eUpdates:\u003c\/strong\u003e updates are included only where stated on this page.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch3\u003eRefunds\u003c\/h3\u003e\n\u003cp\u003eThis is a digital product delivered immediately after purchase. By completing your order you request immediate delivery and acknowledge that, once the download has been accessed, the statutory right to cancel no longer applies to the extent permitted by law. Refund requests are handled in accordance with our published Refund Policy.\u003c\/p\u003e\n\u003cp style=\"font-size:0.85em;color:#666;\"\u003eClaude, Codex, Gemini and Cursor are trademarks of their respective owners. MCP Cart is an independent marketplace and is not affiliated with, endorsed by, or sponsored by any of them. Compatibility references describe interoperability only.\u003c\/p\u003e\n\u003c!-- mcpcart:static-blocks:end --\u003e","brand":"MCP Cart","offers":[{"title":"Default Title","offer_id":53054677385527,"sku":"MCP-LAGUAGU-JEV-SKILLS-JEV-EVIDENCE-EVAL","price":35.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0981\/3950\/4951\/files\/xtpz5ix3TRPOKVZ1UbTQ4_f7092ff860c64000b1fdaada3f2d7708.jpg?v=1790507248","url":"https:\/\/mcpcart.com\/products\/optimize-ai-decisions-with-jev-evidence-evaluation-tool","provider":"SPF PRO","version":"1.0","type":"link"}