Hermes Jev Skills provides reusable skills for model routing, search, memory filtering, triage and browser actions. Jev makes bounded choices such as selecting a model or ranking retrieved passages; the host model handles prose. The skills work with Hermes, Claude Code and Codex. Shadow mode records routing decisions without switching models.
Reranking & relevance
Projects that score passages, memories or candidates for relevance before an LLM reads them. Score and Noul answers replace generated judgements.
49 active projects match this task, ordered by traction. Browse other tasks →
489
Jev Search turns plain-language requests into web links and snippets with editable filters and visible relevance scores. For local use, configure Search1API and a Jev provider in .dev.vars, install dependencies with pnpm, and run pnpm dev.
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The repository collects separate latency-focused demos, including Sift email labeling and Turbo Rerank search ranking. Each app has its own setup: install dependencies in its directory, configure TYPESAFE_API_KEY and run its development command.
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Practical, tested recipes for TypeSafe's Jev decision model on OpenRouter: support triage, database indexing, file organizing, tagging, taxonomies, dedupe, PII detection, extraction, search re-ranking and a browser agent.
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Jev-powered relevance filtering and reranking for RAG in Python.
Does a TypeSafe Jev rerank beat embedding search? Graded relevance eval (9,831 pairs, 164 zh/en queries) over the Agent Skills Hub catalog, with the judge-circularity bias measured.
Can a decision model beat dedicated rerankers? TypeSafe Jev vs Cohere Rerank 4 vs ZeroEntropy zerank-2 vs a chat-model baseline: 14 datasets, every raw API response, bootstrap ranges on every gap.
Cut Claude Code's skill manifest by ~75% with TypeSafe Jev. Scores every installed skill for relevance and hides the rest via skillOverrides — 12,750 → 3,185 tokens on a 217-skill install, for $0.0009 a session.
An automated research paper radar that fetches the latest papers from arXiv, evaluates their relevance to a configurable research profile using TypeSafe AI, and ranks them by relevance score. Designed for personalized, daily literature discovery across different research domains.
Agent Seek — precision web recall for agents. You.com discover + TypeSafe Jev ranking. MCP + REST. Live demo: https://agentseek.dev
Drop-in relevance filter for LangChain RAG retrievers powered by TypeSafe Jev.
Contextual, inline clipboard decomposition for macOS — Tab-to-paste with full history and time-decay ranking. Powered by TypeSafe JEF.
2
Wikipedia link races with direct Jev ranking and a live terminal display.
2
Agentic RAG pipeline for biomedical evidence discovery: query planning and multi-round search across twelve public databases plus citation-graph expansion, ranking by relevance and study design, and citation-grounded synthesis with disagreements reported explicitly.
An MCP server and CLI that rank postings against your profile, explain every score, and measure the ranking against your own judgment.
Find Amazon keyword ideas with Nexscope and evaluate their relevance to listing text with Jev.
Production reranker benchmark: TypeSafe Jev vs Qwen3-Reranker-0.6B on BEIR SciFact and NFCorpus — nDCG@10, cost/query and p50/p95 latency. Quality is indistinguishable; Jev is ~2x faster, Qwen ~4x cheaper. From the team at denser.ai
Agent Skill and Python workflow for Clay lead scoring, B2B prospect qualification, and people-search ranking with TypeSafe JEV.
Semantic code completion for Neovim using Jev (TypeSafe AI) as a ranking oracle. Fuzzy candidates appear instantly; Jev re-ranks them by code context.
RAG quality gate adapted for TypeSafe Jev - relevance filtering + document injection detection, fan-out in one call
Does ORDER BY over a Jev probability put rows in a defensible order? Independent ranking, calibration and invariant measurements of TypeSafe AI's Jev: passes six pre-registered gates on 360 labeled rows, fails four of six on graded product relevance.
The data pipeline behind LinklyAI/best-skills — a daily, cross-ecosystem ranking of AI agent skills
0
System-1 decision models (Jev/Laya/Kev-class) as the governance layer for an LLM agent context lifecycle: growing association graph over session segments, relevance-gated recall with Trace-as-State ordering, and plan pre-ranking — measured in solve rate, cache hit/miss tokens, cost and latency. DSH plugin + harness-agnostic proxy.
Use Jev instead of an LLM for RAG decisions: relevance grading, query routing, hallucination checks and LLM-as-a-judge evals. Faster, cheaper, measured first.