found the perfect use case for @typesafeai Jev: instant compaction in 2026, why is compaction still a summarization prompt? Jev can make it instant by scoring every tool call and dropping what’s irrelevant
EcosystemTypeSafe agent skill
Agent skill
A drop-in SKILL.md that teaches coding agents to structure Jev states and questions and to batch questions per call.
- Category
- Ecosystem
- Also known as
- TypeSafe agent skill
- Related terms
- 4
- Directory entries
- 2
- Docs
- docs.typesafe.ai
- Added
- 2026-09-24
Definition
The official skill ships in typesafe-ai/skills and installs into Claude Code, Codex, and other agent environments. It teaches the right way to structure states and questions, and it exists because agents fall into the one-question-per-call habit more than people do.
Community skills extend the pattern: Hermes Jev Skills installs nine plain SKILL.md files for model routing, passage filtering, skill selection, and bounded computer or browser actions.
Tagsagentsskills
Related terms
Definitions that connect to this one.
Coding agentThe fastest-growing corner of the Jev ecosystem: MCP servers, tool-call gates, context pruners, and skill rankers.EcosystemMCP serverA server that exposes Jev to coding agents as a tool through the Model Context Protocol, usually one evaluate call.EcosystemParallel questionsBatching many questions into one request instead of many calls, which is dramatically cheaper and faster with identical answers.PatternsSkill suggestionRank a large skill catalog in one request, then re-read the top few in full detail and pick at most one.Patterns
From the directory
2.1kGitHub stars
Drop-in skill for Claude Code, Codex, and other agent environments that teaches the right way to structure states and questions.
Tools & IntegrationsDocs#official#agent#skill
Official
typesafe-ai/skills
typesafe-ai/skills811GitHub stars
A Python toolkit that installs nine plain SKILL.md files for Hermes, Claude Code, and Codex, letting Jev route models, filter retrieved passages, select skills, and choose bounded computer or browser actions.
Tools & Integrations#community#python#agents
Community
kerpopule/hermes-jev-skillsPython
kerpopule/hermes-jev-skillsPythonFrom the community
Posts from builders shipping with Jev right now.
Instant compaction with Jev
A Claude session from 1M to 86K tokens
This is actually insane. This uses @typesafeai Jev model, as a plugin in Claude to review all the un-nesseasary tool calls, and it takes 1s to run! Like, literally, 1 second to take my Claude session from nearly 1M to ... 86K tokens! 😮 Ask your claude to install it and be Show more
found the perfect use case for @typesafeai Jev: instant compaction in 2026, why is compaction still a summarization prompt? Jev can make it instant by scoring every tool call and dropping what’s irrelevant
Vercel's fx safety reviewer, 18x faster
We're seeing extraordinary results from @typesafeai. Default mode in 𝚏𝚡 is auto, with a safety reviewer analyzing every command. That reviewer runs on GPT Luna today. Jev is up to 18x faster (p95) *and* more accurate. It's coming to @vercel AI Gateway and likely new default.
@fazxes
We benchmarked fx auto mode (safety) classifier with @typesafeai's Jev. tl;dr: ~5-18x faster and more accurate than 𝚐𝚙𝚝-𝟻.𝟼-𝚕𝚞𝚗𝚊, our current top choice
Jev lands on OpenRouter
Jev by @typesafeai is now on OpenRouter, in beta. Jev is a System One model. Instead of generating text, it takes your app's state plus a typed question and returns a typed decision with a probability attached. There is no JSON prompting, parsing layer, and nothing to validate Show more
700 leads scored for $0.09
JEV is INSANE. We gave it 700 high-intent leads and personalised outreach messages. In 40 seconds, it predicted how each message would perform, assigned a confidence score and detected lead-message mismatches. All for just $0.09. JEV can also score leads, analyse buying Show more
Beating Gemini Flash Lite on an eval
Ran @typesafeai's Jev against an existing classifier eval that previously used Gemini 2.5 Flash Lite. It won both on quality (saturated the eval) and speed (6x)
