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Jev Decision

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Devin-AXIS
jev-decision

Use Jev to select among available tools, skills or agents, or evaluate outputs against explicit rubrics. Use when the user requests Jev or a bounded decision would help; not for open-ended writing or research.

Overview

PublisherDevin-AXIS
RepositoryiPolloWork
Skill namejev-decision
Stars
6.9K
Forks
1.1K
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Devin-AXIS on GitHub. Read the source before you install it.

Installation

Install the Jev Decision AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Devin-AXIS/iPolloWork/tree/main/examples/plugin-packages/jev-decision-model/skills/jev-decision
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Devin-AXIS/iPolloWork.git /tmp/iPolloWork
mkdir -p .claude/skills
cp -r /tmp/iPolloWork/examples/plugin-packages/jev-decision-model/skills/jev-decision .claude/skills/jev-decision
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Jev Decision in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Jev Decision on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Jev Decision is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Jev DSH 决策引擎

Jev returns structured judgments. The current Agent retains planning, parameter construction, execution and native subagent collaboration.

Call through the current host

  • In iPolloWork, discover jev-decision-model with ipollowork_extension_list_actions, then use ipollowork_extension_call with action evaluate and the payload below in args.
  • In standalone DSH, use jev_evaluate with the same payload. jev_status checks local configuration; jev_check_connection verifies the key with a small billable request.
  • Work also exposes status and check-connection. A configured key is not proof of a successful API connection. If missing or invalid, direct the user to plugin authorization (Work) or the TYPESAFE_API_KEY credential (DSH); do not request the key in chat.

Choose and prepare

Discover actual candidates from the host or the visible tool/skill catalog. Respect the user's explicit selections. Include only available, permitted candidates and their stable IDs, short descriptions and relevant constraints. Jev does not discover tools, install plugins, grant permissions or invoke another Agent by itself.

Send the task and minimal relevant context as state; do not send credentials, entire transcripts or complete files unnecessarily. Each question contains type, a complete instructions question, and the appropriate criteria. Question IDs identify results; they are not instructions to the model.

  • choice: choose one of 2–255 keyed candidates; include an other / none candidate when needed.
  • score: grade against 2–10 ordered levels, indexed from zero. Several independent scores can rank candidates.
  • noul: estimate the probability of a clearly stated yes/no condition. To select multiple complementary skills, batch one relevance question per candidate instead of forcing a single Choice. A value near 0.5 means uncertainty, not medium skill.

Batch independent questions sharing a state; use a later request if a question depends on an earlier result. Plugin bounds are 64 questions and 256 KiB per request. These are plugin limits, not claims about the provider's limits.

json
{
  "state": { "task": "整理已收集的竞品资料,制作汇报 PPT", "available": ["research", "slides"] },
  "questions": {
    "use_research": { "type": "noul", "instructions": "这项任务是否需要分析竞品资料?" },
    "use_slides": { "type": "noul", "instructions": "这项任务是否需要制作演示文稿?" }
  }
}

Use the result

Preserve the returned probabilities, confidence, model and usage when reporting. Jev does not return written reasoning: distinguish your explanation from its structured result. Check current availability again before execution. Read the selected Skill and construct tool arguments using the current host's schemas.

Thresholds and weights come from the user's task or application policy, not universal magic numbers. Low confidence, missing options, or API failure calls for ordinary Agent reasoning or clarification; say when Jev was unavailable instead of inventing its result. A recommendation does not authorize external actions, override the user's choice or bypass approval. No automatic global routing or self-training is installed by this plugin.

Reference: https://docs.typesafe.ai/api

Frequently asked questions

What does the Jev Decision AI skill do?

Use Jev to select among available tools, skills or agents, or evaluate outputs against explicit rubrics. Use when the user requests Jev or a bounded decision would help; not for open-ended writing or research.

Why use Jev Decision on TypingMind?

Because you install it once and use it with any model. Jev Decision is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Jev Decision in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Devin-AXIS/iPolloWork/tree/main/examples/plugin-packages/jev-decision-model/skills/jev-decision. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Jev Decision?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Jev Decision?

As many as you like. As long as a model supports skills, you can use Jev Decision with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Jev Decision AI skill free?

It is published on GitHub by Devin-AXIS. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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