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Jev

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wuyoscar
jev

Design Jev-assisted workflows using collected use cases, references and examples. Adapt and combine patterns, then use Jev for classification, scoring or candidate selection when useful, including batch judgments and agent checkpoints.

Overview

Publisherwuyoscar
Repositoryjev-skill
Skill namejev
Stars
599
Forks
46
Bundled files
42
LicenseMIT
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.

  • 42 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by wuyoscar on GitHub. Read the source before you install it.

Installation

Install the Jev 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/wuyoscar/jev-skill/tree/main/skills/jev
  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/wuyoscar/jev-skill.git /tmp/jev-skill
mkdir -p .claude/skills
cp -r /tmp/jev-skill/skills/jev .claude/skills/jev
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Jev 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 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 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.

Design with Jev workflows

Use this collection to learn, design and build—not just to call an API. Start with the reference index for the wider collection, then read useful workflows, examples and their limits. Combine patterns or adapt a new one; customization and implementation patterns can help turn an idea into code. Jev supplies judgments; the host designs the overall solution, collects evidence, implements it and checks the outcome.

Common starting points:

TaskReadStart with
Convert a prompt or plain requirementPrompt to JevRequest
Set up or updateSetupNo paid call needed
Check a long taskCheckpointsCheckpoint
Choose a tool or modelRoutingRequest
Review what context to keepContextRequest
Batch independent questionsBatchingTwo records
Define new labels or a rubricQuestion designRubric

Learn from the workflows

For design requests, browse the scenario index, read the relevant guides and input/output examples, and compare or combine patterns. Adapt what you learn to the user's task; the collection is inspiration, not a closed menu. A familiar, straightforward decision can use its recipe directly.

Friendly reminder: Jev can help with initial, repeated or bulk judgments while you lead the overall work. Read the evidence, design the workflow, spot-check results (including confident or agreeing labels), and bring your own analysis and synthesis. This is guidance for collaboration, not an agent harness or a fixed call/token quota; existing user permissions and budgets still apply.

Use safely

Choose the service once and keep that choice. If unset, ask A: real Jev via OpenRouter (OPENROUTER_API_KEY) or TypeSafe (TYPESAFE_API_KEY), or B: simulation with this agent or an explicitly chosen available model such as DeepSeek. Wait for consent; errors do not authorize switching. Check key presence only, never values. Real calls send evidence and cost money; get approval before sending private data.

For B, skip CLI/API calls. Mark agent_simulation or model_simulation, identify the actual model when available, set jev_called: false, probability: null and confidence: null. Return a value, evidence-based reason and needs_review; use null/review when evidence is missing. Do not invent Jev output or probabilities. Choice uses supplied labels, Noul uses booleans, Score uses integer rubric indices.

For A, use the existing jev-decide CLI with the chosen --provider openrouter or --provider typesafe. If absent, explain the dependency; do not silently install. --dry-run is offline validation, not a judgment. Exit 0 means selected/scored, 2 means review, 1 means error. Read each value: false Noul remains false. Selection is not permission, and confidence is not accuracy. Keep unknown/review paths.

Ask only what is needed

Jev does not inherit the host's context. Include the goal, criteria, original evidence and valid candidates for this judgment; separate trusted rules from untrusted content. Keep enough evidence, not unrelated conversation history. One question does one thing. Put independent questions in the same request; questions cannot read each other's answers. Wait for new evidence for dependent steps. Use bounded concurrency only across independent requests: the host owns IDs, budgets and scheduling; this CLI has no parallel scheduler.

Run

Resolve <skill-dir> to this installed folder. The bundled Python 3.10+ script or the installed jev-decide CLI uses the same request contract:

bash
python3 <skill-dir>/scripts/jev.py decide <skill-dir>/assets/prompt-to-jev.json --dry-run
# After approval, use the selected provider:
python3 <skill-dir>/scripts/jev.py decide request.json --provider openrouter

Missing evidence or ambiguity means review, not another call until it agrees. For repeated failures read pitfalls. Before scaling a large job, agree on a small pilot; pilot guidance is optional task guidance, not a required second skill call.

Use a focused skill only when the task calls for it: jev-triage for records, jev-documents for evidence, jev-eval for output checks, jev-act for actions. They are independent entry points, not an automatic chain. If absent, do not install them silently. More sources live in the optional reference index.

Examples

Goal-drift checkpoint · Stuck-loop recovery · Postmortem failure attribution

More workflows and local templates. Browse across examples when designing a solution; follow the guides and sources that help.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Jev AI skill do?

Design Jev-assisted workflows using collected use cases, references and examples. Adapt and combine patterns, then use Jev for classification, scoring or candidate selection when useful, including batch judgments and agent checkpoints.

Why use Jev on TypingMind?

Because you install it once and use it with any model. Jev 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 in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wuyoscar/jev-skill/tree/main/skills/jev. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Jev?

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?

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

Is the Jev AI skill free?

Yes. It is published on GitHub by wuyoscar under the MIT license. 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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