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Jev Blender

CommunityPopular
kerpopule
jev-blender

Use for all Blender work. Default code-first execution, local QA and bounded Jev diagnostic advice.

Overview

Publisherkerpopule
Repositoryhermes-jev-skills
Skill namejev-blender
Stars
1.3K
Forks
124
Bundled files
Instructions only
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.

  • Self-contained

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

  • Open source

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

Installation

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

Use it in TypingMind

Enable Jev Blender 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 Blender 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 Blender 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.

Default Blender workflow

Use this entry point for Blender tasks, alongside the task's geometry, animation and production skills. It grants no new paid, public, production, credential or customer-data authority. Do not change model routing or restart a gateway to use it.

Choose execution locally

Use a deterministic background script for a known-ahead build or long final render. For repeated inspect/edit cycles, prefer an installed, owned warm-session adapter with private pipes and isolated preferences. Load blender-warm-session when installed. Do not install an unreviewed DSH plugin, adopt an existing Blender GUI or expose a control port. If warm execution is unavailable, use the verified background-script route and disclose the fallback.

Keep one bounded resource owner, a whole-job deadline, an exclusive disposable workspace and explicit source/output identities. Preserve source files. Never detach a worker across turns. Blender exit success alone is not artifact acceptance.

Check before asking a model

Measure applicable geometry, materials, framing and lighting against the task specification. Unknown checks must be measured; failures return to the owning planner for a reviewed repair. Do not label an unperformed or inapplicable check as passed. Apply role-aware topology rules, not a blanket closed-solid assumption. Save native checkpoints before risky edits.

Use jevkit.blender.select(state) or pipe the closed JSON schema in docs/blender.md into jev blender-next. No scene files, screenshots, code, names, paths, free text or private measurements may enter this helper. The only permitted symptoms are its fixed vocabulary. private=true always bypasses inference.

Known steps run locally. Jev may prioritize a prevalidated read-only inspection only when the visual cause is unclear and a measured candidate probe costs at least one second. Cap the advice request at one second and the owned loop at two model requests and six probes. Skip Jev for cheap work. Its latency and confidence do not prove the selected cause. Preserve abstentions and failures; never lower confidence to obtain an action.

Jev cannot write Blender code, authorize a scene edit, invent arguments or declare completion. Dispatch only exact caller-owned read-only probe IDs. Verify returned results locally, then let the owning planner author any repair. Stop on stale/no-progress observations, exhausted budgets, unknown IDs or script errors. An unavailable chooser returns control to the local planner, not success.

Finish on real evidence

Re-run failed checks on the repaired artifact. Inspect decoded representative views and applicable motion frames. Reopen native files and independently reimport exports where relevant. Only an explicit local artifact verifier can mark the workflow complete. Close the owned worker and verify cleanup. Report timings, failures and untested boundaries honestly. Synthetic decision tests do not prove artistic quality, fewer agent turns, universal speedups or superiority over MCP.

Frequently asked questions

What does the Jev Blender AI skill do?

Use for all Blender work. Default code-first execution, local QA and bounded Jev diagnostic advice.

Why use Jev Blender on TypingMind?

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

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

Which AI models can use Jev Blender?

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 Blender?

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

Is the Jev Blender AI skill free?

Yes. It is published on GitHub by kerpopule 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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