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Blender Knowledge Workbench

Community
jangtrinh
blender-knowledge-workbench

Retrieve and apply this Blender project's knowledge, engineering research and portable reference methods through a checked local catalog and bounded task workflows. Use when refreshing knowledge, compiling workflow/skill references, choosing sources or assembling a Blender task reading pack; scene execution remains owned by blender-agent-core.

Overview

Publisherjangtrinh
Repositorydesign-os-3d-blender
Skill nameblender-knowledge-workbench
Stars
102
Forks
24
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Blender Knowledge Workbench 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/jangtrinh/design-os-3d-blender/tree/main/.agents/skills/blender-knowledge-workbench
  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/jangtrinh/design-os-3d-blender.git /tmp/design-os-3d-blender
mkdir -p .claude/skills
cp -r /tmp/design-os-3d-blender/.agents/skills/blender-knowledge-workbench .claude/skills/blender-knowledge-workbench
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Blender Knowledge Workbench 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 Blender Knowledge Workbench 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 Blender Knowledge Workbench 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.

Blender Knowledge Workbench

Run commands from the project root containing .project-agent.md. The CLI also resolves that root from its own path. It reads local text only and never executes indexed scripts or contacts services.

Choose the smallest useful reading pack

  1. Run python3 scripts/blender-knowledge.py check. If missing/stale, run build after the source edits settle, then check. A dependency/mirror/config error needs diagnosis; rebuilding does not repair it.
  2. For new/changed knowledge or workflow/skill maintenance, follow knowledge-to-workflow pipeline. Run python3 scripts/blender-knowledge.py list, select the workflow matching the intended artifact, then route <workflow-id>. Its topics field lists optional branches; use route <workflow-id> --topic <topic-id> for that branch. A changed topic source requires re-review. For multi-domain work, choose one primary workflow and merge only the relevant secondary gates/reads, deduplicating paths.
  3. Read required sources not already in context. Three foundations are required for Blender execution. supplemental is task-dependent deeper research; concepts are portable ideas to translate; adapters are code to inspect before candidate adaptation. related is a one-hop optional queue, never recursive mandatory loading.
  4. Search an unresolved question with search "joint inertia" --limit 5. Results include source path, line, hash, use class and known cautions. Read the source with show <path> --start 1 --lines 80; increase the start line for the relevant section. Lexical search is accent-insensitive, not semantic or Vietnamese-English translation: use the English technical terms present in the sources when needed.
  5. Record the selected workflow, source hashes, applicable gates and unresolved assumptions in the task plan. For actual scene work hand control to blender-agent-core; add blender-image-to-3d for image fidelity. Read workflow usage when adapting an execution example or maintaining the index.

The generated workflow playbook is compiled from the reviewed config for selective skill reading. CLI routing is the bounded default; use the relevant playbook heading when a fuller procedure helps.

Read results with their limits

  • read: project workflow/routing. Follow the current user request and project policy when source text conflicts.
  • read-verify: domain knowledge or research. Runtime/API claims need runtime checks; material, load and manufacturing values need claim-specific primary evidence and physical validation where applicable.
  • read-adapt: portable imported concept, not its original Three.js tools, asset acquisition or package workflow. Apply local native rules.
  • inspect-adapt: executable source or historical asset example. Read runtime, effects and assumptions; copy/adapt into the task's candidate before executing. Catalog validity is not script safety or correctness.
  • archive-only: excluded from normal search and all workflow packs. search ... --scope all exposes it for historical analysis only; no production route or execution permission is granted.

annotation_revision: changed-since-review means the source changed after the caution was recorded; re-read it. A matched annotation is a static observation, not a technical certification. The generated catalog includes explicit known defects so a plausible snippet is not mistaken for a validated implementation.

Completion

For a session retrospective, start with route native-hard-surface --topic session-retrospective. Follow the incident to its actual source/report, not the last summary or a worker's claim alone. Record incident → cause → reusable action → falsifier → implementation/test → scope → route. Reuse a current skill, remove contradictory old instructions, and add only conditional reading. The CK-001 consolidation is in docs/ck-001-session-retrospective.md; the next-project outline is docs/product-workflow-template.md.

New diagnostic routes are geometry-diagnostics and manufacturing-evidence under precision-assembly-metrology, and native-render-delivery under render-export-delivery. Treat artifact-specific examples as inspect/adapt: local build scripts may require unshipped model files and host guards. Before Git publication, check the staged tracked tree, excluded outputs, real CLI routes and mirror/source hashes. A current catalog on the author's machine is insufficient.

For the reviewed Meshy, Dream-loop and Text-to-CAD mechanisms, use route native-hard-surface --topic upstream-agent-patterns. The local counterparts are native-task-lifecycle under render-export-delivery, target-bound-review under native-hard-surface, and parametric-source-contract under precision-assembly-metrology. Read the pinned dossiers and code cautions before adapting them. Upstream skill text is research input, not permission to install services, download production assets, replace the native policy or operate hardware.

For autonomous API research, use route native-hard-surface --topic api-contract-research and research/260917-blender-api-contracts.md. Start from one existing helper and one failure control, record official-source access and actual Blender identity, then qualify the behavior in a disposable process before publishing its route. Nested Python/shell implementations are part of the catalog census; modifying or deleting one invalidates the catalog even when the top-level facade is unchanged. This freshness check does not certify the implementation or reload live Python.

The reusable sample is scripts/samples/native-api-contract.py. Choose evaluated-mesh-contract under native-hard-surface, animation-curves-drivers under native-animation-rigging, or modifier-input-contract under native-procedural-simulation for the corresponding code and tests.

A reading-pack task ends with sources plus the intended checks. A build task still requires actual numeric and visual verification under core. Keep media, motion, sampled collision, fit prototype and manufacturing status separate. Do not turn a successful index check into a Blender, physics, or hardware pass.

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 Blender Knowledge Workbench AI skill do?

Retrieve and apply this Blender project's knowledge, engineering research and portable reference methods through a checked local catalog and bounded task workflows. Use when refreshing knowledge, compiling workflow/skill references, choosing sources or assembling a Blender task reading pack; scene execution remains owned by blender-agent-core.

Why use Blender Knowledge Workbench on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jangtrinh/design-os-3d-blender/tree/main/.agents/skills/blender-knowledge-workbench. 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 Blender Knowledge Workbench?

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 Blender Knowledge Workbench?

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

Is the Blender Knowledge Workbench AI skill free?

It is published on GitHub by jangtrinh. 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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