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Model Prompt Pack

CommunityPopular
mohitagw15856
model-prompt-pack

Use when asked to write prompts for image-to-3D or text-to-3D tools, generate 3D models of a topic's parts, plan the assets for a 3D explorer, or build a labelled parts list with sources. Produces prompts for image-to-3D tools, one per object, plus a parts list for labelling in which every label names a trusted source, and states that generated geometry is illustrative, not authoritative. Second step of explorer-pipeline.

Overview

Publishermohitagw15856
Repositorypm-claude-skills
Skill namemodel-prompt-pack
Stars
1.4K
Forks
257
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 mohitagw15856 on GitHub. Read the source before you install it.

Installation

Install the Model Prompt Pack 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/mohitagw15856/pm-claude-skills/tree/main/plugins/pm-3d-explorer/skills/model-prompt-pack
  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/mohitagw15856/pm-claude-skills.git /tmp/pm-claude-skills
mkdir -p .claude/skills
cp -r /tmp/pm-claude-skills/plugins/pm-3d-explorer/skills/model-prompt-pack .claude/skills/model-prompt-pack
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Model Prompt Pack 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 Model Prompt Pack 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 Model Prompt Pack 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.

Model Prompt Pack

Image-to-3D tools can turn a clean reference image into a usable mesh in minutes, but they invent detail: an extra valve, a wrongly placed bone, a plausible part that does not exist. For an explorer that teaches, the geometry can be approximate; the labels cannot be wrong. This skill writes one prompt per object for generating reference images and meshes, and a parts list in which every label is tied to a named trusted source.

The accuracy rule

  1. Every label needs a named trusted source: a textbook with edition, a standards body, a peer-reviewed paper, a museum or manufacturer reference, or an educational body's published material. A URL alone is not a source unless it identifies the publisher.
  2. Generated geometry is illustrative, not authoritative. Every output that shows the model says so, and the viewer displays the statement.
  3. No source, no label. A part the user cannot source is either dropped or shown as "unlabelled".

Required Inputs

Ask for these if not provided:

  • Topic and object(s): one assembly, or several separate objects
  • Level of detail: which parts a learner must identify, from the interface brief if there is one
  • Trusted sources available to the user, or the curriculum or standard to follow
  • The 3D tool, if chosen (for example an image-to-3D service, or an open model such as TRELLIS or Hunyuan3D), and any polygon or file-size limits
  • Style: realistic, stylised or diagrammatic

Output Structure

1. Asset plan

Decide, and say why: one mesh per part (generate each part separately, then assemble; best for exploded views and clicking) or one mesh split afterwards (generate the whole, then separate parts in a 3D editor). Most explorers need one mesh per selectable part.

2. Prompt cards, one per object

Object: [part name]                     Mesh name in GLB: [kebab_or_PascalName]
Reference image prompt:
  "[part], isolated, centred, three-quarter view, neutral light grey background, soft even
   studio lighting, [style], clear silhouette, no text, no labels, no shadow on background"
Negative prompt: text, watermark, multiple objects, cropped, motion blur, extra parts
Image-to-3D settings: single object; target about [n] triangles; PBR texture [size]; GLB output
Fit check: compare the generated mesh against [named source figure] for shape and position

3. Parts list for labelling

| Mesh name | Label | One-line description (learner level) | Source (publisher, title, edition or date, page or figure) | Confidence (high, medium) |

The mesh names in this table must match the names in the GLB exactly; explorer-viewer-builder reads them.

4. Assembly and export notes

Scale (one unit equals one centimetre, or state otherwise), origin at the assembly's centre, each part as a separately named node, total GLB size target (under 15 MB for the web), and Draco or meshopt compression if the viewer supports it.

5. Illustrative statement

The exact sentence for the viewer, for example: "This 3D model is illustrative. Shapes and proportions are approximate; labels follow [source]."

Quality Checks

  • There is exactly one prompt card per object or part to be generated
  • Every label in the parts list names a trusted source with publisher and title
  • No label appears without a source; unsourced parts are dropped or marked unlabelled
  • Mesh names in the parts list match the names the prompt cards assign
  • Every prompt card includes a fit check against a named source figure
  • The illustrative statement is included and names the source used for labels
  • Export notes state scale, origin and a file-size target

Anti-Patterns

  • Trusting the mesh. Generators add plausible detail; check each part against the source figure.
  • Wikipedia as the only source. Use it to find the underlying reference, then cite that.
  • Prompting for the labelled diagram. Text in generated images is unreliable and copies textbook layouts; generate clean objects and label in the viewer.
  • One giant mesh. Without separate parts there is nothing to click or explode.

Example Trigger Phrases

  • "Write image-to-3D prompts for the parts of the human heart, with a sourced parts list."
  • "Plan the 3D assets for a jet engine explorer."
  • "Give me a prompt per part for generating a plant cell in 3D."
  • "I need labelled 3D models for a volcano cross-section. Sources must be cited."

Frequently asked questions

What does the Model Prompt Pack AI skill do?

Use when asked to write prompts for image-to-3D or text-to-3D tools, generate 3D models of a topic's parts, plan the assets for a 3D explorer, or build a labelled parts list with sources. Produces prompts for image-to-3D tools, one per object, plus a parts list for labelling in which every label names a trusted source, and states that generated geometry is illustrative, not authoritative. Second step of explorer-pipeline.

Why use Model Prompt Pack on TypingMind?

Because you install it once and use it with any model. Model Prompt Pack 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 Model Prompt Pack in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mohitagw15856/pm-claude-skills/tree/main/plugins/pm-3d-explorer/skills/model-prompt-pack. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Model Prompt Pack?

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 Model Prompt Pack?

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

Is the Model Prompt Pack AI skill free?

Yes. It is published on GitHub by mohitagw15856 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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