Fal Ai Generation

Community

witnesstodark

fal-ai-generation

Generate and edit images, create video, audio, 3D assets or material maps through fal.ai MCP or the Python queue client. Use for fal model discovery, reference uploads, generation, recovery and local delivery.

Overview

Publisher	witnesstodark
Repository	`mr-mak-workspace`
Skill name	`fal-ai-generation`
Stars	545
Forks	183
Bundled files	9
License	MIT
Links	Skill source Repository

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.
9 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by witnesstodark on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/witnesstodark/mr-mak-workspace/tree/main/.agents/skills/fal-ai-generation
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/witnesstodark/mr-mak-workspace/tree/main/.agents/skills/fal-ai-generation

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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/witnesstodark/mr-mak-workspace.git /tmp/mr-mak-workspace
mkdir -p .claude/skills
cp -r /tmp/mr-mak-workspace/.agents/skills/fal-ai-generation .claude/skills/fal-ai-generation

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Fal Ai Generation 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 Fal Ai Generation 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 Fal Ai Generation 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.

fal.ai generation

Turn the user's brief and supplied references into downloaded, reviewed assets. Use the connected fal.ai MCP when available. The included Python helper handles local uploads and queue recovery when MCP cannot access the local filesystem. This skill and its relative resources can be copied into another project.

Choose the operation

Distinguish concept exploration, faithful editing, reference-sheet preparation, motion generation, mesh generation and material creation. Preserve a selected provider, model, accepted image, pose, camera and requested output count. An edit changes the requested feature; it does not redesign the whole asset.

When no model is selected, call recommend_model with the actual task. Then read get_model_schema for the exact endpoint. Similar display names on different providers are not interchangeable endpoint IDs. Inspect pricing for the planned batch, especially video or 3D; use existing task authorization without asking again for already-approved work. Defaults in an old script are not a model choice.

Read MCP calls for parameter examples and recovery. Read prompting and review for concept, reference-edit and material decisions. Read setup when connecting another machine. Each recipient uses their own account and credentials.

Run and retain the job

Inspect the selected input files. Upload only the references needed for the requested task. A hosted MCP cannot read a local file path: use the upload helper or a client-side upload tool, then pass the resulting URL.
Prepare inputs from the live schema. Fields such as image_urls, image_url, aspect_ratio, image_size, resolution and duration differ by endpoint. Do not pass every model the same argument object. Do not add web search, background removal, extra views or video unless they serve this task.
Prefer submit_job for long operations; run_model is convenient for images. Immediately save endpoint, request ID, returned status/result URLs and the input parameters in the task directory. processing is normal, not failure.
Continue with check_job, then get_job_result when complete. Prefer the canonical URLs returned by the service. A wait timeout does not authorize a duplicate submission. A terminal result can still contain a provider error. If submission returned no ID, inspect request history before trying again.
Download the outputs before relying on a CDN link. Inspect each requested asset, preserve rejected takes with their reasons, and make the chosen result easy to open and download. Record technical completion separately from visual acceptance. A generated mesh still needs its own rig/topology/export checks.

Keep the prompt, source filenames or hashes, model ID, request ID, local outputs and review verdict together. Credentials and raw signed links do not belong in a shareable report. In Mr. Mak, use the current Workspace card and its shared image viewer; elsewhere use the recipient's requested output directory.

Included tools and examples

Queue helper: --help, upload, submit, status and result download. One receipt per job; an existing receipt blocks another submission.
Python dependencies.
Concept input and edit input: neutral requests, with no private artwork or working account URLs.
Example task: a non-spending rehearsal followed by the commands for an explicitly requested generation.

Resolve these paths against this skill directory. Run --help or submit --dry-run to check installation without a generation. Runtime keys come from the environment or an explicitly selected .env; never print their values.

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 Fal Ai Generation AI skill do?

Why use Fal Ai Generation on TypingMind?

Because you install it once and use it with any model. Fal Ai Generation 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 Fal Ai Generation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/witnesstodark/mr-mak-workspace/tree/main/.agents/skills/fal-ai-generation. 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 Fal Ai Generation?

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 Fal Ai Generation?

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

Is the Fal Ai Generation AI skill free?

Yes. It is published on GitHub by witnesstodark 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

fal.ai generation

Choose the operation

Run and retain the job

Included tools and examples

Bundled files

Frequently asked questions

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