2D Sprite Generator
0x0funky/agent-sprite-forge
Produces game-ready 2D characters, creatures, props, icons and effects as master stills, sheets or clips, and exports frames for common game engines.
Generate and edit images, create video, audio, 3D assets or material maps through fal.ai MCP or the Python queue client.
Install Fal AI Generation
$ npx skills add witnesstodark/mr-mak-workspace --skill fal-ai-generation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install witnesstodark/mr-mak-workspace fal-ai-generation --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/witnesstodark/mr-mak-workspace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/fal-ai-generation .claude/skills/fal-ai-generation && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "fal-ai-generation" agent skill from https://github.com/witnesstodark/mr-mak-workspace/tree/main/.agents/skills/fal-ai-generation into .claude/skills/fal-ai-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fal-ai-generation", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/witnesstodark/mr-mak-workspace/tree/main/.agents/skills/fal-ai-generationType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add witnesstodark/mr-mak-workspace --skill fal-ai-generation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install witnesstodark/mr-mak-workspace fal-ai-generation --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/witnesstodark/mr-mak-workspace.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.agents/skills/fal-ai-generation .agents/skills/fal-ai-generation && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "fal-ai-generation" agent skill from https://github.com/witnesstodark/mr-mak-workspace/tree/main/.agents/skills/fal-ai-generation into .agents/skills/fal-ai-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fal-ai-generation", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add witnesstodark/mr-mak-workspace --skill fal-ai-generation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install witnesstodark/mr-mak-workspace fal-ai-generation --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/witnesstodark/mr-mak-workspace.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.agents/skills/fal-ai-generation .cursor/skills/fal-ai-generation && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "fal-ai-generation" agent skill from https://github.com/witnesstodark/mr-mak-workspace/tree/main/.agents/skills/fal-ai-generation into .cursor/skills/fal-ai-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fal-ai-generation", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/witnesstodark/mr-mak-workspace.git --path .agents/skills/fal-ai-generation--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add witnesstodark/mr-mak-workspace --skill fal-ai-generation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install witnesstodark/mr-mak-workspace fal-ai-generation --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/witnesstodark/mr-mak-workspace.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.agents/skills/fal-ai-generation .gemini/skills/fal-ai-generation && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "fal-ai-generation" agent skill from https://github.com/witnesstodark/mr-mak-workspace/tree/main/.agents/skills/fal-ai-generation into .gemini/skills/fal-ai-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fal-ai-generation", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install witnesstodark/mr-mak-workspace fal-ai-generationInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add witnesstodark/mr-mak-workspace --skill fal-ai-generation -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/witnesstodark/mr-mak-workspace.git skills-src && mkdir -p .github/skills && cp -r skills-src/.agents/skills/fal-ai-generation .github/skills/fal-ai-generation && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "fal-ai-generation" agent skill from https://github.com/witnesstodark/mr-mak-workspace/tree/main/.agents/skills/fal-ai-generation into .github/skills/fal-ai-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fal-ai-generation", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add witnesstodark/mr-mak-workspace --skill fal-ai-generation -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install witnesstodark/mr-mak-workspace fal-ai-generation --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/witnesstodark/mr-mak-workspace.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.agents/skills/fal-ai-generation .opencode/skills/fal-ai-generation && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "fal-ai-generation" agent skill from https://github.com/witnesstodark/mr-mak-workspace/tree/main/.agents/skills/fal-ai-generation into .opencode/skills/fal-ai-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fal-ai-generation", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
fal-ai-generation- GitHub stars
- 347
- Token cost
- ~1k tokens
- SKILL.md length
- 530 words
- Files
- 10 (incl. scripts, references)
- Skills in repo
- 19
- Repo updated
- First seen
- Licence
- MIT
At a glance
Generate and edit images, create video, audio, 3D assets or material maps through fal.ai MCP or the Python queue client.
- Works in 5 steps: Inspect the selected input files. Upload… → Prepare inputs from the live schema.… → Prefer submit_job for long operations;… → …
- Fal model discovery
- SKILL.md covers Choose the operation, Run and retain the job and Included tools and examples
- Runs Python scripts from its folder
What it does
Fal AI Generation is an agent skill from witnesstodark/mr-mak-workspace. 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.
Its SKILL.md is about 1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 12 other files, including scripts and reference files (for example `examples/WALKTHROUGH.md`, `examples/concept.json` and `examples/edit.json`).
It sits in Media & Creative, covering Image editing and Game assets and audio. It works with fal, Model Context Protocol and Python. The repository describes itself as: A local desktop workspace for Codex and Claude Code, with project reports, creative skills and optional voice. The licence is MIT.
When your agent uses it
- Fal model discovery
- Reference uploads
- Recovery and local delivery
Example prompts
- “/fal-ai-generation”
Requirements
- Python 3
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- Inspect the selected input files. Upload only the references needed for the
- Prepare inputs from the live schema. Fields such as image_urls, image_url,
- Prefer submit_job for long operations; run_model is convenient for images.
- Continue with check_job, then get_job_result when complete. Prefer the
- Download the outputs before relying on a CDN link. Inspect each requested
What it can do on your machine
Read from SKILL.md and the folder at commit c7c9fbf. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 3 files in scripts/ (Python), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Fal AI Generation loads about 1k tokens when it runs, and up to ~3.5k if it reads all its reference files. Until then it costs about 57 tokens; SKILL.md has 530 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check: notes
The automated check noted patterns worth knowing about, such as sudo or a known installer.
- NoteMentions a .env fileSKILL.md:69
e environment or an explicitly selected `.env`; never print their values.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from witnesstodark/mr-mak-workspace at commit c7c9fbf, republished under its MIT licence (© witnesstodark). 530 words, ~1,039 tokens.
Download SKILL.mdSave it as
.claude/skills/fal-ai-generation/SKILL.md (or your agent's skills folder). This skill also uses 9 other files; get the full folder from GitHub.- name
- fal-ai-generation
- description
- 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.
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 Windows 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,resolutionand 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_jobfor long operations;run_modelis convenient for images. Immediately save endpoint, request ID, returned status/result URLs and the input parameters in the task directory.processingis normal, not failure. - Continue with
check_job, thenget_job_resultwhen 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.
Show full SKILL.md (136 more words)Show less
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.
© witnesstodark, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 9 other files (scripts, references) in .agents/skills/fal-ai-generation of witnesstodark/mr-mak-workspace.
- SKILL.md
- examples/WALKTHROUGH.md
- examples/concept.json
- examples/edit.json
- references/mcp-workflow.md
- references/prompting-and-review.md
- references/setup.md
- scripts/fal_job.py
- scripts/requirements.txt
- scripts/test_fal_job.py
Open the folder on GitHubat commit c7c9fbf
Compare with similar skills
Fal AI Generation next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Fal AI Generation this skillwitnesstodark/mr-mak-workspace | 347 | — | ~1k | Automated safety check: Notes | MIT | |
| 2D Sprite Generator0x0funky/agent-sprite-forge | 4.4k | — | ~3.6k | Automated safety check: Pass | MIT | |
| Web Visualsglifxyz/glif-mcp-server | 213 | — | ~1.5k | Automated safety check: Pass | MIT | |
| Scenario Texturesscenario-labs/skills | 946 | — | ~2k | Automated safety check: Pass | MIT | |
| Fmodel Unpackpa001024/dna-builder | 137 | — | ~2.6k | Automated safety check: Pass | MIT | |
| Make Imagesglifxyz/glif-mcp-server | 213 | — | ~931 | Automated safety check: Pass | MIT |
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Related
Works with
Categories
Questions about Fal AI Generation
What does Fal AI Generation do?
Generate and edit images, create video, audio, 3D assets or material maps through fal.ai MCP or the Python queue client. Fal AI Generation is an agent skill from witnesstodark/mr-mak-workspace.ai MCP or the Python queue client.
When should I use Fal AI Generation?
Fal AI Generation fits situations like: fal model discovery; reference uploads; recovery and local delivery.
How do I install Fal AI Generation in Claude Code?
Run `npx skills add witnesstodark/mr-mak-workspace --skill fal-ai-generation -a claude-code`. Or copy the skill folder (.agents/skills/fal-ai-generation in witnesstodark/mr-mak-workspace) into .claude/skills/fal-ai-generation in your project. Claude Code loads it when a task matches its description.
How do I install Fal AI Generation in Codex?
Run `npx skills add witnesstodark/mr-mak-workspace --skill fal-ai-generation -a codex`. Or copy the skill folder (.agents/skills/fal-ai-generation in witnesstodark/mr-mak-workspace) into .agents/skills/fal-ai-generation in your project. Codex loads it when a task matches its description.
Can I use Fal AI Generation in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add witnesstodark/mr-mak-workspace --skill fal-ai-generation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/fal-ai-generation, .gemini/skills/fal-ai-generation, .github/skills/fal-ai-generation and .opencode/skills/fal-ai-generation in your project.
What does Fal AI Generation need to run?
Going by SKILL.md and its folder, Fal AI Generation needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Fal AI Generation access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Fal AI Generation safe to install?
Our automated static check of SKILL.md found notes only (mentions a .env file), nothing it rates as a warning. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Fal AI Generation use?
Fal AI Generation is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Fal AI Generation use?
About 1k tokens (SKILL.md is roughly 4.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.5k tokens, read only when the agent opens those files.
What are the alternatives to Fal AI Generation?
Skills that share tags, products or a category with Fal AI Generation: 2D Sprite Generator (0x0funky/agent-sprite-forge, 4.4k stars), Web Visuals (glifxyz/glif-mcp-server, 213 stars), Scenario Textures (scenario-labs/skills, 946 stars) and Fmodel Unpack (pa001024/dna-builder, 137 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Fal AI Generation?
witnesstodark (a GitHub user) maintains it in witnesstodark/mr-mak-workspace, which has 347 GitHub stars. The repository holds 19 skills in this directory. The repository was last updated on October 9, 2026.
Source: witnesstodark/mr-mak-workspace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.