Borrowing it
Nothing to install: this file belongs to yapota2018/affaan-m-everything-claude-code. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/yapota2018/affaan-m-everything-claude-code/main/.agents/skills/fal-ai-media/SKILL.mdgit clone --depth 1 https://github.com/yapota2018/affaan-m-everything-claude-codeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/yapota2018/affaan-m-everything-claude-code/fal-ai-media)<a href="https://agentmods.dev/skills/yapota2018/affaan-m-everything-claude-code/fal-ai-media"><img src="https://agentmods.dev/badge/skills/yapota2018/affaan-m-everything-claude-code/fal-ai-media/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/yapota2018/affaan-m-everything-claude-code/fal-ai-media"><img src="https://agentmods.dev/badge/skills/yapota2018/affaan-m-everything-claude-code/fal-ai-media.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00078 | $0.01850 |
| Opus 5 | $0.00039 | $0.00925 |
| Sonnet 5 | $0.00016 | $0.00370 |
| Haiku 4.5 | $0.00008 | $0.00185 |
Grade B, and why
fal-ai-media scanned grade B with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Sends data to an external URLmediumData exfiltration
A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.
resp = requests.post( "https://api.elevenlabs.io/v1/text-to-speech/<voice_id>", Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
resp = requests.post( This is a copy
98% identical to fal-ai-media — 1 line differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 278 lines — stays where its author put it; the contents beside it link to each section on GitHub.
fal.ai Media Generation
Generate images, videos, and audio using fal.ai models via MCP.
When to Activate
- User wants to generate images from text prompts
- Creating videos from text or images
- Generating speech, music, or sound effects
- Any media generation task
- User says "generate image", "create video", "text to speech", "make a thumbnail", or similar
MCP Requirement
fal.ai MCP server must be configured. Add to ~/.claude.json:
"fal-ai": {
"command": "npx",
"args": ["-y", "fal-ai-mcp-server"],
"env": { "FAL_KEY": "YOUR_FAL_KEY_HERE" }
}
Get an API key at fal.ai.
MCP Tools
The fal.ai MCP provides these tools:
search— Find available models by keywordfind— Get model details and parametersgenerate— Run a model with parametersresult— Check async generation statusstatus— Check job statuscancel— Cancel a running jobestimate_cost— Estimate generation costmodels— List popular modelsupload— Upload files for use as inputs
Image Generation
Nano Banana 2 (Fast)
Best for: quick iterations, drafts, text-to-image, image editing.
generate(
model_name: "fal-ai/nano-banana-2",
input: {
"prompt": "a futuristic cityscape at sunset, cyberpunk style",
"image_size": "landscape_16_9",
"num_images": 1,
"seed": 42
}
)
Nano Banana Pro (High Fidelity)
Best for: production images, realism, typography, detailed prompts.
generate(
model_name: "fal-ai/nano-banana-pro",
input: {
"prompt": "professional product photo of wireless headphones on marble surface, studio lighting",
"image_size": "square",
"num_images": 1,
"guidance_scale": 7.5
}
)
Common Image Parameters
| Param | Type | Options | Notes |
|---|---|---|---|
prompt |
string | required | Describe what you want |
image_size |
string | square, portrait_4_3, landscape_16_9, portrait_16_9, landscape_4_3 |
Aspect ratio |
num_images |
number | 1-4 | How many to generate |
seed |
number | any integer | Reproducibility |
guidance_scale |
number | 1-20 | How closely to follow the prompt (higher = more literal) |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 278 lines · 78 tokens per session scan B 86f110798171
fal-ai-media is a skill published in the GitHub repository yapota2018/affaan-m-everything-claude-code (4 stars, last pushed 5mo ago), licensed MIT. It adds 78 tokens to every session and 1,850 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it B with 2 findings (sends data to an external url, makes network calls). It is 98% identical to fal-ai-media, differing in 1 line, and is treated as a copy.
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