据 AlphaMoat 统计,截至 2026-09,fal 累计下载 3,145 次、获 1 个星标,在全部 5,078 个 AI 技能中排名第 3,624 位,在「设计创意」分类(512 个)中排名第 338 位。
| 平台 | 累计安装 | 累计下载 | 星标 | 评论 |
|---|---|---|---|---|
| ClawHub ↗ | 98 | 3,060 | 1 | - |
| SkillHub ↗ | 209 | 3,145 | 1 | — |
各平台的计数口径不同,统计时间不一致。
可在 fal.ai 上搜索、查看并运行生成式 AI 模型,涵盖图像、视频、音频等任务。
资料发布于 2026-10-27
以下是根据技能文档生成的示例,不代表实际运行结果。
2026-09 起统计方法调整:同名技能按作者分别统计,并覆盖全部技能。图中虚线为调整前的月份,与之后的数据不直接比较,也不计算跨越调整的增长。
| 月份 | 累计下载 | 环比 | 星标 |
|---|---|---|---|
| 2026-09 | 3,145 | — | 1 |
| 2026-08 | 2,966 | +4.3% | 1 |
| 2026-07 | 2,845 | +9.5% | 1 |
| 2026-06 | 2,599 | +4.4% | 1 |
| 2026-05 | 2,489 | — | 1 |
| 2026-03 | 2,075 | — | 1 |
以下是作者发布的原始文件,按纯文本显示,本站未做修改或审核;其中的链接不可点击,命令请核实后再运行。抓取于 2026-10-23。
--- name: fal version: 1.0.1 description: Search, explore, and run fal.ai generative AI models (image generation, video, audio, 3D). Use when user wants to generate images, videos, or other media with AI models. allowed-tools: Bash(curl *), Bash(jq *), Bash(mkdir *), Read, Write argument-hint: "<command> [model_id] [--param value]" --- # fal.ai Model API Skill
Run 1000+ generative AI models on fal.ai.
## Arguments
- **Command:** `$0` (search | schema | run | status | result | upload)
- **Arg 1:** `$1` (model_id, search query, or file path)
- **Arg 2+:** `$2`, `$3`, etc. (additional parameters)
- **All args:** `$ARGUMENTS`
## Session Output
Save generated files to session folder:
```bash
mkdir -p ~/.fal/sessions/${CLAUDE_SESSION_ID}
```
Downloaded images/videos go to: `~/.fal/sessions/${CLAUDE_SESSION_ID}/`
---
## Authentication
Requires `FAL_KEY` environment variable. If requests fail with 401, tell user:
```
Get an API key from https://fal.ai/dashboard/keys
Then: export FAL_KEY="your-key-here"
```
---
## Command: `$0`
### If $0 = "search"
Search for models matching `$1`:
```bash
curl -s "https://api.fal.ai/v1/models?q=$1&limit=15" \
-H "Authorization: Key $FAL_KEY" | jq -r '.models[] | "• \(.endpoint_id) — \(.metadata.display_name) [\(.metadata.category)]"'
```
For category search, use:
```bash
curl -s "https://api.fal.ai/v1/models?category=$1&limit=15" \
-H "Authorization: Key $FAL_KEY" | jq -r '.models[] | "• \(.endpoint_id) — \(.metadata.display_name)"'
```
Categories: `text-to-image`, `image-to-video`, `text-to-video`, `image-to-3d`, `training`, `speech-to-text`, `text-to-speech`
---
### If $0 = "schema"
Get input schema for model `$1`:
```bash
curl -s "https://api.fal.ai/v1/models?endpoint_id=$1&expand=openapi-3.0" \
-H "Authorization: Key $FAL_KEY" | jq '.models[0].openapi.components.schemas.Input.properties'
```
Show required vs optional fields to help user understand what inputs are needed.
---
### If $0 = "run"
Run model `$1` with parameters from remaining arguments.
**Step 1: Parse parameters**
Extract `--key value` pairs from `$ARGUMENTS` after the model_id to build JSON payload.
Example: `/fal run fal-ai/flux-2 --prompt "a cat" --image_size landscape_16_9`
→ Model: `fal-ai/flux-2`
→ Payload: `{"prompt": "a cat", "image_size": "landscape_16_9"}`
**Step 2: Submit to queue**
```bash
curl -s -X POST "https://queue.fal.run/$1" \
-H "Authorization: Key $FAL_KEY" \
-H "Content-Type: application/json" \
-d '<JSON_PAYLOAD>'
```
**Step 3: Poll until complete**
```bash
# Get request_id from response, then poll:
while true; do
STATUS=$(curl -s "https://queue.fal.run/$1/requests/$REQUEST_ID/status" \
-H "Authorization: Key $FAL_KEY" | jq -r '.status')
echo "Status: $STATUS"
if [ "$STATUS" = "COMPLETED" ]; then break; fi
if [ "$STATUS" = "FAILED" ]; then echo "Job failed"; break; fi
sleep 3
done
```
**Step 4: Get result and save**
```bash
# Fetch result
RESULT=$(curl -s "https://queue.fal.run/$1/requests/$REQUEST_ID" \
-H "Authorization: Key $FAL_KEY")
# Create session output folder
mkdir -p ~/.fal/sessions/${CLAUDE_SESSION_ID}
# Download images/videos
# For images: jq -r '.images[0].url' and curl to download
# Save as: ~/.fal/sessions/${CLAUDE_SESSION_ID}/<timestamp>_<model>.png
```
---
### If $0 = "status"
Check status of request `$2` for model `$1`:
```bash
curl -s "https://queue.fal.run/$1/requests/$2/status?logs=1" \
-H "Authorization: Key $FAL_KEY" | jq '{status: .status, queue_position: .queue_position, logs: .logs}'
```
---
### If $0 = "result"
Get result of completed request `$2` for model `$1`:
```bash
curl -s "https://queue.fal.run/$1/requests/$2" \
-H "Authorization: Key $FAL_KEY" | jq '.'
```
---
### If $0 = "upload"
Upload file `$1` to fal CDN:
```bash
curl -s -X POST "https://fal.run/fal-ai/storage/upload" \
-H "Authorization: Key $FAL_KEY" \
-F "file=@$1"
```
Returns URL to use in model requests.
---
## Quick Reference
**Popular models:**
- `fal-ai/flux-2` — Fast text-to-image
- `fal-ai/flux-2-pro` — High quality text-to-image
- `fal-ai/kling-video/v2/image-to-video` — Image to video
- `fal-ai/minimax/video-01/image-to-video` — Image to video
- `fal-ai/whisper` — Speech to text
**Common parameters for text-to-image:**
- `--prompt "description"` — What to generate
- `--image_size landscape_16_9` — Aspect ratio (square, portrait_4_3, landscape_16_9)
- `--num_images 1` — Number of images
**Example invocations:**
- `/fal search video` — Find video models
- `/fal schema fal-ai/flux-2` — See input options
- `/fal run fal-ai/flux-2 --prompt "a sunset over mountains"`
- `/fal status fal-ai/flux-2 abc-123`
- `/fal upload ./photo.png`
ContextWeave 架构图一键生成
用 Nano Banana Pro 生成或编辑图片的脚本技能。
用 whisper 命令行在本地把音频转写成文字或做翻译的技能。
通过 MCP 服务生成与编辑图表的技能。
面向界面设计的思路与落地建议技能,覆盖布局、交互流程、设计令牌与前端改动。
面向多家视频模型 API 的 AI 视频生成与编排技能。
用 ffmpeg 从视频里取出画面帧,或生成便于快速查看的缩略图。
用本地 Whisper 把音频文件转成文字,模型下载完成后可完全离线运行。
数据月份:2026-09 · 下载/星标/安装数聚合自公开技能注册库(ClawHub、SkillHub),按月更新。口径说明见数据方法。