據 AlphaMoat 統計,截至 2026-09,fal 累計下載 3,145 次、獲 1 個星標,在全部 5,078 個 AI 技能中排名第 3,624 位,在「設計創意」分類(512 個)中排名第 338 位。
| 平台 | 累計安裝 | 累計下載 | 星標 | 評論 |
|---|---|---|---|---|
| ClawHub ↗ | 98 | 3060 | 1 | - |
| SkillHub ↗ | 209 | 3145 | 1 | — |
各平台的計數口徑不同,統計時間不一致。
可在 fal.ai 上搜尋、檢視並執行生成式 AI 模型,涵蓋圖像、影片、音訊等任務。
資料釋出於 2026-10-27
以下是根據技能文件生成的示例,不代表實際執行結果。
2026-09 起統計方法調整:同名技能按作者分別統計,並覆蓋全部技能。圖中虛線為調整前的月份,與之後的資料不直接比較,也不計算跨越調整的增長。
| 月份 | 累計下載 | 環比 | 星標 |
|---|---|---|---|
| 2026-09 | 3145 | — | 1 |
| 2026-08 | 2966 | +4.3% | 1 |
| 2026-07 | 2845 | +9.5% | 1 |
| 2026-06 | 2599 | +4.4% | 1 |
| 2026-05 | 2489 | — | 1 |
| 2026-03 | 2075 | — | 1 |
以下是作者釋出的原始檔案,按純文字顯示,本站未做修改或稽核;其中的連結不可點選,命令請核實後再執行。抓取於 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 架構圖一鍵生成
用 Nano Banana Pro 生成或編輯圖片的指令碼技能。
用 whisper 命令列工具在本機將音訊轉成文字或進行翻譯。
透過 MCP 服務生成與編輯圖表的技能。
面向介面設計的思路與落地建議技能,覆蓋佈局、互動流程、設計令牌與前端改動。
面向多家影片模型 API 的 AI 影片生成與編排技能。
用 ffmpeg 從影片裡取出畫面幀,或生成便於快速檢視的縮圖。
用本地 Whisper 把音訊檔案轉成文字,模型下載完成後可完全離線執行。
資料月份:2026-09 · 下載/星標/安裝數聚合自公開技能註冊庫(ClawHub、SkillHub),按月更新。口徑說明見資料方法。