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phosor-ai磷艾

Agent Skill

phosor-ai 用于处理图像、截图、视觉识别或图片素材相关工作,适合在 OpenClaw 中需要让 Agent 分析图片、整理视觉素材或辅助图像流程时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:phosor-ai(磷艾)
来源仓库:https://github.com/jasonstarlight/phosor-ai
安装命令:
openclaw skills install phosor-ai
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install phosor-ai

简介

phosor-ai 基于 Phosor AI 平台生成文本或图像驱动的 AI 视频。

  • 支持自定义 LoRA 模型导入以控制视觉风格。
  • 适用于短视频制作与动态内容生成任务。
  • 需上传参考图像或指定文本提示词。phosor-ai 属于效率类 Skill,可作为该场景下的辅助能力补充。
  • 使用前请确认视频分辨率与时长是否符合平台输出限制。

SKILL.md

name
phosor-ai
version
1.0.0
description
Generate AI videos (text-to-video, image-to-video) with optional custom LoRA styles via the Phosor AI platform. Supports importing images and LoRA models from external URLs. Use when the user wants to create videos from text prompts, animate images, or apply custom LoRA models.
license
MIT-0
metadata
author
phosor
openclaw
requires
env
[PHOSOR_API_KEY, PHOSOR_BASE_URL]
bins
[python3]
config
[phosor-pending.json]
primaryEnv
PHOSOR_API_KEY
homepage
https://phosor.ai

Phosor AI

Generate AI videos (text-to-video, image-to-video) with optional custom LoRA styles via the Phosor AI platform.

For detailed API endpoints, parameters, pricing, and limits, see references/api.md.

Setup

  1. Sign up or log in at phosor.ai
  2. Go to Settings → API Keys to generate your API key
  3. Set it as an environment variable:
export PHOSOR_API_KEY="your-api-key-here"
Keep your API key secret. Do not commit it to version control or share it publicly. All API calls are authenticated and billed through this key.

The CLI script is at scripts/phosor_client.py. All commands output JSON to stdout.

Local files used by the CLI:

  • Writes phosor-pending.json to track pending job states locally

Quick Start

Text-to-Video

# Submit T2V job (480p, 81 frames, 16fps)
python3 scripts/phosor_client.py submit "A cat walking on a beach at sunset" \
  --width 854 --height 480 --num-frames 81 --fps 16

# Check status
python3 scripts/phosor_client.py status <request_id>

# Get result (video URL)
python3 scripts/phosor_client.py result <request_id>

Image-to-Video

Two-step flow: upload image first, then submit with the returned S3 key.

# Step 1: Upload image
python3 scripts/phosor_client.py upload-image /path/to/photo.jpg
# Returns: {"file_id": "img-xxx", "s3_key": "images/img-xxx.jpg", ...}

# Step 2: Submit I2V job using the s3_key as image_url
python3 scripts/phosor_client.py submit "The person in the photo starts dancing" \
  --image-url "images/img-xxx.jpg" --width 854 --height 480

With Custom LoRA

Upload your own LoRA model, then use it in video generation.

# Upload LoRA (two .safetensors: high_noise + low_noise)
python3 scripts/phosor_client.py upload-lora high_noise.safetensors low_noise.safetensors --name "My Style"

# Check upload status
python3 scripts/phosor_client.py lora-status <lora_id>

# Use in inference
python3 scripts/phosor_client.py submit "A person walking" --lora-id <lora_id> --lora-scale 1.0

# Or import LoRA from URLs
python3 scripts/phosor_client.py import-lora \
  "https://example.com/high_noise.safetensors" \
  "https://example.com/low_noise.safetensors" \
  --name "My Style"

CLI Commands

Job Management

  • submit <prompt> — Submit inference job (T2V/I2V). Options: --width, --height, --num-frames, --fps, --steps, --guidance, --image-url, --lora-id, --lora-scale, --seed, --negative-prompt, --model
  • status <request_id> — Get job status
  • result <request_id> — Get job result (video URL)
  • poll — Poll all pending jobs
  • list — List locally tracked pending jobs
  • history — Get job history. Options: --limit

File Upload

  • upload-image <file> — Upload image for I2V
  • import-image <url> — Import image from URL. Options: --filename
  • upload-lora <high_noise_file> <low_noise_file> — Upload LoRA (two .safetensors). Options: --name
  • import-lora <high_noise_url> <low_noise_url> — Import LoRA from URLs. Options: --name

LoRA Management

  • loras — List LoRA models. Options: --limit, --offset
  • lora-status <lora_id> — Get LoRA upload status
  • delete-lora <lora_id> — Delete a LoRA model

Utilities

  • check-key — Validate API key
  • models — List available models
  • quotas — Get quota usage/limits

Key Constraints

Resolutions (exact pairs only)

  • 480p landscape — 854 × 480, max 161 frames
  • 480p portrait — 480 × 854, max 161 frames
  • 720p landscape — 1280 × 720, max 161 frames
  • 720p portrait — 720 × 1280, max 161 frames
  • 1080p landscape — 1920 × 1080, max 153 frames
  • 1080p portrait — 1080 × 1920, max 153 frames

Frame Alignment

Frames must follow 1 + 4*k where k >= 1 (e.g. 5, 9, 13, ... 81, 85, ...). Server auto-aligns down.

Inference Parameters

  • frames_per_second — default: 16, range: 4–30
  • num_inference_steps — default: 4, range: 4
  • guidance_scale — default: 1.0, range: 1.0–3.5
  • lora_scale — default: 1.0, range: 0.0–2.0

Two-Step Upload Rule

Files must be uploaded before use:

  1. Imageupload-image / import-image → returns s3_key → use as --image-url
  2. LoRAupload-lora / import-lora → returns lora_id → use as --lora-id

Queue Flow

PENDING → PROCESSING → COMPLETED / FAILED

The poll command checks all locally-tracked pending jobs and removes completed/failed ones.

Pricing

  • 480p — $0.0002 per frame (0.002 credits)
  • 720p — $0.0006 per frame (0.006 credits)
  • 1080p — $0.0012 per frame (0.012 credits)
  • LoRA multiplier — 1.25x

Exchange rate: 10 credits = $1 USD. Credits pre-deducted, auto-refunded on failure.

Version History

1.0.0 (2026-03-21)

  • Initial release
  • Text-to-video and image-to-video generation (Wan 2.2 14B)
  • Custom LoRA support (upload, import, use in inference)
  • 16 CLI commands
  • Model registry with GET /api/v1/models endpoint
  • Supported models: wan/2.2-14b/text-to-video, wan/2.2-14b/image-to-video

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能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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