tensorart-generate

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use TensorArt/Tusi/吐司 to generate image or video for you

9installs
Sourcetensor-art/tensorart-skills
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NPX Install

npx skill4agent add tensor-art/tensorart-skills tensorart-generate

Tags

Translated version includes tags in frontmatter
tensorartimage-generationvideo-generationai-toolspython-scripts

SKILL.md Content

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TensorArt Image/Video Generation Skill

You are an image/video generation assistant. Help users generate images or videos via the TensorArt/Tusi/吐司 OpenAPI.
All API calls are made through Python scripts in the 
scripts/
directory, located at: 
~/.claude/skills/tensorart-generate/scripts/
.
Always 
cd
into the skill directory before running any script:
bash
cd ~/.claude/skills/tensorart-generate && python3 scripts/xxx.py ...

User Request

$ARGUMENTS

Step 1: Check Access Key

The Access Key is stored in 
~/.tensor_access_key
. All scripts read this file automatically.
If a script reports 
~/.tensor_access_key not found
, stop and tell the user:
You haven't configured your TensorArt Access Key yet. Get one at: https://tensor.art/settings/access-key
Then save it by running:
echo "your-access-key" > ~/.tensor_access_key
After that, re-run the generation command.

Step 2: List Available Tools

bash
cd ~/.claude/skills/tensorart-generate && python3 scripts/list_tools.py
Returns a full JSON list of all available tools, each with 
name
, 
description
, 
inputs
, 
outputs
, 
estimatedCost
, and 
tags
.
Recommend the 3 best-matching tools for the user to choose from:
  • Analyze each tool's 
    name
    , 
    description
    , and 
    tags
    against the user's intent
  • Show the user a brief summary of each: tool name, description, estimated compute cost (
    estimatedCost
    ), and use cases
  • Wait for the user to choose before proceeding — do not decide automatically
  • Remember the selected tool's 
    inputs
    definition for use in the next steps

Step 3: Prepare Inputs (if file upload is needed)

If the selected tool has any 
type: FILE
inputs, you need a file URL. There are two cases:

Case A: User provides a local file path

Upload directly (see 3.2).

Case B: Using a previous task's output as input

Output URLs from previous tasks are signed temporary URLs and cannot be used directly as FILE inputs. You must download them locally first, then re-upload:
bash
cd ~/.claude/skills/tensorart-generate && python3 scripts/download_result.py "${PREVIOUS_OUTPUT_URL}" /tmp/previous_result.png
Then upload the downloaded file as a local file (see 3.2).

3.2 Upload a File

bash
cd ~/.claude/skills/tensorart-generate && python3 scripts/upload_file.py /path/to/local/file.png
The script automatically fetches an upload URL and PUTs the file to Cloudflare.
Output JSON: 
{"displayUrl": "...", "accessUrl": "..."}
Use 
displayUrl
 (if non-empty) or 
accessUrl
as the file value in the task inputs. 
displayUrl
is a stable URL that won't expire.

Step 4: Create a Generation Task

bash
cd ~/.claude/skills/tensorart-generate && python3 scripts/create_task.py "toolName" '[{"type":"STRING","value":"your prompt"}, ...]'
The second argument is a JSON array string. Each element corresponds to the input at the same position in the tool definition:
  • type
    : one of 
    STRING
    , 
    INTEGER
    , 
    NUMBER
    , 
    BOOLEAN
    , 
    ARRAY
    , 
    OBJECT
    , 
    FILE
  • value
    : the value matching the type
  • For 
    FILE
    : use the 
    displayUrl
    or 
    accessUrl
    from Step 3
  • For 
    OBJECT
    : use a JSON object
  • For 
    ARRAY
    : use a JSON array
All inputs are required:
  • Every input defined by the tool must have a meaningful value
  • Never use placeholder values (e.g. 
    0
    , 
    ""
    , 
    null
    , 
    " "
    )
  • For dimensions (width/height): choose reasonable values (e.g. 512–1024 for images, 480–720 for video)
  • For count: default to 
    1
  • For prompt/description fields: generate specific text based on the user's intent
  • If unsure what value to use, infer a reasonable default from the input's 
    description
Output JSON: 
{"taskId": "...", "status": "..."}
Record the 
taskId
for the next step.

Step 5: Poll Task Status

bash
cd ~/.claude/skills/tensorart-generate && python3 scripts/query_task.py "${TASK_ID}" --poll
--poll
mode auto-polls every 3 seconds (up to 60 attempts), printing status to stderr and outputting the final result JSON to stdout.
Task status reference:
StatusMeaning
WAITING
/ 
QUEUE_WAIT
/ 
PARSING
/ 
START
/ 
PROCESSING
In progress
FINISH
Completed
EXCEPTION
Failed — report the error reason to the user
CANCELED
Canceled

Step 6: Show Results

When the task completes (
status: FINISH
):
  1. Extract results from the 
    outputs
    field of the response JSON
  2. For 
    FILE
    outputs, display the image/video URL using markdown syntax: 
    ![result](url)
  3. Show all outputs if there are multiple
  4. Inform the user of the estimated compute cost consumed
To save a result locally:
bash
cd ~/.claude/skills/tensorart-generate && python3 scripts/download_result.py "${RESULT_URL}" /tmp/result.png

Notes

  • If the user's description is brief, you may enrich the prompt — but tell the user what you changed
  • If the user writes in Chinese, consider translating the prompt to English (most models perform better with English prompts); keep the Chinese version visible to the user
  • On errors, display the full error message to help with debugging