#601

Global Rank · of 601 Skills

byted-airesearch-videoeval AI Agent Skill

View Source: bytedance/agentkit-samples

Critical

Installation

npx skills add bytedance/agentkit-samples --skill byted-airesearch-videoeval

14

Installs

Byted Airesearch Videoeval

Use this skill to submit and query long-running material evaluation tasks.

When to use

Use this skill when the user wants to:

  • evaluate a video material or creative asset
  • submit a material evaluation task and get back a task identifier
  • check the status of an existing evaluation task later
  • fetch the final detail/result of a previously created evaluation task

Do not use this skill for generic video upload requests.

Current workflow

  1. Validate the full input batch before any upload starts.
  2. Upload the local video files and capture the returned attachment_id values.
  3. Create a task with the uploaded attachment IDs.
  4. Return success immediately after the task is created.
  5. Ask the user to query task list or task detail later if they want progress or results.

This workflow is intentionally non-blocking. Do not poll automatically after task creation.

Mandatory behavior

  • Do not expose the upload API as a standalone user-facing capability.
  • Do not trigger this skill for generic requests such as “upload this video”, “store this file”, or “send this video”.
  • Only call the upload API when the user explicitly intends to create a new material evaluation task.
  • For new task creation, prefer scripts/submit_evaluation_task.py so validation, upload, and task creation stay in one controlled flow.
  • Treat scripts/upload_video.py as an internal helper used by the orchestration flow, not as the primary user entrypoint. The script itself rejects direct use unless it is called with the internal orchestration marker.

For multi-file submissions, use the orchestration entrypoint so the whole batch is validated before the first upload starts.

Submission limits

  • A single task can include at most 10 videos.
  • Non-enabled users have a rolling free quota of at most 10 submitted videos within the last 24 hours.
  • The new task's video count is added to the number of videos already submitted in the last 24 hours. If the total exceeds 10, the service rejects the task and asks the user to contact Volcengine sales to enable access.
  • Enabled users are not restricted by this rolling 24-hour free quota.
  • Quota accounting is based on the actual number of videos submitted per task, with no deduplication.
  • Any task created within the last 24 hours counts toward the rolling quota, including running tasks.
  • Login-based access and API key access share the same quota pool.
  • The skill enforces the per-task limit locally before upload starts. The rolling 24-hour quota is enforced by the service, and the skill should surface the service rejection with a clear explanation.

Authentication

The current APIs use API key authentication.

All API requests sent by this skill must include the header:

  • x-product-version: 20
  • Authorization: bearer {API_KEY}

Preferred input methods:

  • --api-key "<api-key>"
  • BYTED_AIRESEARCH_VIDEOEVAL_API_KEY

If no API key is available, ask the user to create or view one at:

  • https://console.volcengine.com/datatester/ai-research/audience/list?tab=apikey

Then ask the user to provide the API key before calling the API.

If the API key is missing, the scripts must fail immediately with a clear error that points the user to the API key page above.

Known API coverage

Upload attachment

  • Endpoint: POST https://console.volcengine.com/datatester/compass/api/v3/survey/attachment
  • Request: multipart/form-data
  • File field: file
  • Upload constraints:
    • each upload request contains exactly one video file
    • each video must be 50MB or smaller
    • file format must be mp4
    • MIME type must be video/mp4
  • Output mapping:
    • preserve the raw attachment payload
    • map the attachment object's id field to attachment_id

The upload_video.py script is an internal helper for the create-task workflow. It is not the primary user-facing entrypoint.

Create task

  • Endpoint: POST https://console.volcengine.com/datatester/compass/api/v3/survey/task
  • Fixed request fields:
    • form_id: 0
    • agent_id: 125
    • audience_id: 3664529
  • Optional request fields currently exposed:
    • prompt
    • language
    • is_typical_user_enabled
    • typical_user_count
    • typical_user_selection_mode
    • is_report_enabled
    • attachment_ids
  • Create constraints:
    • one task submission can include at most 10 videos
    • attachment_ids must contain at most 10 items per request

The create step should send the uploaded attachment_id as a one-element attachment_ids array unless multiple attachment IDs are explicitly provided.

List and detail

Task detail is wired and can query an existing task directly:

  • Endpoint: GET https://console.volcengine.com/datatester/compass/api/v3/survey/task/{id}
  • Current auth: API key bearer token
  • Output mapping:
    • map the task object's id field to task_id
    • map the task object's status field to task_status
    • parse task.detail
    • keep only items where key == video_structured_result and sub_tab != null
    • expose a compact summary block for downstream agent use

Task list is wired and can query existing tasks directly:

  • Endpoint: GET https://console.volcengine.com/datatester/compass/api/v3/survey/task
  • Current auth: API key bearer token
  • Current fixed query params:
    • page=1
    • page_size=100
    • agent_id=125
  • Output mapping:
    • map each task item to task_id, name, status, created_at, updated_at
    • preserve pagination info in data.page

Commands

# Validate a whole batch before upload, then upload all files and create the task
python scripts/submit_evaluation_task.py \
  --file /path/to/video-1.mp4 \
  --file /path/to/video-2.mp4 \
  --api-key "<api-key>"

# Single-video create flow
python scripts/submit_evaluation_task.py \
  --file /path/to/video.mp4 \
  --prompt "Evaluate this material for audience fit and content quality." \
  --api-key "<api-key>"

# Query task list later
python scripts/list_evaluation_tasks.py

# Query task detail later
python scripts/get_evaluation_task_detail.py --task-id 12345

Response handling

All scripts emit JSON to stdout with the same top-level envelope:

  • status
  • message
  • request_id
  • data
  • error

Important normalized fields:

  • submit task: data.task_id, data.task_status, data.submitted_video_count
  • list: data.items
  • detail: data.detail, data.summary

Final answer rules

  • Use the structured JSON returned by the detail endpoint as the internal source of truth.
  • Present the final answer in human-readable natural language.
  • When the task is finished, prefer a concise report-style answer rather than a raw data dump.
  • Do not dump raw JSON to the user.
  • Do not expose internal field names such as video_eval, video_user_report, distribution, field_desc, or similar implementation-oriented keys.
  • Apply the same rule to task list responses: use the list result as internal source data, but present the outcome as a natural-language summary rather than raw fields.
  • For task list answers, it is acceptable to include the task ID, task name, status, created time, and updated time in human-readable prose, because those fields help the user choose a task for follow-up detail queries.
  • When multiple videos are present, summarize them separately.
  • For finished task detail results, prefer a readable report flow such as: task conclusion first, then one short section per video, then overall recommendations if the source data supports them.
  • If the task is not finished yet, do not fabricate a report. Clearly state the current status and ask the user to check again later.
  • Only provide raw structured data if the user explicitly asks for the raw result.

Practical guidance

  • For new submissions, use the orchestration flow rather than exposing upload as a standalone step to the user.
  • Validate the local file before upload. Reject non-MP4 files, files with non-video/mp4 MIME types, or files larger than 50MB with a direct and actionable error message.
  • Validate the task creation input before calling the API. Reject any request that contains more than 10 attachment IDs with a direct and actionable error message.
  • For a multi-video submit flow, validate the full batch size before any upload starts. If the batch contains more than 10 files, fail immediately and do not upload anything.
  • If the service rejects task creation because the rolling 24-hour free quota was exceeded, use this standard Chinese wording for the user-facing message: 免费版用户每24小时最多提交10个评估视频,如需购买请联系火山引擎销售人员
  • After create succeeds, tell the user the task has been submitted successfully and can be checked later.
  • Use task list when the user wants to browse or find historical tasks.
  • Use task detail when the user already knows the task ID and wants the final result.

Installs

Installs 14
Global Rank #601 of 601

Security Audit

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How to use this skill

1

Install byted-airesearch-videoeval by running npx skills add bytedance/agentkit-samples --skill byted-airesearch-videoeval in your project directory. Run the install command above in your project directory. The skill file will be downloaded from GitHub and placed in your project.

2

No configuration needed. Your AI agent (Claude Code, Cursor, Windsurf, etc.) automatically detects installed skills and uses them as context when generating code.

3

The skill enhances your agent's understanding of byted-airesearch-videoeval, helping it follow established patterns, avoid common mistakes, and produce production-ready output.

What you get

Skills are plain-text instruction files — not executable code. They encode expert knowledge about frameworks, languages, or tools that your AI agent reads to improve its output. This means zero runtime overhead, no dependency conflicts, and full transparency: you can read and review every instruction before installing.

Compatibility

This skill works with any AI coding agent that supports the skills.sh format, including Claude Code (Anthropic), Cursor, Windsurf, Cline, Aider, and other tools that read project-level context files. Skills are framework-agnostic at the transport level — the content inside determines which language or framework it applies to.

Data sourced from the skills.sh registry and GitHub. Install counts and security audits are updated regularly.

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