Runway Dev Models

作者 runwaymle3dffc15498e無授權條款收錄於 2026年10月8日更新於 2026年10月8日

Build, modify, debug, or verify Runway model generation in an application: discover accessible models and constraints with MCP, implement SDK calls, and wire inputs and outputs into the product UI. Use with +runway-dev. Not for Model Routers, Characters, recipes, or agent-side generate scripts.

AI 產生的概覽

指導將 Runway 模型生成端點整合到應用程式,涵蓋模型探索、SDK 呼叫與介面串接。

功能
此技能提供在應用程式中建置、修改、偵錯或驗證 Runway 模型生成的指引。內容涵蓋透過 MCP 工具探索可用模型及其輸入限制、使用對應的等待輔助方法實作 SDK 呼叫、處理上傳與資料 URI 等輸入媒體,以及將生成控制項與輸出狀態串接到產品介面。它也說明如何以單次測試生成進行驗證,並在簽章 URL 過期前保存輸出。
適用情境
適用於在應用程式中新增或偵錯 Runway 文字轉影片、文字轉圖片或類似生成端點的情況。它針對涉及模型選擇、後端 SDK 整合,以及生成輸入輸出介面串接的工作。不適用於 Model Routers、Characters、recipes 或代理端生成指令碼。
執行需求
需要在伺服器端保存的 Runway API 金鑰(RUNWAYML_API_SECRET)、Node 或 Python 的 Runway SDK,以及存取目前 Runway 文件的能力。建議使用 MCP 工具列出模型、查詢額度餘額與檢查任務,以取得即時模型存取與限制。API 呼叫與文件存取需要網路;不隨附指令碼。

Runway Dev — Models

Companion: Use +runway-dev for shared guidance when available. If it is not installed, inspect the workspace, probe RUNWAYML_API_SECRET without printing it, and read current docs. Encourage connecting Dev MCP for live model access and constraints. If the user declines or cannot connect, continue from current docs and existing model configuration.

Goal

Help the user choose and integrate a model endpoint (/v1/text_to_video, /v1/text_to_image, etc.) across their application, including its backend call and existing UI. Verify changes with one working SDK call when safe.

MCP tools

  • list_models — discover models the selected project can call and read each model's inputConstraints.
  • get_credit_balance — check budget before billable verification.
  • get_task — inspect or debug an existing task, not replace SDK wait helpers in application code.

Workflow

  1. Inspect the existing application. Confirm the user experience, target modality, and where generation inputs and outputs belong.
  2. If the task requires model selection, access verification, or current constraints, call list_models with { projectId, endpoint }. If existing code pins a model, proceed from current docs unless live access must be verified.
  3. Follow the endpoint docs linked by llms.txt; do not infer request fields from another model.
  4. Keep RUNWAYML_API_SECRET behind the application's server boundary.
  5. Implement or update the SDK call by chaining .waitForTaskOutput() in Node or .wait_for_task_output() in Python directly from the create call.
  6. If the application has a UI, wire its controls to the backend and render loading, error, and generated-output states.
  7. When verification is appropriate, submit one test generation. Present the result and offer to persist output before its signed URL expires.

Input media

  • Follow the current input docs linked by llms.txt: use a public HTTPS URL, a small data URI, or an ephemeral upload.
  • Send browser-selected files to the application's server, then use the SDK upload helper and pass its runway:// URI to generation. Local filesystem paths cannot be API inputs.
  • Do not accept arbitrary remote URLs from clients. Prefer uploads or allowlisted origins.
  • Ephemeral inputs and generated output URLs expire; persist anything the application must retain.

Do not

  • Guess model ids, ratios, or durations from memory or other models.
  • Put API keys in frontend bundles.
  • Add manual polling when the SDK wait helper fits, or resubmit on transient read errors.

Docs

來源與署名

來源:runwayml/skills位於skills/runway-dev-models提交e3dffc1

授權條款: 無授權條款

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