Runway Dev Models

作者 runwaymle3dffc15498e无许可证72 个星标收录于 2026年10月8日更新于 2026年10月8日仓库5周前更新

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 生成的概览

指导将 Runway 模型生成端点集成到应用中,涵盖模型发现、SDK 调用与界面接入。

功能
该技能提供在应用中构建、修改、调试或验证 Runway 模型生成的指导。内容包括通过 MCP 工具发现可用模型及其输入约束、使用相应等待辅助方法实现 SDK 调用、处理上传和数据 URI 等输入媒体,以及将生成控件和输出状态接入产品界面。它还说明如何用一次测试生成进行验证,并在签名 URL 过期前持久化输出。
适用场景
适用于在应用中添加或调试 Runway 文生视频、文生图或类似生成端点的场景。它面向涉及模型选择、后端 SDK 集成以及生成输入输出界面接入的工作。不适用于 Model Routers、Characters、recipes 或代理端生成脚本。
运行要求
需要保存在服务端的 Runway API 密钥(RUNWAYML_API_SECRET)、Node 或 Python 的 Runway SDK,以及访问当前 Runway 文档的能力。建议使用 MCP 工具列出模型、查询额度余额和检查任务,以获取实时模型访问与约束。API 调用和文档访问需要网络;不附带脚本。

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

来源与署名

来源:runwayml/skills位于skills/runway-dev-models提交e3dffc1

许可证: 无许可证

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