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Nexrender Cloud exposes a set of tools that work well together for AI-driven video personalization. Rather than requiring hardcoded layer names and asset mappings, agents can discover template structure at runtime, generate assets dynamically via AI providers, and submit jobs at scale - all through the same REST API used for standard automation.

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The Nexrender Skill

The Nexrender skill packages API reference into a format that Claude and other LLM agents can load at runtime. It covers job payload structure, asset types, output settings, codec identifiers, secret references, font resolution, and common failure patterns - so an agent has correct, grounded knowledge of the API without relying on general training data. Load the skill into your Claude/Codex-based agent or MCP environment before asking it to construct job payloads or introspect templates. The skill works alongside live API responses: the agent brings API knowledge, the API brings runtime template data.

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Layer and Composition Analytics

The /v3/templates/{id}/compositions and /v3/templates/{id}/layers endpoints expose the After Effects structure of a template as machine-readable data. Agents can use this to work generically against any template rather than relying on prior knowledge of its layer names or structure. Compositions (GET /api/v3/templates/{id}/compositions) return: Layers (GET /api/v3/templates/{id}/layers) return: Both endpoints support limit (default 300, max 1000) and offset for pagination. See Template Introspection for full response shapes and status polling.

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AI Content Generation

The nx:genai function calls an external AI media provider at render time and injects the generated asset into a specified layer before After Effects composites the frame. As of now, the only supported provider is fal.ai - please reach out to support, so we could prioritize adding new providers.
The data object is passed directly to the fal.ai model. Its shape depends on the model - refer to the fal.ai model library for available models and their input schemas. The type field controls how the result is injected: image replaces the layer’s footage source; video does the same and can be followed by nx:layer-duration-set to match the composition duration to the generated clip length. Store your FAL_KEY as a Nexrender secret (see below) and reference it with ${secret.FAL_KEY} rather than embedding the value in the payload. See Secrets Management for the full API. See nx:genai for the full parameter reference.

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Webhooks

Add a webhook.url to any job or batch payload and Nexrender will POST the completed job object to your endpoint when it reaches a terminal state (finished or error). This is the preferred pattern for agent workflows - it avoids polling and lets the agent react immediately to render completion.
Your endpoint must return a 2xx response. Nexrender retries up to 3 times with exponential backoff on non-2xx responses or connection failures. See Tracking Renders for webhook payload shape and polling as a fallback.

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Batch Jobs

POST /api/v2/batches accepts up to 1,000 job payloads in a single request. Each job in the batch is a standard job payload - the same template, assets, settings, and webhook fields. Nexrender creates all jobs atomically and returns aggregate progress via GET /api/v2/batches/{id}. Batches support partial success: failed jobs appear in an errors array while successful ones proceed. This makes batches suitable for large personalisation runs where individual record failures should not block the rest. See Batch Jobs for the full request and response schema.

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Getting Started

  1. Install the skill from github.com/nexrender/nexrender-skill into your Claude or MCP environment
  2. Store credentials - add your Nexrender API key and any provider keys (e.g. FAL_KEY) as secrets
  3. Upload a template and confirm its status is uploaded
  4. Inspect the template using the compositions and layers endpoints to discover its structure
  5. Submit a job using layer names and composition names from the introspection response