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Sora

JetBrainsOfficialCreated by OpenAIDistributed by JetBrains

  • Skill
  • Content generator
  • Design and creative
  • Apache-2.0
  • Low risk
  • Has scripts

From the author

Use when the user asks to generate, edit, extend, poll, list, download, or delete Sora videos, create reusable non-human Sora character references, or run local multi-video queues via the bundled CLI (`scripts/sora.py`); includes requests like: (i) generate AI video, (ii) edit this Sora clip, (iii) extend this video, (iv) create a character reference, (v) download video/thumbnail/spritesheet, and (vi) Sora batch planning; requires `OPENAI_API_KEY` and Sora API access.
— the description by OpenAI

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The Sora skill provides an interface to interact with Sora video models for creating, editing, and extending video content. It includes a bundled CLI for managing video jobs, character references, and asset downloads. The skill supports structured prompt augmentation to help users define scene details, camera movements, and lighting, and requires an OpenAI API key for operation.

What it does

  • Generates new video clips from text prompts
  • Creates reusable non-human character references from short video clips
  • Edits existing generated videos using targeted prompts
  • Extends the duration of completed video clips
  • Manages video job queues and polls for status updates
  • Downloads generated assets including videos, thumbnails, and spritesheets

When to use it

  • When you need to generate cinematic or social video content
  • When you want to maintain character consistency across multiple shots
  • When you need to modify or continue an existing Sora-generated video
  • When you need to manage multiple video generation tasks via a local queue

Advantages

  • Provides a structured workflow for prompt engineering and video generation
  • Enables character consistency through dedicated reference uploads
  • Supports iterative editing and extension of video clips
  • Includes a CLI for deterministic job management and batch processing

Usage examples

“Generate a 4-second video of a matte black camera on a pedestal with a slow orbit.”
The agent uses the CLI to trigger a Sora generation job with the specified prompt, camera movement, and duration.
“Create a character reference from this video file named Mossy.”
The agent uploads the provided video file to create a reusable character ID for future generations.
“Extend this video by 8 seconds showing the camera rising above the rooftops.”
The agent uses the extend command with the video ID to generate a continuation of the specified clip.
“Edit the video video_abc123 to change the color palette to teal and rust.”
The agent submits an edit request for the specified video ID, applying the requested color changes while preserving the original shot.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/jetbrains/sora/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip sora.zip -d .agents/skills

Documentation by OpenAI

Sora Video Generation Skill

Creates or manages Sora video jobs for the current project (product demos, marketing spots, cinematic shots, social clips, UI mocks). Defaults to sora-2 with structured prompt augmentation and prefers the bundled CLI for deterministic runs. Note: $sora is a skill tag in prompts, not a shell command.

When to use

  • Generate a new video clip from a prompt

  • Create a reusable character reference from a short non-human source clip

  • Edit an existing generated video with a targeted prompt change

  • Extend a completed video with a continuation prompt

  • Poll status, list jobs, or download assets (video/thumbnail/spritesheet)

  • Run a local multi-job queue now, or plan a true Batch API submission for offline rendering

Decision tree

  • If the user has a short non-human reference clip they want to reuse across shots → create-character

  • If the user has a completed video and wants the next beat/continuation → extend

  • If the user has a completed video and wants a targeted change while preserving the shot → edit

  • If the user has a video id and wants status or assets → status, poll, or download

  • If the user needs many renders immediately inside Codex → create-batch (local fan-out, not the Batch API)

  • If the user needs many renders for offline processing or a studio pipeline → use the official Batch API flow described in references/video-api.md

  • Otherwise → create (or create-and-poll if they need a ready asset in one step)

Workflow

  1. Decide intent: create vs create-character vs edit vs extend vs status/download vs local queue vs official Batch API.

  2. Collect inputs: prompt, model, size, seconds, any image reference, and any character IDs.

  3. Prefer CLI augmentation flags (--use-case, --scene, --camera, etc.) instead of hand-writing a long structured prompt. If you already have a structured prompt file, pass --no-augment.

  4. Run the bundled CLI (scripts/sora.py) with sensible defaults. For long prompts, prefer --prompt-file to avoid shell-escaping issues.

  5. For async jobs, poll until terminal status (or use create-and-poll).

  6. Download assets (video/thumbnail/spritesheet) and save them locally before URLs expire.

  7. If the user wants continuity across many shots, create character assets first, then reference them in later create calls.

  8. If the user wants to iterate on a completed shot, prefer edit; if they want the shot to continue in time, prefer extend.

  9. Use one targeted change per iteration.

Authentication

  • OPENAI_API_KEY must be set for live API calls.

If the key is missing, give the user these steps:

  1. Create an API key in the OpenAI platform UI: https://platform.openai.com/api-keys

  2. Set OPENAI_API_KEY as an environment variable in their system.

  3. Offer to guide them through setting the environment variable for their OS/shell if needed.

  • Never ask the user to paste the full key in chat. Ask them to set it locally and confirm when ready.

Defaults & rules

  • Default model: sora-2 (use sora-2-pro for higher fidelity).

  • Default size: 1280x720.

  • Default seconds: 4 (allowed: "4", "8", "12", "16", "20").

  • Always set size and seconds via API params; prose will not change them.

  • sora-2-pro is required for 1920x1080 and 1080x1920.

  • Use up to two characters per generation.

  • Use the OpenAI Python SDK (openai package). If high-level SDK helpers lag the latest Sora guide, use low-level client.post/get/delete inside the official SDK rather than standalone HTTP code.

  • Require OPENAI_API_KEY before any live API call.

  • If uv cache permissions fail, set UV_CACHE_DIR=/tmp/uv-cache.

  • Input reference images must be jpg/png/webp and should match target size.

  • JSON input_reference objects use either file_id or image_url; uploaded file paths use multipart.

  • Download URLs expire after about 1 hour; copy assets to your own storage.

  • Batch-generated videos remain downloadable for up to 24 hours after the batch completes.

  • create-batch in scripts/sora.py is a local concurrent queue, not the official Batch API.

  • Prefer the bundled CLI and never modify scripts/sora.py unless the user asks.

  • Sora can generate audio; if a user requests voiceover/audio, specify it explicitly in the Audio: and Dialogue: lines and keep it short.

API limitations

  • Models are limited to sora-2 and sora-2-pro.

  • API access to Sora models requires an organization-verified account.

  • Duration must be set via the seconds parameter and currently supports 4, 8, 12, 16, and 20.

  • Character uploads currently work best with short 2-4 second non-human MP4s in 16:9 or 9:16, at 720p-1080p.

  • Extensions can add up to 20 seconds each, up to six times per source video, for a maximum total length of 120 seconds.

  • Extensions currently do not support characters or image references.

  • This skill supports editing existing generated videos by ID.

  • The official Batch API currently supports POST /v1/videos only, with JSON bodies rather than multipart uploads.

  • Output sizes are limited by model (see references/video-api.md for the supported sizes).

  • Video creation is async; you must poll for completion before downloading.

  • Rate limits apply by usage tier (do not list specific limits).

  • Content restrictions are enforced by the API (see Guardrails below).

Guardrails (must enforce)

  • Only content suitable for audiences under 18.

  • No copyrighted characters or copyrighted music.

  • No real people (including public figures).

  • Input images with human faces are rejected.

  • Character uploads in this skill are for non-human subjects only.

Prompt augmentation

Reformat prompts into a structured, production-oriented spec. Only make implicit details explicit; do not invent new creative requirements.

Template (include only relevant lines):

Use case: <where the clip will be used>
Primary request: <user's main prompt>
Scene/background: <location, time of day, atmosphere>
Subject: <main subject>
Action: <single clear action>
Camera: <shot type, angle, motion>
Lighting/mood: <lighting + mood>
Color palette: <3-5 color anchors>
Style/format: <film/animation/format cues>
Timing/beats: <counts or beats>
Audio: <ambient cue / music / voiceover if requested>
Text (verbatim): "<exact text>"
Dialogue:
<dialogue>
- Speaker: "Short line."
</dialogue>
Constraints: <must keep/must avoid>
Avoid: <negative constraints>

Augmentation rules:

  • Keep it short; add only details the user already implied or provided elsewhere.

  • For edits, explicitly list invariants ("same shot, change only X").

  • For character-based shots, mention the character name verbatim in the prompt.

  • If any critical detail is missing and blocks success, ask a question; otherwise proceed.

  • If you pass a structured prompt file to the CLI, add --no-augment to avoid the tool re-wrapping it.

Examples

Generation example (single shot)
Use case: product teaser
Primary request: a close-up of a matte black camera on a pedestal
Action: slow 30-degree orbit over 4 seconds
Camera: 85mm, shallow depth of field, gentle handheld drift
Lighting/mood: soft key light, subtle rim, premium studio feel
Constraints: no logos, no text
Edit example (invariants)
Primary request: same shot and framing, switch palette to teal/sand/rust with warmer backlight
Constraints: keep the subject and camera move unchanged
Character consistency example
Primary request: Mossy, a moss-covered teapot mascot, hurries through a lantern-lit market at dusk
Camera: cinematic tracking shot, 35mm, shoulder height
Lighting/mood: warm dusk practicals, soft haze
Constraints: keep Mossy’s silhouette and moss texture consistent across the shot

Prompting best practices (short list)

  • One main action + one camera move per shot.

  • Use counts or beats for timing ("two steps, pause, turn").

  • Keep text short and the camera locked-off for UI or on-screen text.

  • Add a brief avoid line when artifacts appear (flicker, jitter, fast motion).

  • Shorter prompts are more creative; longer prompts are more controlled.

  • Put dialogue in a dedicated block; keep lines short for 4-8s clips.

  • Mention character names verbatim when using uploaded character IDs.

  • State invariants explicitly for edits (same shot, same camera move).

  • Prefer edit for targeted changes and extend for timeline continuation.

  • Iterate with single-change follow-ups to preserve continuity.

Guidance by asset type

Use these modules when the request is for a specific artifact. They provide targeted templates and defaults.

  • Cinematic shots: references/cinematic-shots.md

  • Social ads: references/social-ads.md

CLI + environment notes

  • CLI commands + examples: references/cli.md

  • API parameter quick reference: references/video-api.md

  • Prompting guidance: references/prompting.md

  • Sample prompts: references/sample-prompts.md

  • Troubleshooting: references/troubleshooting.md

  • Network/sandbox tips: references/codex-network.md

Reference map

  • references/cli.md: how to run create/edit/extend/create-character/poll/download/local-queue flows via scripts/sora.py.

  • references/video-api.md: API-level knobs (models, sizes, duration, characters, edits, extensions, official Batch API).

  • references/prompting.md: prompt structure, character continuity, editing, and extension guidance.

  • references/sample-prompts.md: copy/paste prompt recipes (examples only; no extra theory).

  • references/cinematic-shots.md: templates for filmic shots.

  • references/social-ads.md: templates for short social ad beats.

  • references/troubleshooting.md: common errors and fixes.

  • references/codex-network.md: network/approval troubleshooting.

Files

assets/sora-small.svg

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an API key, Needs Python

Security

What our scanner noticed· Low risk

  • Scripts that your agent may run

This skill includes scripts that your agent may run. Read them before you use it.

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
OpenAI
Publisher
JetBrains (Official)
Distributed by
JetBrains
Source
JetBrains/skills · sora · commit 3f53782
Licence
Apache License 2.0 (redistribution allowed)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

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Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download sora.zip