Agent skill

sora

Use when the user asks to generate, remix, poll, list, download, or delete Sora videos via OpenAI’s video API using the bundled CLI (`scripts/sora.py`), including requests like “generate AI video,” “Sora,” “video remix,” “download video/thumbnail/spritesheet,” and batch video generation; requires `OPENAI_API_KEY` and Sora API access.

Stars 19
Forks 4

Install this agent skill to your Project

npx add-skill https://github.com/x-cmd/skill/tree/main/data/openai/.curated/sora

SKILL.md

Sora Video Generation Skill

Creates or manages short video clips for the current project (product demos, marketing spots, cinematic shots, UI mocks). Defaults to sora-2 and a structured prompt augmentation workflow, 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
  • Remix an existing video by ID
  • Poll status, list jobs, or download assets (video/thumbnail/spritesheet)
  • Batch runs (many prompts or variants)

Decision tree (create vs remix vs status/download vs batch)

  • If the user has a video id and wants a change → remix
  • If the user has a video id and wants status or assets → status/poll/download
  • If the user needs many prompts/assets → create-batch
  • If the user asks for two versions with a small change (same shot, different subject/detail) → create the base, then remix for the variation
  • Otherwise → create (or create-and-poll if they need a ready asset in one step)

Workflow

  1. Decide intent: create vs remix vs status/download vs batch.
  2. Collect inputs: prompt, model, size, seconds, and any input reference image.
  3. If batch: write a temporary JSONL under tmp/ (one job per line), run once, then delete the JSONL.
  4. Prefer CLI augmentation flags (--use-case, --scene, --camera, etc.) instead of pre-writing a structured prompt. If you already produced a structured prompt file, pass --no-augment to avoid double-wrapping.
  5. Run the bundled CLI (scripts/sora.py) with sensible defaults (see references/cli.md). For long prompts, prefer --prompt-file to avoid shell-escaping issues; pair with --no-augment if the prompt is already structured.
  6. For async jobs, poll until complete (or use create-and-poll).
  7. Download assets (video/thumbnail/spritesheet) and save locally.
  8. Remove intermediate files created during invocation (for example prompt.txt, remix_job.json, temp JSONL). If the sandbox blocks rm, skip cleanup or truncate the files without surfacing an error.
  9. Iterate with a single targeted change per prompt.

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" as strings).
  • Always set size and seconds via API params; prose will not change them.
  • Use the OpenAI Python SDK (openai package); do not use raw HTTP.
  • 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.
  • Download URLs expire after about 1 hour; copy assets to your own storage.
  • 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 is limited to 4/8/12 seconds and must be set via the seconds parameter.
  • The API expects seconds as a string enum ("4", "8", "12").
  • 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.

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

Remix 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

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.
  • State invariants explicitly for remixes (same shot, same camera move).
  • 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/poll/remix/download/batch via scripts/sora.py.
  • references/video-api.md: API-level knobs (models, sizes, duration, variants, status).
  • references/prompting.md: prompt structure and iteration 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.

Expand your agent's capabilities with these related and highly-rated skills.

x-cmd/skill

pufferlib

High-performance reinforcement learning framework optimized for speed and scale. Use when you need fast parallel training, vectorized environments, multi-agent systems, or integration with game environments (Atari, Procgen, NetHack). Achieves 2-10x speedups over standard implementations. For quick prototyping or standard algorithm implementations with extensive documentation, use stable-baselines3 instead.

19 4
Explore
x-cmd/skill

fluidsim

Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides pseudospectral methods with FFT, HPC support, and comprehensive output analysis.

19 4
Explore
x-cmd/skill

metabolomics-workbench-database

Access NIH Metabolomics Workbench via REST API (4,200+ studies). Query metabolites, RefMet nomenclature, MS/NMR data, m/z searches, study metadata, for metabolomics and biomarker discovery.

19 4
Explore
x-cmd/skill

geniml

This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.

19 4
Explore
x-cmd/skill

zinc-database

Access ZINC (230M+ purchasable compounds). Search by ZINC ID/SMILES, similarity searches, 3D-ready structures for docking, analog discovery, for virtual screening and drug discovery.

19 4
Explore
x-cmd/skill

astropy

Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.

19 4
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results