AGENT SKILL

video-prompting

Draft and refine prompts for video generation models (text-to-video and image-to-video). Use when a user asks for a "video prompt" or a model-specific prompt such as Ovi, Sora, Veo 3, Wan 2.2, LTX-2, or LTX-2.3, including requests like "text-to-video prompt", "image-to-video prompt", or "write a prompt for [model]".

License 许可
MIT
最近更新
2026-04-16
Stars
★ 52
Forks
13

01 前言 Front Matter

name
video-prompting
description
Draft and refine prompts for video generation models (text-to-video and image-to-video). Use when a user asks for a "video prompt" or a model-specific prompt such as Ovi, Sora, Veo 3, Wan 2.2, LTX-2, or LTX-2.3, including requests like "text-to-video prompt", "image-to-video prompt", or "write a prompt for [model]".

02 资源文件 12

03 SKILL.md 正文

Video Prompting

Overview

Turn a user’s intent into a strong, model-compliant video prompt by routing to the correct model guide and applying its formatting/tokens.

Model-specific guidance lives in references/models/. This file is the entry point: pick the model, ask the minimum clarifying questions, then draft the prompt in that model’s expected format.

Model Index

  • Ovi: references/models/ovi/prompting.md
  • Sora (Sora 2): references/models/sora/prompting.md
  • Veo 3 / 3.1: references/models/veo3/prompting.md
  • Wan 2.2: references/models/wan22/prompting.md
  • LTX-2: references/models/ltx2/prompting.md
  • LTX-2.3: references/models/ltx2-3/prompting.md

To add a new model later: create references/models/<model>/prompting.md, then add it to this index.

Workflow

Step 1 — Identify the model and input mode

If the user did not name a model, ask which model they are using (or offer supported options from the Model Index).

Then confirm the input mode:

  • Text-to-video (t2v), or
  • Image-to-video (i2v)

If i2v: ask the user to share the image (optional, but it will help you generate a better prompt). Use the image as an anchor according to the chosen model’s guidance (e.g., keep identity/wardrobe/composition stable; focus your text on motion/camera/what changes).

If the chosen model has versions, duration constraints, or required parameters, ask the minimum questions needed to select the right format (see the model guide). For LTX-2.3 specifically: default to a 10-second clip when duration is missing, ask if the user wants shorter or longer, and scale motion complexity to match that duration.

Step 2 — Load the model reference and follow its format

Open the model’s prompting.md from the Model Index and follow its rules strictly (tokens, audio formatting, parameter constraints, recommended structure).

Step 3 — Draft the prompt as a coherent clip

Default structure (adapt to the model’s style and required sections):

  1. Subject(s): who/what, distinctive details
  2. Setting: where/when, lighting, mood
  3. Action progression: what changes over time (start → beat → beat → end)
  4. Camera: framing/movement only if it matters
  5. Dialogue/audio: only if the model supports it, using the model’s exact format

Avoid keyword soup. Prefer a single, well-described shot unless the user explicitly wants multiple cuts/shots.

Step 4 — Output

Default: output only the final prompt text.

If the user asks for options: provide 2–3 distinct prompt variants, each fully self-contained and compliant with the model’s formatting.

If the model uses required API parameters (e.g., duration/size), include a short “Recommended parameters” line only when the user has specified them or explicitly asks for them.

04 同仓库更多 Skills

eric861129
eric861129/SKILLS_All-in-one

what-if-oracle

Run structured What-If scenario analysis with multi-branch possibility exploration. Use this skill when the user asks speculative questions like "what if...", "what would happen if...", "what are the possibilities", "explore scenarios", "scenario analysis", "possibility space", "what could go wrong", "best case / worst case", "risk analysis", "contingency planning", "strategic options", or any question about uncertain futures. Also trigger when the user faces a fork-in-the-road decision, wants to stress-test an idea, or needs to think through consequences before committing.

Skill与智能体开发
★ 52
eric861129
eric861129/SKILLS_All-in-one

venue-templates

Access comprehensive LaTeX templates, formatting requirements, and submission guidelines for major scientific publication venues (Nature, Science, PLOS, IEEE, ACM), academic conferences (NeurIPS, ICML, CVPR, CHI), research posters, and grant proposals (NSF, NIH, DOE, DARPA). This skill should be used when preparing manuscripts for journal submission, conference papers, research posters, or grant proposals and need venue-specific formatting requirements and templates.

内容创作者写作与文案资料检索与研究
★ 52
eric861129
eric861129/SKILLS_All-in-one

the-fool

Use when challenging ideas, plans, decisions, or proposals using structured critical reasoning. Invoke to play devil's advocate, run a pre-mortem, red team, or audit evidence and assumptions.

开发工程师Skill与智能体开发
★ 52
eric861129
eric861129/SKILLS_All-in-one

scientific-writing

Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process with (1) section outlines with key points using research-lookup then (2) convert to flowing prose. IMRAD structure, citations (APA/AMA/Vancouver), figures/tables, reporting guidelines (CONSORT/STROBE/PRISMA), for research papers and journal submissions.

内容创作者写作与文案Skill与智能体开发
★ 52
eric861129
eric861129/SKILLS_All-in-one

scientific-brainstorming

Creative research ideation and exploration. Use for open-ended brainstorming sessions, exploring interdisciplinary connections, challenging assumptions, or identifying research gaps. Best for early-stage research planning when you do not have specific observations yet. For formulating testable hypotheses from data use hypothesis-generation.

Skill与智能体开发
★ 52
eric861129
eric861129/SKILLS_All-in-one

research-lookup

Look up current research information using the Parallel Chat API (primary) or Perplexity sonar-pro-search (academic paper searches). Automatically routes queries to the best backend. Use for finding papers, gathering research data, and verifying scientific information.

开发工程师后端与接口资料检索与研究
★ 52