material-report

Analyze ad material videos and produce a markdown report with framework, material traits, and acquisition keywords, then propose new material production frameworks and detailed storyboard tables. Use when users provide a video asset and want analysis plus new creative guidance for performance ads.

Safety Notice

This listing is from the official public ClawHub registry. Review SKILL.md and referenced scripts before running.

Copy this and send it to your AI assistant to learn

Install skill "material-report" with this command: npx skills add fevolq/material-report

Material Report

Quick Start

  • Request or confirm: video path, category/vertical, target platform.
  • Duration spec: keep the new material duration roughly consistent with the original video unless the user explicitly requests otherwise.
  • Analyze the provided video and output a markdown report.

Workflow

  1. Verify the video file exists and read basic metadata (duration, resolution, fps).
  2. Before extracting frames, check whether frame images already exist. If they do, reuse them directly.
  3. If frame extraction is needed, use ffmpeg to extract frames. Save all frame images in a folder named after the video (same base name) under the current working directory. If permissions prevent this, ask the user to grant access; do not change the output path.
  4. If ffmpeg is not available on the user's machine, prompt the user to install ffmpeg (do not provide installation instructions in this skill) or provide an existing frame image folder path.
  5. Extract the original framework as ordered stages (opening hook, problem, turning points, resolution, ending/CTA).
    • Hook identification: prioritize the first 0-5 seconds and any large on-screen headline, abrupt visual change, or explicit "attention" phrasing. If the hook is ambiguous, state multiple candidates and pick the strongest with a brief rationale.
  6. Summarize material traits (format, tempo, overlays, UI density, meme usage, etc.).
  7. List acquisition keywords (concise, actionable, aligned to content and visual tactics).
  8. Create at least one new material framework. For the "different direction" variant, choose the best-performing direction based on your judgment, without being constrained by the original material. Do not deviate from the same category/vertical and target platform.
  9. Provide a detailed storyboard table for the new framework with time ranges, on-screen action, copy, and visual effects.
  10. Deliver the report in markdown in the response.
  11. If the user does not specify category/vertical, first ask for it; then proceed with a best-effort inference and label it clearly as an assumption in the report.

Output Template (Markdown)

  • Load references/report_template.md and follow its structure

Notes

  • Match the user's language and tone.
  • Keep the storyboard single-level lists and clear time ranges.
  • If the user requests a same-framework version, keep the structure identical and only change content details.

Source Transparency

This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.

Related Skills

Related by shared tags or category signals.

General

Ai Competitor Analyzer

提供AI驱动的竞争对手分析,支持批量自动处理,提升企业和专业团队分析效率与专业度。

Registry SourceRecently Updated
General

Ai Data Visualization

提供自动化AI分析与多格式批量处理,显著提升数据可视化效率,节省成本,适用企业和个人用户。

Registry SourceRecently Updated
General

Ai Cost Optimizer

提供基于预算和任务需求的AI模型成本优化方案,计算节省并指导OpenClaw配置与模型切换策略。

Registry SourceRecently Updated