material-report_skill

This skill analyzes a video asset to generate a markdown material report with traits, acquisition keywords, and strategic production frameworks for ads.
  • Python

2.6k

GitHub Stars

2

Bundled Files

2 months ago

Catalog Refreshed

3 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill openclaw/skills --skill material-report

  • _meta.json282 B
  • SKILL.md2.7 KB

Overview

This skill analyzes an ad video asset and produces a Markdown material report with the original creative framework, material traits, and acquisition keywords, then proposes new production frameworks and detailed storyboard tables. It is designed for performance ad teams who want actionable analysis plus ready-to-shoot creative guidance. The output is concise, structured, and tailored to the same category/vertical and target platform you specify.

How this skill works

The skill inspects the provided video file (or an existing frame folder) to read metadata and extract frames when needed, preferring reuse of pre-extracted frames. It automatically identifies the original framework stages (hook, problem, turning points, resolution, ending/CTA), summarizes visual and tempo traits, and generates acquisition keywords tied to the material. Finally, it proposes at least one new material framework plus a detailed storyboard table with time ranges, on-screen action, copy, and visual effects, and returns the full report in Markdown.

When to use it

  • You have a video asset and need a structured analysis for creative optimization.
  • You want new creative frameworks and shot-level storyboards for performance ads.
  • You need acquisition-focused keywords derived from the video’s visual tactics.
  • You want the report formatted in Markdown for easy handoff to editors and ad ops.
  • You need to keep new material duration consistent with the original unless otherwise requested.

Best practices

  • Provide the exact video path and confirm category/vertical and target platform before analysis.
  • If possible, include a folder of pre-extracted frames to speed processing and preserve file permissions.
  • Keep requested new-duration close to the original unless testing a new ad length explicitly.
  • When hooks are ambiguous, allow the skill to propose multiple hook candidates with a ranked rationale.
  • Approve proposed storyboard durations and key frames before production to avoid rework.

Example use cases

  • Analyze a 30s Instagram ad to extract the strongest 0–5s hook and produce two alternate storyboards for A/B testing.
  • Convert a high-performing YouTube ad into a shorter platform-native variant with a new framework focused on faster hook and stronger CTA.
  • Audit creative traits across a campaign batch, standardize acquisition keywords, and propose a unified framework for scaled production.
  • Provide editors with a ready-to-shoot storyboard table after reviewing client-supplied footage and brand guidelines.

FAQ

The skill will prompt you to install ffmpeg or to provide a path to an existing frame image folder; it will not perform installations itself.

I didn’t specify a category/vertical—can you still run the report?

Yes. The skill will ask for the category first; if you prefer, it will make a best-effort inference and clearly label that assumption in the report.

Can you keep the same framework but change only content details?

Yes. If you request a same-framework variant, the skill preserves the structure and updates copy, visuals, and timing as specified.

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