remotion-brand-research

Extract visual, verbal, and audience signals into Markdown and JSON brand research dossiers.

Updated Aug 23, 2026
One-click install
npx skills add https://github.com/CleanExpo/Pi-Dev-Ops --skill remotion-brand-research
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: remotion-brand-research
Source: https://github.com/CleanExpo/Pi-Dev-Ops/tree/main/skills/remotion-brand-research
Command: npx skills add https://github.com/CleanExpo/Pi-Dev-Ops --skill remotion-brand-research

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It reduces the time and guesswork required to understand a portfolio company's visual identity and brand voice by consolidating evidence from public sources into a structured research dossier.

Core Features & Use Cases

  • Public brand discovery into structured outputs: Collects visual, verbal, and audience signals from a homepage and key deep pages, then compiles them into a consistent BrandResearch dossier for downstream rendering.
  • Evidence-first extraction: Samples primary colours, extracts logo references from page headers/images, identifies web fonts, pulls tagline and CTA patterns, and flags forbidden claims for safer codification.
  • Supports delta-driven refresh: Automatically refreshes research when a canonical brand config file is missing or stale (older than 90 days) or when the user requests a refresh, enabling iterative brand codification workflows.
  • Hands-off handoff to codification: Produces both Markdown and a structured JSON sibling intended to be consumed by a follow-on codify step that writes brand config files.

Quick Start

Ask the agent to refresh brand research for a specific brand slug and it will produce a dated BrandResearch dossier from the company’s public pages and repository signals.

Frequently Asked Questions about remotion-brand-research

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I extract a brand dossier from a company website and GitHub README?

Brand dossier extraction consolidates visual, verbal, and audience signals from public pages and repository metadata into structured Markdown and JSON outputs. It samples primary colours, identifies web fonts, extracts logo references, and pulls tagline patterns for downstream use.

What is evidence-first brand research and why avoid inventing colours?

Evidence-first brand research pulls visual identity and tone of voice strictly from verifiable public sources. It avoids inventing colours or competitor-derived voice, ensuring the resulting brand dossier reflects actual homepage signals, GitHub metadata, and deep page content without fabrication.

How do I automate a brand identity refresh when the config file is stale?

Automated brand identity refresh triggers when a canonical brand config is missing or older than 90 days. It re-evaluates public sources and repository signals to produce a newly dated brand research dossier, supporting iterative delta-driven refresh workflows.

Does web scraping for brand research respect robots.txt rules?

Web scraping for brand research includes robots.txt awareness to ensure safe handling of public sources. This constraint prevents unauthorized data extraction while gathering homepage signals, logo references, and tone of voice patterns for the structured dossier.

Can I use extracted brand signals to generate a JSON config file?

Extracted brand signals produce a structured JSON sibling alongside a Markdown dossier. This JSON output is specifically designed for hands-off handoff to a follow-on codify step that writes canonical brand config files.

What's the best way to document a portfolio company's visual identity and tone of voice?

Documenting visual identity and tone of voice involves sampling primary colours, extracting web fonts, and pulling CTA patterns from public pages. This evidence-first approach compiles a deterministic brand dossier in Markdown and JSON formats.