research-analyst

Identify actionable competitive insights from multi-brand content data.

2|1|Updated Apr 23, 2026

Search Tags:#engagement #benchmark #analyze #competitive-analysis #content-performance #hook-analysis #platform-comparison

Ready to run

npx skills add https://github.com/adologyai/content-intelligence-plugin --skill research-analyst-adologyai

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: research-analyst
Source: https://github.com/adologyai/content-intelligence-plugin/tree/main/skills/research-analyst
Command: npx skills add https://github.com/adologyai/content-intelligence-plugin --skill research-analyst-adologyai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Guides competitive analysis and benchmarking workflows. Use when analyzing brand performance, comparing competitors, identifying trends, or answering strategic questions about content performance.

Core Features & Use Cases

Landscape Scan: parallel retrieval and high-level item distributions to map the market.
Targeted Filtering & Deep Dives: focus analyses on promising brands, formats, and hooks; read item details for actionable insights.
Heavy Mode & Cross-KS Research: perform comprehensive, multi-knowledge-set analyses and cross-market benchmarking.
Proactive Intelligence: surface data quality warnings, engagement skew alerts, and concrete follow-up analyses.
Save & Share: save standout items to collections for stakeholder review.

Quick Start

Run a parallel, multi-brand competitive analysis for Brand A to identify top outperforming hooks and formats.

Frequently Asked Questions about research-analyst

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

FAQPage Schema

How do I run a competitive analysis across multiple brands to find top performing content?▼

Run a competitive analysis by using parallel retrieval to map the market landscape, filtering for promising brands and formats, and reading item details to extract actionable insights on top performing content.

What is cross-KS benchmarking and when do I need it for competitive research?▼

Cross-KS benchmarking is a comprehensive multi-knowledge-set analysis used to compare performance metrics across different markets, required when you need to answer strategic questions about content performance beyond a single brand scope.

Can I analyze content performance and engagement metrics without manual data aggregation?▼

Yes, you can analyze content performance automatically by applying parallelized tool usage to aggregate items and retrieve high-level distributions, allowing you to identify engagement trends and hook performance without manual aggregation.

Does this competitive analysis approach handle skewed engagement data or data quality issues?▼

Yes, the competitive analysis approach proactively surfaces data quality warnings and engagement skew alerts during the landscape scan, ensuring that strategic decisions rely on accurate benchmarking data.

What is the best way to save and share standout competitive insights with stakeholders?▼

The best way to share standout competitive insights is to save high-performing items directly to collections during your deep dive analysis, creating a curated stakeholder review repository of actionable hooks and formats.