# Competitor Social Research

> Compare competitors' social content across platforms to identify recurring topics, formats, hooks, audience reactions, distribution patterns, and credible content opportunities for a brand.

- Skill: `gooseworks-ai/competitor-social-research` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add gooseworks-ai/competitor-social-research`
- Raw SKILL.md: https://api.skillmd.com/api/skills/gooseworks-ai/competitor-social-research/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Research & Search
- Author: gooseworks-ai (https://skillmd.com/u/gooseworks-ai)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/gooseworks-ai/competitor-social-research

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# Competitor Social Research

Build an evidence-backed view of what competitors publish, what earns unusual attention, and where the brand can differentiate.

## Inputs

- The brand, 3–8 competitors, and target audience.
- Platforms and market. Cover TikTok, Instagram, YouTube, Facebook, LinkedIn, Threads, Bluesky, Reddit, or Pinterest only when relevant to the category and audience.
- Default window: 90 days; default sample: 30 recent posts per competitor per platform.

## Workflow

1. Confirm official competitor handles and collect recent posts with `scrapecreators-api`. Include company pages, short-form video, community posts, discussion platforms, and visual-search platforms when they materially affect the category.
2. Normalize metrics within each account and platform. Never compare raw TikTok views directly with LinkedIn reactions.
3. Label every post by topic, format, hook, proof type, CTA, product/education/entertainment intent, and audience problem.
4. Use `outlier-post-finder` to identify unusually strong posts relative to each account's own baseline.
5. Use `transcript-intelligence` for important video posts and `comment-mining` when audience reaction matters. Use `creator-profile-teardown` for priority competitor accounts when positioning and repeatable mechanics need deeper analysis.
6. Separate platform-specific behavior from cross-platform strategy. Do not treat reposting the same asset as independent confirmation of a trend.
7. Separate repeated evidence from inference. Longevity or engagement suggests a pattern; it does not reveal spend, revenue, or causality.

## Output

- Competitor-by-platform coverage table.
- Content mix and cadence comparison.
- Hook, format, proof, and CTA patterns.
- Ten sourced outliers with why they may have worked.
- Saturated themes, whitespace, and five brand-specific experiments.
- Source appendix and limitations.