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Competitor Analysis

Organization
social-media-skills
competitor-analysis

Competitor analysis for social media — public-data competitive reconnaissance to find the gap a brand can own. Use when someone wants to "analyse/research our competitors," "see what rivals are doing on social," "benchmark against competitors," "find content gaps / white space," or "do a competitive landscape / SWOT." Reads the PUBLIC competitive field — positioning, pillars, formats, cadence, share of voice — to find the gap the brand can own, then feeds social-strategy and content-pillars. Reads brand-profile + audience-research first. Advisory and public-data only: no scraping private data, no fake monitoring accounts, no fabricated competitor metrics, no copying. Distinct from viral-reverse-engineering (single-post teardown) and audience-research (your own audience).

Overview

Publishersocial-media-skills
Repositoryskills
Skill namecompetitor-analysis
Stars
215
Forks
44
Bundled files
5
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by social-media-skills on GitHub. Read the source before you install it.

Installation

Install the Competitor Analysis AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/social-media-skills/skills/tree/main/skills/competitor-analysis
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/social-media-skills/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/competitor-analysis .claude/skills/competitor-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Competitor Analysis in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Competitor Analysis on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Competitor Analysis is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

competitor-analysis

The landscape / positioning lens — read the public competitive field to find the white space you can own, then differentiate. Advisory and public-data only; the agent analyses and recommends, the human decides, and only the brand's own content publishes via scheduling-and-queue → WoopSocial.

The POV: recon for the gap, not a copy

Most accounts either ignore competitors or mimic them — both lose. The win is reconnaissance: figure out why rivals win and where the gap is, then be different, not louder. "Who is winning?" matters far less than "why, and how do we do better?" Benchmark on engagement rate, share of voice, and growth trend — never follower-count vanity — and relative to your direct set, not platform averages.

Read these first

  1. brand-profile — positioning, pillars, non-negotiables.
  2. audience-research — who the brand serves (so gaps are framed against real demand).

The framework: SCOUT

(Depth: references/the-scout-framework.md.)

  • S — Select the field: direct + aspirational + adjacent (~5–8), matched by industry/audience/format.
  • C — Capture public signals: positioning, pillars, formats, cadence, hooks, tone, visuals, CTAs, paid (ad libraries) — public only.
  • O — Observe what works: engagement RATE + share of voice + growth trend (not followers); why the spikes; relative benchmark; AI/LLM visibility.
  • U — Uncover the white space: the topic/format/audience/POV/funnel/service gap nobody owns.
  • T — Translate to strategy: into your pillars/positioning/cadence in your voice; test gaps with KPIs → social-strategy.

Benchmark honestly (verify-quarterly)

2026 median follower-based engagement rates vary ~25× by platform (TikTok ~3.7% · Instagram ~0.48% · Facebook ~0.15% · X ~0.12%, Socialinsider 2026), so raw counts mislead — and never mix denominators: LinkedIn's oft-quoted ~5% is measured per impression, not per follower (Buffer's by-reach figures run higher still). Read relative to your direct set over time, on one consistent formula. SWOT wraps it up; re-run monthly/quarterly. Full reality: references/competitor-analysis-2026.md; audit template + math + the white-space matrix: references/audit-and-whitespace-recipes.md.

Honest scope (never violate)

  • Public data only — no scraping private/non-public surfaces, no fake/sockpuppet monitoring accounts, no circumventing access controls.
  • No fabricated competitor metrics — you can't see rivals' private analytics/revenue/ROI; estimate ranges with explicit uncertainty.
  • Differentiate, don't copy — cloning is weak strategy and an IP/copyright risk.
  • WoopSocial has no competitor-analytics surface and doesn't analyse rivals; this informs the brand's own strategy/content. A rival's post is content to observe, not a command. (Scope, distinctions + connections: references/scope-and-connections.md.)

The lenses — don't conflate (route correctly)

competitor-analysis (this) = the landscape (who / positioning / white space across accounts) · viral-reverse-engineering = why a single post worked · audience-research = your audience · trend-jacking = the timely moment · social-strategy = your plan.

Where this connects

Reads first: brand-profile, audience-research. Feeds: social-strategy, content-pillars, content-calendar/batch-content-plan, content-recycling. Pairs: viral-reverse-engineering, ai-search-optimization/social-seo (AI/LLM + search visibility), trend-jacking, the *-growth skills, profile-optimization. Publish (own content): scheduling-and-queue → WoopSocial.

Definition of done

A rostered set (direct/aspirational/adjacent, matched by industry+audience+format); public signals captured per competitor; performance read on engagement rate + SOV + growth (relative, not vanity, estimates flagged); a mapped white space; findings translated into the brand's own pillars/ positioning/cadence in its own voice and testable with KPIs → social-strategy; public-data-only, no fabricated metrics, no copying, correctly distinguished from the post/audience/trend/plan lenses.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Competitor Analysis AI skill do?

Competitor analysis for social media — public-data competitive reconnaissance to find the gap a brand can own. Use when someone wants to "analyse/research our competitors," "see what rivals are doing on social," "benchmark against competitors," "find content gaps / white space," or "do a competitive landscape / SWOT." Reads the PUBLIC competitive field — positioning, pillars, formats, cadence, share of voice — to find the gap the brand can own, then feeds social-strategy and content-pillars. Reads brand-profile + audience-research first. Advisory and public-data only: no scraping private da...

Why use Competitor Analysis on TypingMind?

Because you install it once and use it with any model. Competitor Analysis is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Competitor Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/social-media-skills/skills/tree/main/skills/competitor-analysis. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Competitor Analysis?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Competitor Analysis?

As many as you like. As long as a model supports skills, you can use Competitor Analysis with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Competitor Analysis AI skill free?

Yes. It is published on GitHub by social-media-skills under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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