Research Competitors Category Pass logo

Research Competitors Category Pass

OrganizationPopular
gooseworks-ai
research-competitors-category-pass

Step of the research-competitors playbook: the worker brief covering the whole category, including searches, AI answers, community threads and category ad winners. Run research-competitors instead; not for direct use.

Overview

Publishergooseworks-ai
Repositorygoose-skills
Skill nameresearch-competitors-category-pass
Stars
1.3K
Forks
219
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

    Published by gooseworks-ai on GitHub. Read the source before you install it.

Installation

Install the Research Competitors Category Pass 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/gooseworks-ai/goose-skills/tree/main/skills/marketing/research-competitors-category-pass
  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/gooseworks-ai/goose-skills.git /tmp/goose-skills
mkdir -p .claude/skills
cp -r /tmp/goose-skills/skills/marketing/research-competitors-category-pass .claude/skills/research-competitors-category-pass
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Research Competitors Category Pass 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 Research Competitors Category Pass 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 Research Competitors Category Pass 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.

category-pass

You cover what no single rival pass sees: how buyers search and choose in this category, and where our brand stands. Do every section; never stop to ask whether to go deeper. Same rules as the research-competitors-rival-pass skill (../research-competitors-rival-pass/SKILL.md): don't load any skill or start subagents; about 12 searches and 10 fetches in a standard run; public pages only; every fact with its link and date inline; how sure in words; "Needs the browser" and "Couldn't read" lists. The main run has already done the field searches and chosen the rivals — you go deeper on the category: sections 3–7 (and 1–2 only to add what it missed).

Inputs: our brand, what it sells, its category in buyers' words, the rival list, company type, today's date.

1. Money searches

Search, in a clean browser or web search where possible: best <category> · <category> software / tool / app · <rival> alternative (each main rival) · <rival> vs <rival> · <our brand> vs <rival> · <our brand> reviews · our brand name alone. Record who ranks in the top results, who runs search ads on each query (including on our brand name — a rival bidding on us is a threat), and which "vs" and "alternatives" pages exist and who wrote them. Results vary by location; say where you searched from.

2. AI-answer presence

Ask the same prompts each time — best <category> tool, alternatives to <main rival>, <our brand> vs <main rival>, what is <our brand> — in every AI answer engine the host can reach (Google's AI Overview in search results counts; others as available). Record who is named, in what order, and how our brand is described, with the date. Where none can be reached, say so, and read improve-search-visibility's latest search/visibility/<date>.md if it exists.

3. Community — how buyers frame the choice

Last 90 days on Reddit (the category's subreddits), Hacker News, LinkedIn comment threads, Quora: best <category>, <category> recommendation, <category> tool for <job>, switching from <rival>. Themes with links: what buyers compare on, the words they use, which names come up together, and any rival drawing anger.

4. Creator demos and reviews

YouTube, TikTok, LinkedIn and X: <category> tool demo, <category> review, best <category> <this year>, and <rival> demo for any rival launch the rival passes flag. For the 3–5 best (by views or engagement relative to the creator's usual): link, date, creator, length, and the beat breakdown — hook · setup · reveal · proof · CTA · style. These are swipe-file candidates: the format is already proven with this audience.

5. Category-wide ad winners

Ads in the category from anyone, not just named rivals: TikTok Creative Center top ads for the category, Meta and LinkedIn ad-library keyword searches for the category's main terms, Google Transparency for category advertisers. Note long-running ads and repeated formats, and any new entrant advertising heavily.

6. Our own brand in the field

Where our brand shows up in sections 1–5, how it is described, and any recent coverage of our own news (from the main run's news scan) that buyers are reacting to.

7. Adjacent options

For each adjacent option the main run named (a spreadsheet, an agency, an in-house team, a DIY tool): how it is pitched (headline or the way buyers describe it), what it costs, and the top 5 complaints buyers have about it, with links. These are often the real competitor for small buyers.

Return this, in this shape

markdown
## Category — as of <date>
Money searches: <query → who ranks · who advertises · comparison pages> (searched from <location>)
AI answers: <prompt → engine → who is named, in order · how we're described> — or "not reachable"
Community themes: <theme — linked examples — names mentioned together>
Creator demos: <link · date · creator · length · beat breakdown> (top 3–5)
Category ad winners: <link · advertiser · start date · format · hook>
New entrants: <name — evidence>
Our brand in the field: <where we appear, how we're described>
Adjacent options: <option — pitch · cost · top 5 complaints (linked)>
Needs the browser: <page — the exact URL or search to open>
Couldn't read: <source — why — what it might have changed>

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 Research Competitors Category Pass AI skill do?

Step of the research-competitors playbook: the worker brief covering the whole category, including searches, AI answers, community threads and category ad winners. Run research-competitors instead; not for direct use.

Why use Research Competitors Category Pass on TypingMind?

Because you install it once and use it with any model. Research Competitors Category Pass 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 Research Competitors Category Pass in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/gooseworks-ai/goose-skills/tree/main/skills/marketing/research-competitors-category-pass. 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 Research Competitors Category Pass?

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 Research Competitors Category Pass?

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

Is the Research Competitors Category Pass AI skill free?

Yes. It is published on GitHub by gooseworks-ai 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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