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Competitor Research

Organization
explorium-ai
competitor-research

Competitive intelligence skill for Claude Code and Codex: build a fact-led competitive brief or side-by-side battlecard on one or more rival companies using real-time firmographics, funding, headcount, exec roster, hiring shifts, tech stack, and recent strategic moves. Use for sales battlecards, GTM competitive analysis, and market intelligence. Triggers on 'competitor analysis', 'size up a competitor', 'compare rivals', 'competitive landscape', 'vendor battlecard', 'rival snapshot', 'track competitor moves'. Works in Claude Code, Codex, Hermes-Agent, and OpenClaw.

Overview

Publisherexplorium-ai
Repositorygtm-skills
Skill namecompetitor-research
Stars
293
Forks
3
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Competitor Research 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/explorium-ai/gtm-skills/tree/main/skills/competitor-research
  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/explorium-ai/gtm-skills.git /tmp/gtm-skills
mkdir -p .claude/skills
cp -r /tmp/gtm-skills/skills/competitor-research .claude/skills/competitor-research
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Competitor Research 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 Research 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 Research 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 Research

Produce a dated, source-tagged competitive brief for one or more named rival companies.

Input

  • One or more competitor company names or domains (required).
  • Optional brief purpose (e.g. "battlecard for renewal", "board update", "win-loss prep"). If absent, ask once before running so the synthesis stays grounded.
  • Optional priority angles (e.g. "pricing pressure", "AI roadmap", "EMEA expansion").
  • Optional named hypotheses to confirm or contradict.

Workflow

  1. Anchor on purpose. Restate the brief purpose, priority angles, and hypotheses in 1-2 sentences before any tool call so synthesis stays scoped.

  2. Resolve each competitor: match a business by name + domain when available. Domain-variant sanity check: if a major-brand input resolves to a tiny headcount with a "Corporate Managing Offices" or "Hotels and motels" classification, the match likely routed to a registered-agent shell entity. Re-try with the alternate domain or with the company-name string. Do not proceed with the wrong identity. Skip any name that does not resolve confidently and flag it under data quality.

  3. Enrich each resolved competitor across these dimensions: firmographics (HQ, headcount, revenue, founded), competitive landscape (named competitors), strategic insights (priorities and recent moves), funding and acquisitions (rounds and M&A), workforce trends (headcount trajectory), technographics (current stack), challenges (publicly surfaced risks), company ratings (third-party signals), and LinkedIn posts (recent owned-channel narrative).

  4. Fetch business events for the resolved set, scoped to the last 90 days. Keep events that map to: product launches, leadership changes at vice president and above, funding rounds, M&A, office openings or closures, and material hiring shifts. Discard the rest. Event-attribution sanity check: before including any event row in the brief, verify the event title or snippet actually mentions the target company. Industry-wide articles can be cross-attributed to multiple competitors' identities in the ingestion pipeline.

  5. Surface the executive team per competitor: size the audience first (count of C-level and VP roles per company), then sample a small slice for preview. Run a count before any larger pull so you can frame the sample honestly.

  6. If the user asks for outreach-ready contacts on a specific exec subset, enrich those prospects with contacts and profiles. Default to email-only contacts: it costs less than email + phone, and phone numbers are only needed for SDR dialer flows. Use presence-of-email as the contact-quality proxy when filtering.

  7. Classify ICP overlap per competitor (High / Partial / None) using firmographics versus the user's stated ICP. If no ICP was stated, mark overlap as "Not assessed" rather than guessing.

  8. Address each named hypothesis explicitly as confirmed, contradicted, or unresolved, citing the specific enrichment or event row that drove the call.

  9. Final delivery: write the working table out only as the last step (exec roster + key firmographic columns); keep all intermediate previews on screen.

Output Format

Executive Comparison

  • Purpose statement (one sentence) and hypothesis check (confirmed / contradicted / unresolved per item).
  • Side-by-side table across all resolved competitors with columns: company name, domain, HQ, headcount, size bucket, revenue bucket, founded year, public/private, CEO or top exec, last funding event, last strategic move (dated), competitive products noted.
  • 90-day move highlights: dated bullets per competitor, source-tagged to the enrichment that surfaced them (strategic insights, funding, business events).
  • Data quality flags: unresolved names, empty enrichments, stale records, contradictions between enrichments.

Per-Competitor Section

Repeat per resolved competitor:

  • Snapshot: HQ, headcount, revenue range, founded, public/private, primary tech-stack signals.
  • Recent moves (last 90 days): dated bullets, each tagged with the enrichment source.
  • Strategic positioning: pulled verbatim from strategic-insights and competitive-landscape where present. Do not paraphrase.
  • Workforce trajectory: direction and magnitude.
  • Challenges: bullets, dated where available.
  • Funding and M&A: latest round, investors, any acquisitions or divestitures.
  • Exec roster preview: sampled rows with full name, job title, LinkedIn URL, professional email (if enriched). Frame as "Sample preview (5 of matches)." If totals were not captured, use "Sample preview (5 rows). Many more match these filters."
  • ICP overlap: High / Partial / None / Not assessed, with the firmographic evidence in one line.
  • Three discovery questions rooted in specific surfaced facts (not generic).

Limitations

  • Strategic-insights and challenges are gated on SEC 10-K filings: populated for public competitors, null for private ones, and can be 12-18 months stale even when present. Use NAICS + LinkedIn category + technographics overlap for private targets.
  • No native sort by employee count, revenue, or contact data quality. Contact quality is approximated by presence-of-email.
  • Employee count and revenue are bucketed, not raw integers. Side-by-side comparisons use bucket labels.
  • No similar-companies tool. For adjacent rivals beyond named inputs, approximate by resolving competitive-landscape outputs and re-enriching them, and flag the approach explicitly.
  • No metropolitan-area taxonomy. Geographic comparisons use country (ISO-2) or region (ISO 3166-2).
  • Only public/private as a company-type filter; finer ownership structure is not available.
  • Sentiment from G2, TrustRadius, or earnings transcripts is not in the data surface and is out of scope.

Frequently asked questions

What does the Competitor Research AI skill do?

Competitive intelligence skill for Claude Code and Codex: build a fact-led competitive brief or side-by-side battlecard on one or more rival companies using real-time firmographics, funding, headcount, exec roster, hiring shifts, tech stack, and recent strategic moves. Use for sales battlecards, GTM competitive analysis, and market intelligence. Triggers on 'competitor analysis', 'size up a competitor', 'compare rivals', 'competitive landscape', 'vendor battlecard', 'rival snapshot', 'track competitor moves'. Works in Claude Code, Codex, Hermes-Agent, and OpenClaw.

Why use Competitor Research on TypingMind?

Because you install it once and use it with any model. Competitor Research 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 Research in TypingMind?

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

Which AI models can use Competitor Research?

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 Research?

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

Is the Competitor Research AI skill free?

Yes. It is published on GitHub by explorium-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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