Agent skill

Competitor Research

by hamzafarooq in hamzafarooq/claude-code-starter

Research 3–5 competitors for any product or feature. An agent skill from hamzafarooq/claude-code-starter.

MITAuto-check passedMarketing & SEO

Install Competitor Research

skills CLI
$ npx skills add hamzafarooq/claude-code-starter --skill competitor-research -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install hamzafarooq/claude-code-starter competitor-research --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/hamzafarooq/claude-code-starter.git skills-src && mkdir -p .claude/skills && cp -r skills-src/demos/research-agent/.claude/skills/competitor-research .claude/skills/competitor-research && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
competitor-research
GitHub stars
145
Token cost
~1.6k tokens
SKILL.md length
776 words
Files
1
Skills in repo
29
Repo updated
First seen
Licence
MIT

At a glance

Research 3–5 competitors for any product or feature. An agent skill from hamzafarooq/claude-code-starter.

  • Works in 6 steps: Scope → Identify competitors → Per competitor (parallel work) → …
  • The user asks about competitors
  • SKILL.md covers Step 1 — Scope, Step 2 — Identify competitors, Step 3 — Per competitor… and Step 4 — Synthesize…, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Competitor Research is an agent skill from hamzafarooq/claude-code-starter. Research 3–5 competitors for any product or feature. Returns positioning, pricing, key differentiators, gaps, and an unclaimed angle. Use when the user asks about competitors, market landscape, or competitive analysis.

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Marketing & SEO, covering Competitor analysis. The licence is MIT.

When your agent uses it

  • The user asks about competitors
  • Market landscape
  • Competitive analysis

Example prompts

  • “/competitor-research”

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Scope
  2. Identify competitors
  3. Per competitor (parallel work)
  4. Synthesize per-competitor cards
  5. Gap Analysis
  6. Close

What it can do on your machine

Read from SKILL.md and the folder at commit 172c531. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Competitor Research loads about 1.6k tokens when it runs. Until then it costs about 60 tokens; SKILL.md has 776 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~60
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from hamzafarooq/claude-code-starter at commit 172c531, republished under its MIT licence (© hamzafarooq). 776 words, ~1,626 tokens.

Download SKILL.mdSave it as .claude/skills/competitor-research/SKILL.md (or your agent's skills folder).
name
competitor-research
description
Research 3–5 competitors for any product or feature. Returns positioning, pricing, key differentiators, gaps, and an unclaimed angle. Use when the user asks about competitors, market landscape, or competitive analysis.

Competitor Research

You research competitors yourself for the light, fast tasks (search and homepage fetches), and you delegate the heavy work to specialist subagents:

  • pricing-fetcher — pricing extraction with WebFetch-first, Playwright-fallback
  • review-miner — distilling user sentiment from 10+ review pages

Workshop note: the split between "skill does it" and "subagent does it" follows one rule: delegate work that is heavy, parallelizable, and returns clean structured output. Light fetches stay in the skill. Heavy or potentially-heavy work (Playwright at ~114K tokens, review-mining across dozens of pages) gets its own context window.


Step 1 — Scope

Ask the user once:

"What product or feature are you researching? Who is it for?"

Skip if they already gave both. Cap at 2 clarifying questions max (geography, segment, direct vs. adjacent). Don't run an interview.

Step 2 — Identify competitors

Use WebSearch by default. Look for:

  • "best [category] tools 2025/2026"
  • G2 / Capterra / Gartner category pages
  • Reddit "alternatives to [known leader]" threads

Use mcp__brave-search__brave_web_search instead only when you need strictly the last 12 months of results (e.g., a fast-moving category where 2-year-old listicles would be misleading). Brave's freshness parameter is deterministic; WebSearch's recency is a soft preference. For most categories the soft preference is fine.

Pick direct competitors (same buyer, same job-to-be-done). List them back to the user briefly so they can correct the set before you go deep.

Step 3 — Per competitor (parallel work)

For each competitor, dispatch the following in a single message with multiple tool calls so they run concurrently:

3a. Homepage — you do this yourself (WebFetch)

Fetch the homepage and extract:

  • Hero headline (verbatim)
  • Subhead (verbatim)
  • Top 3 features in homepage order
  • Stated differentiators (translated to plain English, not paraphrased into marketing-speak)

This stays inline because homepage HTML is server-rendered for ~95% of competitor sites — fast, light, and pollutes nothing.

3b. Pricing — delegate to pricing-fetcher subagent

Spawn one pricing-fetcher per competitor. It tries WebFetch first; falls back to Playwright browser automation only if the page is JS-rendered.

Why this is delegated: Playwright sessions cost ~114K tokens each. Even when only 1 in 3 competitors needs it, isolating that work in subagents keeps the orchestrator's context lean and lets fetches run in parallel.

The subagent returns a structured pricing table plus a Method used field (WebFetch / Playwright) — audit this field. If 4 of 5 competitors all used Playwright, something's wrong with WebFetch and you should investigate before trusting the data.

3c. Reviews — delegate to review-miner subagent

Spawn one review-miner per competitor. It searches across G2/Capterra/Reddit/ HN/ProductHunt/Trustpilot, distills patterns (≥3 confirming voices across ≥2 platforms = signal), and returns recurring strengths + weaknesses.

Why this is delegated: review pages are dense user-voice text — 10+ pages per competitor. Doing it inline pollutes the orchestrator's context with raw review snippets.

If a subagent returns "insufficient data," don't retry with the same prompt — either accept the gap (and surface it in the final report) or hand the next attempt a sharper, narrower question.

Show full SKILL.md (296 more words)Show less

Step 4 — Synthesize per-competitor cards

Combine your homepage research with the two subagents' findings into exactly 6 bullets per competitor:

### [Competitor Name]
- **Positioning**: one sentence (their words, plain English)
- **Target customer**: who they're built for
- **Pricing**: tiers and price points, or "Not public"
- **Differentiators**: 2–3 things they do well
- **Weaknesses**: 1–2 recurring complaints from reviews/forums
- **Source date**: most recent source pulled (YYYY-MM)

Hard rules:

  • 6 bullets exactly. If a bullet is empty, write "—" but keep the line.
  • Pricing must cite a source URL inline if public.
  • No marketing language in your translation.

Step 5 — Gap Analysis

Add this section verbatim:

### Gap Analysis
- **What no competitor does well**: [specific capability gap]
- **Where pricing is underserved**: [a tier or model nobody offers]
- **Unclaimed positioning angle**: [a frame nobody owns]

Each gap must be falsifiable — grounded in something a reader can verify. "Better UX" is not a gap. "No competitor offers per-seat pricing under $10/mo for teams under 5" is.

Step 6 — Close

End the report with exactly one line:

Based on this, which gap are you trying to own?

No summary. No "let me know if you want more." Just the question.


Tool selection cheat sheet

TaskDefault toolReach for MCP when...
Identify competitorsWebSearchYou need strict 12-month recency → brave_web_search
Fetch a homepageWebFetch(never — homepage is light)
Fetch a pricing pagepricing-fetcher subagent(subagent decides internally whether Playwright is needed)
Mine reviewsreview-miner subagent(subagent decides internally — uses Brave's news/summarizer for specific cases)

Default principle: built-in tools first, MCPs only when they offer something built-ins can't.


Anti-patterns (do not do)

  • ❌ Doing your own deep web searches once you've identified competitors — delegate pricing and review work to the subagents
  • ❌ Calling subagents sequentially per competitor instead of in parallel
  • ❌ Reaching for brave_web_search when WebSearch would have been fine (this wastes Brave's free-tier budget)
  • ❌ Invoking Playwright tools directly from the orchestrator — those calls belong in the pricing-fetcher subagent so the heavy context is isolated
  • ❌ Accepting a pricing-fetcher report where 4 of 5 competitors used Playwright — investigate WebFetch first
  • ❌ Adding a 7th bullet "for completeness"
  • ❌ Inventing pricing because the public site is vague
  • ❌ Listing every G2 complaint — only recurring patterns
  • ❌ Writing a closing paragraph after the sharp question

© hamzafarooq, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in demos/research-agent/.claude/skills/competitor-research of hamzafarooq/claude-code-starter.

Open the folder on GitHubat commit 172c531

Compare with similar skills

Competitor Research next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Competitor Research compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Competitor Research this skillhamzafarooq/claude-code-starter145—~1.6kAutomated safety check: PassMIT
SEO Content Brief GeneratorAgriciDaniel/claude-seo19k2 repos~2.6kAutomated safety check: PassMIT
SEO DataforseoAgriciDaniel/codex-seo7972 repos~4.6kAutomated safety check: PassMIT
Competitor ProfilingNexus-JPF/note-companion8704 repos~3.5kAutomated safety check: PassMIT
Startup Competitorsferdinandobons/startup-skill1.2k—~4.1kAutomated safety check: PassMIT
Amazon Listing Competitor Analysisbrowser-act/skills6.1k1 repos~3.2kAutomated safety check: PassMIT

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Categories

Questions about Competitor Research

What does Competitor Research do?

Research 3–5 competitors for any product or feature. An agent skill from hamzafarooq/claude-code-starter. Competitor Research is an agent skill from hamzafarooq/claude-code-starter. Research 3–5 competitors for any product or feature.

When should I use Competitor Research?

Competitor Research fits situations like: the user asks about competitors; market landscape; competitive analysis.

How do I install Competitor Research in Claude Code?

Run `npx skills add hamzafarooq/claude-code-starter --skill competitor-research -a claude-code`. Or copy the skill folder (demos/research-agent/.claude/skills/competitor-research in hamzafarooq/claude-code-starter) into .claude/skills/competitor-research in your project. Claude Code loads it when a task matches its description.

How do I install Competitor Research in Codex?

Run `npx skills add hamzafarooq/claude-code-starter --skill competitor-research -a codex`. Or copy the skill folder (demos/research-agent/.claude/skills/competitor-research in hamzafarooq/claude-code-starter) into .agents/skills/competitor-research in your project. Codex loads it when a task matches its description.

Can I use Competitor Research in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add hamzafarooq/claude-code-starter --skill competitor-research -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/competitor-research, .gemini/skills/competitor-research, .github/skills/competitor-research and .opencode/skills/competitor-research in your project.

What does Competitor Research need to run?

SKILL.md names no scripts, command-line tools or credentials: Competitor Research is instructions for the agent only.

Does Competitor Research access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Competitor Research safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Competitor Research use?

Competitor Research is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Competitor Research use?

About 1.6k tokens (SKILL.md is roughly 6.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Competitor Research?

Skills that share tags, products or a category with Competitor Research: SEO Content Brief Generator (AgriciDaniel/claude-seo, 19k stars), SEO Dataforseo (AgriciDaniel/codex-seo, 797 stars), Competitor Profiling (Nexus-JPF/note-companion, 870 stars) and Startup Competitors (ferdinandobons/startup-skill, 1.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Competitor Research?

hamzafarooq (a GitHub user) maintains it in hamzafarooq/claude-code-starter, which has 145 GitHub stars. The repository holds 29 skills in this directory. The repository was last updated on September 13, 2026.

Source: hamzafarooq/claude-code-starter on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.