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SEO48k · #2605

competitor-analysis

Helps your agent research competitors. It collects information and creates detailed reports. Useful for understanding the competitive landscape and making decisions.

What it does

This tool researches your competitors online. It gathers information about them, takes screenshots of their pages, and organizes everything into a final report.

When to use it

  • ✓When you want to understand who your competitors are.
  • ✓To compare your products/services with those of other companies.
  • ✓When planning to launch a new business or product.

Example of what to ask your AI

“Research the main competitors of my management software company.”

Want more? Open the sections below.

How to use

The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.

Skill link

https://github.com/sickn33/agentic-awesome-skills/blob/main/plugins/agentic-awesome-skills-claude/skills/competitor-analysis/SKILL.md

Ready-to-paste message

Use this skill to help me: https://github.com/sickn33/agentic-awesome-skills/blob/main/plugins/agentic-awesome-skills-claude/skills/competitor-analysis/SKILL.md Read the SKILL.md file at that link and follow its instructions for my task.
How it works

The tool uses a system to search for information on the web, analyzes the data found, and presents it in an easy-to-read report, including visual and feature comparisons.

Installation

Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.

In the terminal (Claude Code, Codex, Cursor), in your project folder:

npx skills add sickn33/agentic-awesome-skills --skill competitor-analysis

Or download just the file:

curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/competitor-analysis/SKILL.md -o SKILL.md
Technical content (original SKILL.md)

Original text by the author, straight from GitHub.

Competitor Analysis

When to Use

Use when the user needs structured competitor research with Browserbase discovery, enrichment lanes, screenshots, comparison matrices, and a final HTML report.

Source: browserbase/skills (MIT).

Analyze a user's competitors. Uses Browserbase Search API for discovery and a 4-lane Plan→Research→Synthesize pattern for enrichment — outputting an HTML report with overview, per-competitor deep dives, a side-by-side feature/pricing matrix, and a chronological mentions feed.

Required: BROWSERBASE_API_KEY env var and the browse CLI installed (npm install -g browse).

First-run setup: On the first run you'll be prompted to approve browse cloud fetch, browse cloud search, cat, mkdir, sed, etc. Select "Yes, and don't ask again for: browse cloud fetch:*" (or equivalent) for each. To permanently approve, add these to your ~/.claude/settings.json under permissions.allow:

"Bash(browse:*)", "Bash(bunx:*)", "Bash(bun:*)", "Bash(node:*)",
"Bash(cat:*)", "Bash(mkdir:*)", "Bash(sed:*)", "Bash(head:*)", "Bash(tr:*)", "Bash(rm:*)"

Path rules: Always use full literal paths in Bash — NOT ~ or $HOME. Resolve the home directory once and use it everywhere. When building subagent prompts, replace {SKILL_DIR} with the full literal path.

Output directory: All output goes to ~/Desktop/{company_slug}_competitors_{YYYY-MM-DD}/. This directory contains one .md file per competitor plus the generated HTML views and CSV.

CRITICAL — Tool restrictions (applies to main agent AND all subagents):

  • All web searches: use browse cloud search. NEVER WebSearch.
  • All page fetches: use browse cloud fetch --allow-redirects (returns markdown by default; add --format raw if you need the original HTML, then pipe through sed ... | tr -s ' \n' to extract text). NEVER WebFetch. 1 MB response limit — fall back to browse get markdown (after browse open <url> --remote) for JS-heavy pages.
  • All research output: subagents write one markdown file per competitor to {OUTPUT_DIR}/{competitor-slug}.md using bash heredoc. NEVER use the Write tool or python3 -c. See references/example-research.md for the file format.
  • Report compilation: use node {SKILL_DIR}/scripts/compile_report.mjs {OUTPUT_DIR} --user-company "{user_company}" --open — generates index.html, competitors/*.html, matrix.html, mentions.html, results.csv in one step and opens overview.
  • URL deduplication: node {SKILL_DIR}/scripts/list_urls.mjs /tmp --prefix competitor.
  • Subagents must use ONLY the Bash tool.
  • Main agent NEVER reads raw discovery JSON batch files.

CRITICAL — Minimize permission prompts:

  • Subagents MUST batch ALL file writes into a SINGLE Bash call using chained heredocs.
  • Batch ALL searches and ALL fetches into single Bash calls via && chaining.

Pipeline Overview

Follow these 8 steps in order. Do not skip or reorder.

  1. User Company Research — Deeply understand the user's company, produce precise_category + category_include_keywords + exclusion_list
  2. Depth Mode + Seed Input — Choose depth, accept optional seed competitor URLs
  3. Discovery (3 parallel waves) — Wave A (alternatives), Wave B (precise category), Wave C (comparison-page graph via "X vs Y" title parsing)
  4. Gate — scripts/gate_candidates.mjs fetches each candidate's hero text (via browse cloud fetch) and drops wrong-category URLs
  5. Confirm enrichment set with the user — Present PASS / UNKNOWN / rejected-brand-matches via AskUserQuestion. User ticks the real ones, adds any the discovery missed. Skipping this step is wasteful because enrichment is expensive (25 subagents × depth budget) and the gate is imperfect (JS-heavy homepages, Cloudflare challenges, semantic-variant taglines)
  6. Deep Enrichment (5 subagents per competitor in deep/deeper modes) — Marketing, Discussion, Social, News, Technical — each lane a separate subagent writing to partials/; then merge_partials.mjs consolidates. In deep/deeper modes, Step 5d adds a 6th Battle Card synthesis lane AFTER Step 5c fact-check completes — produces per-competitor Landmines / Objection Handlers / Talk Tracks grounded in cited evidence.
  7. Screenshots — capture_screenshots.mjs via the browse CLI captures a 1280×800 homepage hero per competitor

…

FAQ

What is 'Browserbase discovery'?

It's the part of the tool that searches for and finds information about competitors on the internet.

What are 'enrichment lanes'?

These are different paths or methods the tool uses to collect and deepen information about each competitor.

Where will the report be saved?

The report will be saved in a folder on your Desktop, named with your company's name and the date.

Details

Original description by the author

Research competitors with Browserbase discovery, enrichment lanes, screenshots, matrices, and HTML reports.

repo: sickn33/agentic-awesome-skills

Popularity

GitHub stars
48k
Ranking position
#2605
License
MIT
Updated
10/15/2026

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