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Competitor analysis

Skill ranbot-ai/awesome-skills/skills/competitor-analysis

Awesome Claude Skills, Tools for Customizing Claude AI workflows

Install
npx -y skills add ranbot-ai/awesome-skills --skill competitor-analysis

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

SKILL.md

5.3 KB, as published. Nobody here has run it

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. Gatescripts/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. Screenshotscapture_screenshots.mjs via the browse CLI captures a 1280×800 homepage hero per competitor
  8. HTML Report — Overview + per-competitor (with embedded hero screenshot + Battle Card card) + matrix + mentions views

Step 0: Setup Output Directory

OUTPUT_DIR=~/Desktop/{company_slug}_competitors_{YYYY-MM-DD}
mkdir -p "$OUTPUT_DIR"

Replace {company_slug} with the user's company name (lowercase, hyphenated) and {YYYY-MM-DD} with today's date. Pass {OUTPUT_DIR} as a full literal path to every subagent.

Clean up discovery batch files from prior runs:

rm -f /tmp/competitor_discovery_batch_*.json

**Re-runs must start from a clean `$

Keep looking

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