/competitive-intel

name: competitive-intel
description: >
  Real-time competitive intelligence and market research using Bright Data's
  web scraping infrastructure. Analyzes competitors' pricing, features, reviews,
  hiring patterns, content strategy, and market positioning with live web data.
  Use this skill when the user wants to analyze competitors, compare products,
  monitor pricing changes, track market trends, research a market landscape,
  build competitive battlecards, find positioning opportunities, or conduct
  any form of competitive or market research. Also use when the user mentions
  competitor analysis, market intelligence, competitive landscape, win/loss
  analysis, or wants to understand what competitors are doing.

Competitive Intelligence

Real-time competitive intelligence powered by live web data. Combines Bright Data CLI (`bdata`) for data collection with strategic analysis frameworks to deliver actionable competitive insights — not stale training knowledge.

**Never answer competitive questions from training knowledge alone.** Always gather live data first using `bdata` commands, then analyze and synthesize.

Prerequisites

1. Bright Data CLI installed:

   curl -fsSL https://cli.brightdata.com/install.sh | bash

2. One-time login completed:

   bdata login

That's it. No env vars, no zone config, no API keys to manage.

Core Workflow

For every competitive intelligence request, follow this workflow:

1. **Clarify scope** — Which competitors? What specifically does the user want to know? Select the right module(s). 2. **Gather live data** — Run `bdata` commands. Parallelize independent calls. Prefer `bdata pipelines` (structured JSON) over `bdata scrape` (raw markdown) when a pipeline exists. 3. **Analyze** — Apply the appropriate strategic framework. Read [references/analysis-frameworks.md](references/analysis-frameworks.md) for SWOT, Porter's Five Forces, positioning matrices, and more. 4. **Format output** — Use the report templates from [references/output-templates.md](references/output-templates.md). 5. **Deliver actionable insights** — Every report MUST end with a "Strategic Recommendations" section. Never deliver raw data without interpretation.

Data Collection Rules

• **Always use `--json` flag** when you need to pipe or parse `bdata` output programmatically
• **Prefer `bdata pipelines`** over `bdata scrape` whenever a pipeline type exists for the target platform — pipelines return clean structured JSON
• **Be cost-efficient** — A snapshot should use 3-8 `bdata` calls, not 50. Scrape what you need.
• **Parallelize** — Run independent `bdata` calls in parallel using multiple Bash tool calls in a single response
• **Handle failures gracefully** — If a page is gated or returns empty, say so and try the fallback. Never hallucinate data to fill gaps.
• **Cite every data point** — Include source URLs for everything. Users must be able to verify.

For the full mapping of intelligence needs to `bdata` commands, read [references/data-source-guide.md](references/data-source-guide.md).

For interpreting raw data as strategic signals, read [references/industry-signals.md](references/industry-signals.md).

---

Analysis Modules

1. Competitor Snapshot

**When to use**: User asks to analyze, profile, or understand a specific competitor.

**Data gathering**:

# Step 1: Discover competitor's website and recent news
bdata search "[competitor name]" --json

# Step 2: Scrape key pages (run in parallel)
bdata scrape [competitor-url]              # Homepage — positioning, messaging
bdata scrape [competitor-url]/pricing      # Pricing tiers and model
bdata scrape [competitor-url]/about        # Team, mission, history (try /about, /about-us, /company)

# Step 3: Structured data enrichment (if URLs available)
bdata pipelines crunchbase_company "[crunchbase-url]"       # Funding, investors, employee count
bdata pipelines linkedin_company_profile "[linkedin-url]"   # Employee count, growth, locations

**Analysis**: Synthesize into a structured profile. Identify positioning, target audience, key claims, strengths, and vulnerabilities. Compare to user's product if context is available.

**Output**: Use the Competitor Snapshot template from [references/output-templates.md](references/output-templates.md).

---

2. Pricing Intelligence

**When to use**: User wants to compare pricing, understand pricing models, or find pricing positioning opportunities.

**Data gathering**:

# Scrape pricing pages for each competitor (run in parallel)
bdata scrape [competitor-a-url]/pricing
bdata scrape [competitor-b-url]/pricing
bdata scrape [competitor-c-url]/pricing

# For e-commerce products
bdata pipelines amazon_product "[amazon-url]"
bdata pipelines walmart_product "[walmart-url]"

# Supplementary: third-party pricing breakdowns
bdata search "[competitor] pricing review" --json

**Analysis**: Extract plan names, prices, feature lists, and limits from each page. Normalize into a comparison matrix. Identify pricing model types (per-seat, usage-based, freemium, enterprise-only). Flag positioning signals and recommend opportunities.

**Output**: Use the Pricing Intelligence template from [references/output-templates.md](references/output-templates.md).

---

3. Review Intelligence

**When to use**: User wants to understand customer sentiment, find competitor pain points, or identify exploitable gaps.

**Data gathering**:

# Find review pages via search
bdata search "[competitor] site:g2.com" --json
bdata search "[competitor] site:capterra.com" --json

# Scrape review pages
bdata scrape [g2-url]
bdata scrape [capterra-url]

# Structured review data (use when direct URLs are available)
bdata pipelines google_maps_reviews "[google-maps-url]" 30
bdata pipelines amazon_product_reviews "[amazon-url]"
bdata pipelines google_play_store "[play-store-url]"
bdata pipelines apple_app_store "[app-store-url]"

**Analysis**: Catego