apify-easy-competitive-intelligence

Competitive Intelligence

Real-time competitive intelligence powered by live web data via Apify actors. Never answer competitive questions from training knowledge alone. Always gather live data first, then analyze.

Prerequisites

• Apify CLI v1.5.0+ (npm install -g apify-cli), or Apify MCP server
• Authenticated session (apify login or APIFY_TOKEN env var)

CLI rules: Always pass --json, --user-agent apify-awesome-skills/apify-easy-competitive-intelligence, and 2>/dev/null.

• Run actor: apify actors call "ACTOR_ID" -i 'INPUT' --json 2>/dev/null → returns run metadata with defaultDatasetId
• Fetch results: apify datasets get-items DATASET_ID --format json > /tmp/results.json 2>/dev/null — save locally, parse from file:
  • Quick extraction: jq '.[] | "\(.field1) | \(.field2)"' /tmp/results.json
  • Aggregation: python3 -c "import json; d=json.load(open('/tmp/results.json')); ..."
  • Tabular: --format csv > /tmp/results.csv + python3 with csv.DictReader
  • Flags: --limit N, --offset N, --format json|jsonl|csv|xlsx|xml
  • Output fields: apify datasets info DATASET_ID --json | jq .fields
• Fetch schema: apify actors info "ACTOR_ID" --input --json 2>/dev/null

If CLI is unavailable and Apify MCP server is connected, use MCP call-actor / fetch-actor-details / get-actor-output directly.

Authentication

If a CLI command fails with an auth error, authenticate using one of these methods:

1. OAuth (interactive): apify login (opens browser)
2. Environment variable: export APIFY_TOKEN=your_token_here
3. From .env file: source .env (if the file contains APIFY_TOKEN=...)

Generate token: https://console.apify.com/settings/integrations

Actor Registry

Every actor call follows three steps:

1. Read — find the actor's section in reference/actor-schemas.md. Use the exact verified input and follow the "How to find" instructions for URLs/slugs.
2. Discover — verify platform URLs and slugs (e.g. via SERP) as described in the actor's schema section. Do not guess — wrong slugs silently return empty or wrong data.
3. Run — call the actor with verified input.

Alternatively, fetch the live schema: apify actors info "ACTOR_ID" --user-agent apify-awesome-skills/apify-easy-competitive-intelligence --input --json 2>/dev/null

Data Need	Actor	Notes
Google SERP	apify/google-search-scraper	Supports country/language. SERP snippets contain ratings & review counts
Page scrape	apify/website-content-crawler	proxyConfiguration REQUIRED. Returns markdown
RAG browse	apify/rag-web-browser	Search + scrape in one call. Good fallback
LinkedIn company	dev_fusion/Linkedin-Company-Scraper	Output in KV store, not dataset
LinkedIn jobs	curious_coder/linkedin-jobs-scraper	Requires LinkedIn search URL, NOT keywords
Crunchbase	pratikdani/crunchbase-companies-scraper	Single company URL per call
Amazon product	junglee/Amazon-crawler	Product or category URLs
Amazon reviews	web_wanderer/amazon-reviews-extractor	May return 0 for some products
Walmart product	e-commerce/walmart-product-detail-scraper	May return empty
Google Maps reviews	compass/Google-Maps-Reviews-Scraper	Use full Google Maps place URL
G2 reviews	automation-lab/g2-scraper	NPS, ratings, switching data. $0.04/run
Capterra reviews	zen-studio/capterra-reviews-scraper	$1.99/1K
Gartner Peer Insights	—	No working actor. Use SERP snippet mining as fallback
Glassdoor	memo23/glassdoor-scraper-ppr	Reviews, salaries, culture, ratings
Reddit	harshmaur/reddit-scraper	Posts + full comment threads
Google Play reviews	neatrat/google-play-store-reviews-scraper	App ID or Play Store URL
App Store	jdtpnjtp/apple-app-store-scraper	Requires SHADER proxy — may not be available on all plans
SimilarWeb	pro100chok/similarweb-scraper	Minimum 10 domains per call
Google News	data_xplorer/google-news-scraper-fast	No boolean operators in keywords
Wayback Machine	andok/wayback-machine-scraper	Full URL including path

Core Workflow

Step 0: Understand the User (once, at start)

Clarify before gathering data:

• Role — Analyzed company, competitor, investor, consultant?
• Decision — Entering market, defending position, choosing vendor, building battlecard?
• Autonomy — Checkpoints after initial findings, or autopilot?

Steps 1–7

1. Clarify scope — Identify competitors. Select module(s). Default geography: US.
2. Read module reference — Load reference/modules/<module>.md for gathering + analysis instructions.
3. Gather live data — For each actor call, follow the three-step pattern: Read (actor-schemas.md) → Discover (SERP for URLs) → Run (call actor). Use PRIMARILY actors from the Actor Registry above.
4. Checkpoint (if not autopilot) — Present first findings, confirm direction.
5. Analyze — Select framework, lead with narrative, support with tables.
6. Verify — Run pre-delivery verification (reference/verification-checklist.md). Check: every claim has a source URL, every major finding has a confidence label, inferences are labeled as such. Remove any ungrounded claims.
7. Deliver — End with strategic recommendations framed for the user's role.

Framework Selection

Situation	Framework
Profile one competitor	SWOT
Market dynamics & forces	Porter's Five Forces
Visual position comparison	Strategy Canvas (Blue Ocean)
Why customers switch	Jobs-to-be-Done
Find white space	Positioning Matrix (2x2)
Predict competitor reaction	Competitive Response Matrix

Data Collection Rules

• Prefer structured actors over website-content-crawler when a dedicated actor exists.
• Cost budget — 3-8 actor calls per snapshot. Track total, warn at 15+.
• Parallelize independent call-actor calls in a single response.
• Failures — Report every failure explicitly (actor, input, error). Retry with corrected input if the cause is obvious. If retry fails, try rag-web-browser as fallback. Never silently skip a failed data source.
• Cite everything — Include source URLs for every data point.
• Async for long runs — Set async: true for actors >30s, poll with get-actor-run.
• Protected platforms — Do NOT use website-content-crawler or rag-web-browser for: g2.com, capterra.com, gartner.com, glassdoor.com, reddit.com, linkedin.com. Use dedicated actors.

Apify vs. WebSearch

Apify required: review sites (G2, Capterra, Gartner, Glassdoor), LinkedIn, Reddit, Amazon, Walmart, app stores, SimilarWeb, Crunchbase, Wayback Machine, Google Maps reviews, news (Google News actor).

WebSearch/WebFetch sufficient (Claude Code built-in tools): competitor discovery, general company info, blog posts, publicly accessible pricing pages.

Data Validation & Grounding

• Every factual claim needs a source URL. No link = not a fact.
• Confidence labels are mandatory. Mark every major finding: High (primary source), Medium (2+ third-party sources), Low (single third-party source). Format: [Confidence | Source]. No report without labels.
• Data tiers: Verified (primary source) → Reported (third-party, attribute) → Inferred (label as "this suggests...") → Ungrounded (omit).
• Numbers are dangerous — employee counts, revenue, funding change fast. Always cite source and date.
• Empty results ARE intelligence — 0 jobs = not hiring, 0 SimilarWeb = small site, 12 reviews = low adoption.
• Cross-reference — Single-source claims are unverified. Multi-source (G2 + Capterra + Reddit) = pattern.

Module Selection

User says...	Module	Reference
"Analyze [competitor]", "Tell me about [company]"	Competitor Snapshot	reference/modules/competitor-snapshot.md
"Compare pricing", "How much does [X] cost"	Pricing Intelligence	reference/modules/pricing-intelligence.md
"Pricing details", "per-use-case costs", "tiers", "add-ons"	Pricing Deep Dive	reference/modules/pricing-deep-dive.md
"What do customers think", "Reviews", "Pain points"	Review Intelligence	reference/modules/review-intelligence.md
"What are they hiring for", "Job postings"	Hiring Signals	reference/modules/hiring-signals.md
"How do they rank", "Content strategy", "SEO"	Content & SEO	reference/modules/content-seo.md
"Who are the players", "Market landscape"	Market Landscape	reference/modules/market-landscape.md
"Full battlecard", "Deep analysis", "Board prep"	Multi-Module	reference/multi-module-playbook.md