apify/apify-easy-competitive-intelligence
skills/apify-easy-competitive-intelligence/SKILL.md
This skill should be used when the user asks to "analyze a competitor", "compare pricing", "competitive landscape", "market research", "what do customers think", "review intelligence", "hiring signals", "content strategy", "SEO battle", "build a battlecard", "competitive analysis", "who are the players", "who competes with", "market intelligence", "competitive positioning", "deep dive on a company", "board prep", "SWOT analysis", "how does [X] compare to [Y]", or mentions competitor analysis, pricing comparison, customer sentiment, or market landscape research. Requires Apify CLI or Apify MCP server.
$ install --global
skillsauthnpx skillsauth add apify/awesome-skills apify-easy-competitive-intelligenceInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Last scanned: May 26, 2026, 3:42 AM53.3s11 files scanned
SKILL.md
- name:
- apify-easy-competitive-intelligence
- description:
- >
- author:
- chocholous
- author_url:
- https://github.com/chocholous
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 loginorAPIFY_TOKENenv 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 withdefaultDatasetId - 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+python3withcsv.DictReader - Flags:
--limit N,--offset N,--format json|jsonl|csv|xlsx|xml - Output fields:
apify datasets info DATASET_ID --json | jq .fields
- Quick extraction:
- 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:
- OAuth (interactive):
apify login(opens browser) - Environment variable:
export APIFY_TOKEN=your_token_here - From .env file:
source .env(if the file containsAPIFY_TOKEN=...)
Generate token: https://console.apify.com/settings/integrations
Actor Registry
Every actor call follows three steps:
- 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. - 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.
- 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
- Clarify scope — Identify competitors. Select module(s). Default geography: US.
- Read module reference — Load
reference/modules/<module>.mdfor gathering + analysis instructions. - 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.
- Checkpoint (if not autopilot) — Present first findings, confirm direction.
- Analyze — Select framework, lead with narrative, support with tables.
- 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. - 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-crawlerwhen a dedicated actor exists. - Cost budget — 3-8 actor calls per snapshot. Track total, warn at 15+.
- Parallelize independent
call-actorcalls 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-browseras fallback. Never silently skip a failed data source. - Cite everything — Include source URLs for every data point.
- Async for long runs — Set
async: truefor actors >30s, poll withget-actor-run. - Protected platforms — Do NOT use
website-content-crawlerorrag-web-browserfor: 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 |
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