---
name: Competitive Intelligence
slug: competitive-intelligence
category: AI Engineering
description: Competitive Intelligence gathers live web data on competitors, pricing, reviews, hiring, and market position before analyzing the findings. Use it for competitor comparisons, battlecards, SWOTs, and market research that should not rely on training data alone.
github: "https://github.com/apify/awesome-skills/tree/main/skills/apify-easy-competitive-intelligence"
language: Python
stars: 248
forks: 61
install: "npx degit https://github.com/apify/awesome-skills/tree/main/skills/apify-easy-competitive-intelligence ~/.claude/skills/apify-easy-competitive-intelligence"
installs_to: ~/.claude/skills/apify-easy-competitive-intelligence
source_path: skills/apify-easy-competitive-intelligence/SKILL.md
collection_size: 14
category_size: 2631
collection_url: "https://dirskills.com/collections/apify/awesome-skills"
added: 2026-09-02T05:21:12.387Z
last_synced: 2026-09-02T05:21:12.387Z
canonical_url: "https://dirskills.com/skills/competitive-intelligence"
---

# Competitive Intelligence

Competitive Intelligence gathers live web data on competitors, pricing, reviews, hiring, and market position before analyzing the findings. Use it for competitor comparisons, battlecards, SWOTs, and market research that should not rely on training data alone.

**Install:**

```bash
npx degit https://github.com/apify/awesome-skills/tree/main/skills/apify-easy-competitive-intelligence ~/.claude/skills/apify-easy-competitive-intelligence
```

## README

# 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' --user-agent apify-awesome-skills/apify-easy-competitive-intelligence --json 2>/dev/null` → returns run metadata with `defaultDatasetId`
- **Fetch results:** `apify datasets get-items DATASET_ID --user-agent apify-awesome-skills/apify-easy-competitive-intelligence --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 --user-agent apify-awesome-skills/apify-easy-competitive-intelligence --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` |