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

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

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