Imported from gooseworks-ai/goose-skills (skills/lead-generation/capabilities/competitor-post-engagers/SKILL.md). Install upstream with npx skills add gooseworks-ai/goose-skills --skill competitor-post-engagers. Copyright stays with the author.

Competitor Post Engagers

Find ICP-fit leads by scraping engagers from a competitor's top-performing LinkedIn posts. Given one or more company page URLs, this skill finds their highest-engagement recent posts, extracts everyone who reacted or commented, and classifies by ICP fit.

Core principle: Scrape all posts in one call per company, then locally rank and select the top N. This minimizes Apify costs while maximizing lead quality.

Phase 0: Intake

Ask the user these questions:

Target Companies

1. LinkedIn company page URL(s) to scrape (e.g., https://www.linkedin.com/company/11x-ai/)
2. Time window — how many days back to look (default: 30)
3. Top N posts per company to extract engagers from (default: 1)

ICP Criteria

4. ICP keywords — job title/role terms that indicate a good lead (e.g., "sales", "SDR", "revenue")
5. Exclude keywords — roles to filter out (e.g., "software engineer", "designer")
6. Geographic focus (optional, e.g., "United States")

Save config in the current working directory (or user-specified path):

competitor-post-engagers-config.json

Config JSON structure:

{
  "name": "<run-name>",
  "company_urls": ["https://www.linkedin.com/company/<competitor>/"],
  "days_back": 30,
  "max_posts": 50,
  "max_reactions": 500,
  "max_comments": 200,
  "top_n_posts": 1,
  "icp_keywords": ["sales", "revenue", "growth", "SDR", "BDR", "outbound"],
  "exclude_keywords": ["software engineer", "developer", "designer"],
  "enrich_companies": true,
  "competitor_company_names": ["<competitor-name>"],
  "industry_keywords": ["freight", "logistics", "trucking", "transportation", "3pl", "supply chain", "carrier", "brokerage", "shipping", "warehousing"],
  "output_dir": "output"
}

• enrich_companies — Enable Apollo company enrichment (default: true). Set to false or use --skip-company-enrich to skip.
• competitor_company_names — Company names to exclude from enrichment (the competitor itself).
• industry_keywords — Industry terms that indicate ICP fit. Matched against Apollo's industry field.

The output_dir is relative to the script directory by default. Override it with an absolute path to write output to a specific location.

Phase 1: Run the Pipeline

python3 skills/competitor-post-engagers/scripts/competitor_post_engagers.py \
  --config competitor-post-engagers-config.json \
  [--test] [--yes] [--skip-company-enrich] [--top-n 3] [--max-runs 30]

Flags:

• --config (required) — path to config JSON
• --test — small limits (20 posts, 50 profiles, 1 top post)
• --yes — skip cost confirmation prompts
• --skip-company-enrich — skip Apollo company enrichment step (saves credits)
• --top-n — override top_n_posts from config
• --max-runs — override Apify run limit

Pipeline Steps

Step 1: Scrape company posts + engagers — For each company URL, one Apify call using harvestapi/linkedin-company-posts with scrapeReactions: true, scrapeComments: true. Returns posts, reactions, and comments in a single dataset.

Step 2: Rank & select top posts — Filter posts by time window (days_back), rank by total engagement (reactions + comments), select top N per company. Then extract engagers (reactors + commenters) only from those selected posts. Deduplication by name. Score engagers by position:

• +3 Commenter (higher intent)
• +2 Position matches ICP keywords
• -5 Position matches exclude keywords

Step 3: Company enrichment (Apollo) — Extract unique company names from engagers, call apollo.enrich_organization(name=...) for each. Returns industry, employee count, description, and location. ~1 Apollo credit per unique company. Merge data back to all engagers from that company. Skip with --skip-company-enrich or "enrich_companies": false.

Step 4: ICP classify & export — Classify as Likely ICP / Possible ICP / Unknown / Tech Vendor. Uses both headline keyword matching AND company industry data (from Step 3) — if the engager's company industry matches industry_keywords, they're classified as "Likely ICP" regardless of role. Export CSV.

Cost Estimates

Phase 2: Review & Refine

Present results:

• Post selection — which posts were chosen and why (engagement counts, preview)
• Per-company breakdown — how many leads from each competitor
• ICP breakdown — counts by tier
• Top 15 leads — name, role, company, engagement type

Common adjustments:

• Too many irrelevant leads — tighten icp_keywords or add exclude_keywords
• Missing ICP leads — broaden icp_keywords
• Wrong posts selected — increase top_n_posts or adjust days_back
• Too expensive — use --test mode or lower max_reactions/max_comments

Phase 3: Output

CSV exported to {output_dir}/{name}-engagers-{date}.csv:

Tools Required

• Apify API token — set as APIFY_API_TOKEN in .env
• Apollo API key — set as APOLLO_API_KEY in .env (for company enrichment)
• Apify actors used:
  • harvestapi/linkedin-company-posts (post + engager scraping)
• Apollo endpoints used:
  • organizations/enrich (company industry/size lookup, 1 credit per company)

Example Usage

Trigger phrases:

• "Find leads engaging with [competitor]'s LinkedIn posts"
• "Scrape engagers from [company]'s top posts"
• "Who is interacting with [competitor]'s content?"
• "Run competitor-post-engagers for [company]"

Test mode:

python3 skills/competitor-post-engagers/scripts/competitor_post_engagers.py \
  --config competitor-post-engagers-config.json --test --yes

Full run:

python3 skills/competitor-post-engagers/scripts/competitor_post_engagers.py \
  --config competitor-post-engagers-config.json --yes