Competitor Post Engagers Agent Skill
> Find leads by scraping engagers from a competitor's top LinkedIn posts. Given one or more company page URLs, scrapes recent posts, ranks by engagement, selects the top N, extracts all reactors and commenters, ICP-classifies, and exports CSV. Use when someone wants to "find leads engaging with competitor content" or "scrape people who interact with [company]'s LinkedIn posts".
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/gooseworks-ai/goose-skills --skill competitor-post-engagersWhat comes with it
35 524 bytes besides the instructionThe instruction itself
11 sections, as written by the authorCompetitor 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
- LinkedIn company page URL(s) to scrape (e.g.,
https://www.linkedin.com/company/11x-ai/) - Time window — how many days back to look (default: 30)
- Top N posts per company to extract engagers from (default: 1)
ICP Criteria
- ICP keywords — job title/role terms that indicate a good lead (e.g., "sales", "SDR", "revenue")
- Exclude keywords — roles to filter out (e.g., "software engineer", "designer")
- 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-enrichto 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:
+3Commenter (higher intent)+2Position matches ICP keywords-5Position 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
| Parameter | Test | Standard |
|-----------|------|----------|
| Posts scraped per company | 20 | 50 |
| Max reactions | 50 | 500 |
| Max comments | 50 | 200 |
| Est. Apify cost (1 company) | ~$0.10 | ~$0.50-1 |
| Est. Apollo credits (company enrich) | ~10-20 | ~30-80 unique companies |
| Est. Apollo cost | ~$0.05-0.10 | ~$0.15-0.40 |
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_keywordsor addexclude_keywords - Missing ICP leads — broaden
icp_keywords - Wrong posts selected — increase
top_n_postsor adjustdays_back - Too expensive — use
--testmode or lowermax_reactions/max_comments
Phase 3: Output
CSV exported to {output_dir}/{name}-engagers-{date}.csv:
| Column | Description |
|--------|-------------|
| Name | Full name |
| LinkedIn URL | Profile link |
| Role | Parsed from headline |
| Company | Parsed from headline |
| Company Industry | From Apollo enrichment |
| Company Size | Estimated employee count from Apollo |
| Company Description | Short company description from Apollo |
| Company Location | City, State, Country from Apollo |
| Source Page | Which competitor's page |
| Post URL | Link to the specific post |
| Post Preview | First 120 chars of post content |
| Engagement Type | Comment or Reaction |
| Comment Text | Their comment (personalization gold) |
| ICP Tier | Likely ICP / Possible ICP / Unknown / Tech Vendor |
| Pre-Filter Score | Priority score from pre-filter |
Tools Required
- Apify API token — set as
APIFY_API_TOKENin.env - Apollo API key — set as
APOLLO_API_KEYin.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
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How to use it
Take gooseworks-ai/competitor-post-engagers from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.