competitor-engagers
Skill759 starsChanged 28 days agoMITView on GitHub
Find people actively engaging with competitor LinkedIn posts. Given a website URL, discovers competitors, fetches their company and employee posts, collects all commenters and reactors, and outputs a deduplicated CSV of engaged prospects. Use when someone wants to find leads from competitor engagement, build outreach lists from LinkedIn activity, or analyze who interacts with competitor content.
- Reads credentials
We split this skill into the rules it gives an agent
7 rules. About 0 are common, 4 are distinctive, and 3 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈4
Distinctive · estimate
This author's own choices. Worth a look.
3
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 13
After collecting the engager CSV, sample ~50 rows and run /icp-prompt-builder before scaling further.
- Very specific: line 19
Before running, ensure these environment variables are set in ~/.env:
- Distinctive: line 38
If the user just says "run it" or provides only a URL, use all defaults.
- Distinctive: line 40
Run the auth preflight check:
- Distinctive: line 44
If it fails, help the user set up their API keys.
- Distinctive: line 46
Run the main script:
- Very specific: line 96
Or wait: if your sample shows heavy non-ICP noise (>50% fans/peers/recruiters), the competitors you chose may be too broad.
≈ “Common” is an estimate: at least 1 in 5 of the 308,232 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Competitor Engagers
Finds people who are actively engaging with your competitors on LinkedIn — the highest-intent prospects for cold outreach.
Required step: Qualify with /icp-prompt-builder (do not skip)
After collecting the engager CSV, sample ~50 rows and run /icp-prompt-builder before scaling further. LinkedIn engagement alone doesn't guarantee ICP fit — someone who reacted to a competitor's post might be a peer, a candidate, a student, or an actual buyer. Qualifying filters separates them.
Why required: unfiltered engager lists typically have 30-50% non-ICP rows (fans, peers, recruiters, students). Running /icp-prompt-builder on a 50-row sample, tuning the qualification prompt, then applying it to the full list cuts wasted enrichment + send costs. Takes 10-15 min.
Setup (First Time Only)
Before running, ensure these environment variables are set in ~/.env:
RAPIDAPI_KEY=<your key from https://rapidapi.com/apibuilderz/api/realtime-linkedin-bulk-data>
OPENROUTER_API_KEY=<your key from https://openrouter.ai/keys>Test your credentials:
npm run competitor-engagers -- --check-authSteps
Parse the user's input. They must provide a website URL (e.g., "clay.com"). Ask for:
- How many competitors to discover (default: 20)
- Posts per company (default: 30)
- Any specific competitor LinkedIn URLs to include
If the user just says "run it" or provides only a URL, use all defaults.
- Run the auth preflight check:
If it fails, help the user set up their API keys.npm run competitor-engagers -- --check-auth - Run the main script:
npm run competitor-engagers -- --url={domain} --competitors={count} --posts={postsPerCompany} {--extra-competitor=URL ...} --verbose - The script prints live progress. It can take 30-120 minutes for a full run (20 competitors x 200 employees x engagement collection). If interrupted, re-run with
--resume:npm run competitor-engagers -- --url={domain} --resume - Once complete, read the output CSV from
~/output/competitor-engagers/and present:- Total unique engagers found
- Top 10 most active engagers (by total_engagements)
- Breakdown by competitor
- Output file path
- Offer follow-up actions:
- "Want to filter to only people with 3+ engagements?"
- "Want to enrich these with email addresses?"
- "Want to run this for a different company?"
- "Want me to start a dry run first to validate the competitor list?"
CLI Reference
--url=<domain> Target company domain (required)
--competitors=<n> AI-discovered competitor count (default: 20)
--posts=<n> Posts per company (default: 30)
--extra-competitor=<url> Add a competitor manually (repeatable)
--dry-run Discover competitors only, skip engagement scraping
--resume Resume from checkpoint after interruption
--verbose Detailed per-request logging
--check-auth Test API credentials and exitNotes
- Skips people employed at the target company OR any of the competitor companies
- Comments and reactions only (reposts not collected)
- 90-day lookback window for posts
- Fetches up to 200 employees per competitor
- One row per person in output (deduplicated, sorted by engagement count)
- Checkpoint saved at
~/output/competitor-engagers/{domain}-checkpoint.json
What to do next
Run /icp-prompt-builder on a 50-row sample of the engagers CSV (required step — LinkedIn engagement alone doesn't guarantee ICP fit). Then /list-quality-scorecard on the filtered list.
Or wait: if your sample shows heavy non-ICP noise (>50% fans/peers/recruiters), the competitors you chose may be too broad. Re-run this skill with tighter competitor selection.
Related skills
/icp-prompt-builder— required filter pass on this skill's output/list-quality-scorecard— grade the filtered list/list-builder(Phase 5, emails) — if LinkedIn engagement doesn't include emails/campaign-copywriting— write copy referencing their specific engagement pattern
Other versions1 project uses a version of this.
Word for word · 0
None found.
Edited · 1
- automatewithuday/GTM-Skills★ 183% same
Wrote their own version · 0
None found.
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository51 other files it gives its agents.
- auto-research-publicSkill
- blitz-list-builderSkill
- campaign-copywritingSkill
- campaign-strategySkill
- cold-email-kickoffSkill
- cold-email-starter-kitSkill
- cold-email-weekly-rhythmSkill
- deliverability-incident-responseSkill
- deliverability-test-publicSkill
- disco-likeSkill
- email-deliverability-auditSkill
- experiment-designSkill
- google-maps-list-builderSkill
- icp-onboardingSkill
- icp-prompt-builderSkill
- inbox-lifecycle-managerSkill
- lead-magnet-brainstormSkill
- list-builderSkill
- list-expanderSkill
- list-quality-scorecardSkill
- perfect-company-listSkill
- personalization-subagent-patternSkill
- clay-playbooksSkill
- playbook-ad-librarySkill
- playbook-ai-specificitySkill
- playbook-case-study-pageSkill
- playbook-company-name-cleaningSkill
- playbook-creative-ideasSkill
- playbook-first-name-cleaningSkill
- playbook-fundraisingSkill
- playbook-google-site-searchSkill
- playbook-hiring-surgeSkill
- playbook-job-posting-languageSkill
- playbook-linkedin-engagementSkill
- playbook-lookalikesSkill
- playbook-name-to-other-prospectsSkill
- playbook-new-in-roleSkill
- playbook-pricing-pageSkill
- playbook-social-link-findingSkill
- playbook-social-postsSkill
- playbook-tech-on-websiteSkill
- playbook-warm-introsSkill
- positive-reply-scoringSkill
- prospeo-full-exportSkill
- prospeo-search-apiSkill
- smartlead-apiSkill
- smartlead-campaign-upload-publicSkill
- smartlead-inbox-managerSkill
- smartlead-spintaxSkill
- spam-word-checkerSkill
- zapmail-domain-setup-publicSkill
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

