linkedin-engager-analytics

# LinkedIn Engager Analytics Pull every liker and commenter on a LinkedIn post and bucket them by ICP fit. Outputs a roster + action list you can feed into your DM or outreach queue. Depends on `APIFY_TOKEN`. Without it, falls back to user-paste of the engager list. ## When to use - After publishing a post: "Who actually engaged? Are they ICP?" - Before a campaign: "Pull the last 5 viral posts in my niche, group their commenters by company size" - Reviewing competitor engagement: which prospects show up across multiple authors ## Input - One or more LinkedIn post URLs - Optional: ICP definition (target titles, company size, industry) - Optional: max engagers per post (default 100) ## Output Output format (engager roster, tier breakdown, action lists): see `references/output-spec.md`. Headline: a table of engagers labelled by ICP tier and a per-tier action list. ## Steps 1. **Fetch engagers.** Call `lib.ApifyClient.fetch_post_engagers(post_url=<url>, max_items=100)`. Returns a list of dicts with `type` ("commenters" | "likers"), `name`, `subtitle` (job title + company), `url_profile`, `content` (comment text if commenter), `datetime`. Cost is roughly $0.005 per engager-record. The underlying actor answers for one audience per run, so `max_items` is the total across both and is split evenly; pass `types=("likers",)` when only one side matters, or add `"reshares"` to include people who reposted. 2. **Parse subtitle into structured fields.** The `subtitle` typically rea...