{"id":"linkedin-engager-analytics","name":"linkedin-engager-analytics","summary":"LinkedInの投稿に「いいね」やコメントをした人を引き出し、ICP適合度(同僚/志望者/見込み客/その他)でセグメント化してください。","body":"# LinkedIn Engager Analytics\n\nPull 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.\n\nDepends on `APIFY_TOKEN`. Without it, falls back to user-paste of the engager list.\n\n## When to use\n\n- After publishing a post: \"Who actually engaged? Are they ICP?\"\n- Before a campaign: \"Pull the last 5 viral posts in my niche, group their commenters by company size\"\n- Reviewing competitor engagement: which prospects show up across multiple authors\n\n## Input\n\n- One or more LinkedIn post URLs\n- Optional: ICP definition (target titles, company size, industry)\n- Optional: max engagers per post (default 100)\n\n## Output\n\nOutput 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.\n\n## Steps\n\n1. **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.\n2. **Parse subtitle into structured fields.** The `subtitle` typically reads \"Director at Acme Corp\" or \"Founder & CEO at SaaS Inc\". Extract: title, company, seniority bucket (IC / Manager / Director / VP / C-suite / Founder).\n3. **Score ICP fit.** Use the user's supplied ICP rules:\n   - Title match (regex or keyword list)\n   - Company size proxy (look up via the user's CRM if integrated, else mark Unknown)\n   - Industry match (parse company name + subtitle keywords)\n4. **Assign tier.**\n   - Peer: founder / operator at similar-stage company in same niche\n   - Aspirational: senior leader (Director+) at larger company in adjacent niche\n   - Prospect: title in ICP target list AND company in ICP target list\n   - Other: no match\n5. **Produce action lists.**\n   - Follow back: peers with active posting (heuristic: appears as author in `fetch_user_recent_comments` of any team member)\n   - Comment-drop targets: aspirational tier\n   - DM-able: prospect tier, with a one-line DM opener referencing the specific post they engaged with (\"Saw you reacted to <post angle>. Curious. Are you currently <ICP problem>?\")\n6. **Optional cross-post analysis.** If the user supplied multiple post URLs, deduplicate engagers and flag people who engaged with 2+ posts (highest-intent signal).\n\n## Inbound-quality signals\n\nHigh-quality = follow up: founder/operator title, company in ICP, active posting history, >10 mutual 2nd-degree connections, prior thoughtful comments on user's posts.\n\nLow-quality = skip: generic praise, template language (\"I'd love to hop on a quick call\"), sales/agency profile with no operator history, same comment copy-pasted across many creators.\n\n## Hard rules\n\nGlobal voice rules: see root `SKILL.md` §Voice rules. Additional skill-specific rules:\n\n- Don't run engager analytics on posts you didn't write or aren't tracking with permission. The data is technically public but high-volume scraping of someone else's audience reads as creepy.\n- Don't DM a prospect on the same day they engaged with your post. Wait 24-72h to avoid the \"thirsty\" pattern.\n- One DM opener per engager, not three. If the first didn't land in 5 business days, drop it.\n\n## Cost accounting\n\n| Action | Apify call | Cost (free tier) |\n|---|---|---|\n| Engager analytics on one post (50 engagers) | `fetch_post_engagers(max_items=50)` | $0.25 |\n| Engager analytics on one post (200 engagers) | `fetch_post_engagers(max_items=200)` | $1.00 |\n\nA weekly engager-analytics run on 1-2 posts stays well under the $5 free monthly credit.\n\n## Files\n\n- `SKILL.md` — this file\n- `references/output-spec.md` — engager roster shape, tier breakdown, action lists, sample run\n\n## Related skills\n\n- `linkedin-thread-monitor` — track author replies to YOUR comments (different surface)\n- `linkedin-comment-drafter` — draft outreach comments to engagers from this report\n- `linkedin-reply-handler` — draft DM follow-ups","author":"@sergebulaev","ownerProfile":null,"authorContacts":null,"sourceUrl":"https://github.com/sergebulaev/linkedin-skills/tree/main/.codex-marketplace/linkedin-skills/skills/linkedin-engager-analytics","license":"MIT","category":"document","lang":"en","tokens":976,"stars":0,"calls30d":1,"claimed":false,"visibility":"public","origin":"crawler","version":"0.1.0","createdAt":"2026-08-22","updatedAt":"2026-08-22","files":[{"path":"references/output-spec.md","size":1464,"sha256":"56ed0c7e6397576fab79c25da871cc93ed99adf82d696d0a6b8e628e87ee1f95"}],"requires":{"mcp":[],"tools":[]},"safety":{"flags":[],"scannedAt":"2026-08-22","hasScripts":false,"networkEndpoints":["www.linkedin.com"]}}