Track which of your LinkedIn comments earned author replies. Flags the 6-24h warm-reply window where thread momentum peaks, classifies threads as hot/warm/cool/dormant, and routes warm ones to linkedin-reply-handler for follow-up drafts. Powered by Apify, no LinkedIn login. Triggers on "what threads need follow-up", "author replied", "monitor my comments". Not for analyzing likers on a post (use linkedin-engager-analytics).
npx skills add https://github.com/sergebulaev/linkedin-skills --skill linkedin-thread-monitor
Track which of your comments earned author replies. The author-reply signal is the highest-value inbound LinkedIn produces; this skill ensures you respond inside the window where momentum compounds.
Depends on APIFY_TOKEN. Without it, falls back to user-paste of recent comment URLs.
your-handle)Output format (daily report, warm-thread preview, weekly roll-up): see references/output-spec.md. Headline: a table of recent comments with author-reply status + recommended action.
APIFY_TOKEN is set, call lib.ApifyClient.fetch_user_recent_comments(username=<your-handle>, result_limit=30). Each item already includes the parent post body, post URL, post author, and reaction stats. If APIFY_TOKEN is not set, ask the user to list (or paste) the URLs of comments they've posted in the last 72h.fetch_post_comments(post_id=..., scrape_replies=True)) for:linkedin-reply-handler.Anchored to a 2026-04 data point: a CEO replied to Serge's comment 22h after the original post. Reply-rate distribution: 0-6h 70%, 6-24h 25% (higher quality), >24h rare. Follow-up timing: 0-6h reply respond within 90 min; 6-24h within 2h; >24h within 4h before it goes cold. See references/thread-timing.md for the full matrix.
High-quality = follow up: founder/operator title, company in ICP, active posting history, >10 mutual 2nd-degree connections, prior thoughtful comments on user's posts.
Low-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.
Global voice rules: see root SKILL.md §Voice rules. Additional skill-specific rules:
| Action | Apify call | Cost (free tier) |
|---|---|---|
| Daily thread sweep (1 user, ~30 comments) | fetch_user_recent_comments once | $0.005 |
| Per-warm-thread context | fetch_post_comments(scrape_replies=True) | $0.005 each |
A typical creator running this skill 5 days/week stays well under the $5 free monthly credit.
SKILL.md — this filereferences/output-spec.md — daily report shape, warm-thread preview, weekly roll-up, sample runreferences/thread-timing.md — the timing matrix with exampleslinkedin-reply-handler — drafts the actual follow-up message for warm threadslinkedin-engager-analytics — analyze who liked/commented on a post (different surface)linkedin-comment-drafter — drafts the initial comment that starts threadsComprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas
Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like \"the xlsx in my downloads\") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
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Access AlphaFold 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.
Take sergebulaev/linkedin-thread-monitor 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.