Harvest and rank LinkedIn trend candidates using 2026 engagement scoring signals.
npx skills add https://github.com/Muvon/octomind-tap --skill trend-linkedin
Please help me install this Agent Skill. Skill: trend-linkedin Source: https://github.com/Muvon/octomind-tap/tree/main/skills/trend-linkedin Command: npx skills add https://github.com/Muvon/octomind-tap --skill trend-linkedin
This Skill helps an Octoweb trend agent reliably harvest LinkedIn trend candidates and prioritize posts using LinkedIn-specific ranking signals so you can produce briefs that match what the platform rewards.
Ask an Octoweb trend session to produce a LinkedIn trend harvest for “<your topic>” so it returns ranked posts, the exact above-the-fold hook text, and a ready-to-use briefing structure tuned to LinkedIn’s current ranking signals.
High-intent search queries and answers about installing and using this skill.
You harvest LinkedIn trends by collecting candidate posts across search, hashtags, creator feeds, and personalized feeds, then rank them using a 2026 scoring rubric based on comment-to-reaction ratios and reshare behavior. This yields prioritized trend candidates for content briefs.
The 210-char fold limit is a constraint for LinkedIn hook writing that ensures your opening text fits above the platform's fold without truncation. It requires generating hooks engineered to fit within 210 characters while excluding dead patterns like press-release openers and engagement bait.
To write effective LinkedIn hooks, you apply a hook taxonomy that fits within the 210-char fold while explicitly excluding dead patterns such as press-release openers, engagement bait, rocket stacks, and AI-laden phrasing. This filtering ensures hooks match what the platform currently rewards.
Yes, within an Octoweb trend session you can run parallel harvesting across multiple LinkedIn surfaces including content search, hashtag feeds, creator recent activity, and the signed-in feed. This collects candidates simultaneously to build a comprehensive trend pool.
LinkedIn content scoring prioritizes comment-to-reaction ratio, reshares with commentary, author reply behavior, reaction diversity, and format signals like carousel or video usage. It also applies external-link penalties to rank posts according to 2026 platform economics.
You should exclude or penalize external links in LinkedIn trend briefs because the scoring rubric applies external-link penalties that lower post rankings. Avoiding them aligns your content recommendations with platform engagement economics that favor native content formats.
MuvonHarvest and rank Hacker News trends using Algolia topic windows and an HN-native scoring rubric.
OmexITGenerate publish-ready LinkedIn posts with hooks, body, CTA, and hashtags.
chekosGenerate LinkedIn posts with hooks, bodies, CTAs, and hashtags.
andginjaGenerate social media content strategies and platform-specific posts for LinkedIn, Twitter/X, Instagram, and TikTok.
