Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add TaplioOfficial/taplio-linkedin-claude-skills --skill linkedin-trending-topics-scannergit clone --depth 1 https://github.com/TaplioOfficial/taplio-linkedin-claude-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/taplioofficial/taplio-linkedin-claude-skills/linkedin-trending-topics-scanner)<a href="https://agentmods.dev/skills/taplioofficial/taplio-linkedin-claude-skills/linkedin-trending-topics-scanner"><img src="https://agentmods.dev/badge/skills/taplioofficial/taplio-linkedin-claude-skills/linkedin-trending-topics-scanner/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/taplioofficial/taplio-linkedin-claude-skills/linkedin-trending-topics-scanner"><img src="https://agentmods.dev/badge/skills/taplioofficial/taplio-linkedin-claude-skills/linkedin-trending-topics-scanner.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00094 | $0.01309 |
| Opus 5 | $0.00047 | $0.00655 |
| Sonnet 5 | $0.00019 | $0.00262 |
| Haiku 4.5 | $0.00009 | $0.00131 |
Grade A, and why
linkedin-trending-topics-scanner scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- linkedin-trending-topics-scanner — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Trending Topics Scanner
Riding a trend with a sharp angle beats inventing a topic from scratch.
When to trigger
The user says "what should I post about this week", "what is trending in [niche]", "I want to react to news but without being basic", "give me 5 hot topics for [audience]".
Inputs to ask for
- The niche and audience.
- The user's positioning or angle (so suggested topics stay on-brand).
- How many topics they want (default to 5).
Process
- Brainstorm the categories where trending topics emerge in their niche :
- News and announcements (a competitor launch, an acquisition, a regulation).
- Tools and products (something everyone is suddenly using).
- Public debates (a hot take that is dividing the industry).
- Conferences and events (recap, reactions).
- Cultural shifts (a way of working that is changing).
- Memes and recurring jokes in the niche.
- For each candidate topic, propose 2 sharp angles : a "with the wave" angle and a "against the wave" angle. Contrarian angles tend to outperform on LinkedIn.
- Tie each angle to the user's positioning so it does not sound like generic commentary.
Output format
TRENDING IN [niche] THIS WEEK
1. [Topic / event / debate]
Why it is hot : [one-liner with context]
Angle A (with the wave) : [post angle]
Angle B (against the wave) : [contrarian angle]
Format suggestion : [story / opinion / listicle / carousel]
2. ...
PICK ONE
[1 sentence recommendation : which topic + which angle is the strongest fit for the user's positioning, and why]
Rules
- Never propose generic "trends" like "AI is changing everything". Be specific : a tool, an event, a debate, a name.
- Always include a contrarian angle. Even if the user does not use it, it sharpens the wave-aligned angle.
- If you do not know what is trending in their niche, say so and ask for sources (newsletters they read, podcasts, communities).
- Do not chase virality at the cost of relevance. A trend the user has no credibility on is a trap.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 78 lines · 94 tokens per session scan A d9636f80bfbb
linkedin-trending-topics-scanner is a skill published in the GitHub repository TaplioOfficial/taplio-linkedin-claude-skills (5 stars, last pushed yesterday), licensed MIT. It adds 94 tokens to every session and 1,309 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
linkedin-humanizer
Remove the AI tells human readers and LinkedIn's AI-slop filter react to in a post or comment: 2026 vocabulary by paragraph density, reveal bridges, staccato fragments, stacked triads, performed sincerity. Tiered rewriter (forensic / strict / aesthetic / all) plus --mode audit pass-fail review and --mode profile voice…
linkedin-marketing
Plan, draft, audit, and publish LinkedIn posts and comments. Use when the user wants to write a viral LinkedIn post, draft a comment or reply on any LinkedIn post URL, audit a draft against 2026 algorithm heuristics, remove AI tells, extract hook formulas from viral posts, or plan a week of content. Powered by the…
linkedin-reply-handler
Draft a reply to a specific existing LinkedIn comment from its URL. Use when the user wants to reply to a comment on any post, or follow up after an author replied to them. Parses the commentUrn, resolves the correct parentComment target (LinkedIn flattens threads to 2 levels), and posts via Publora on approval. Not…
linkedin-post-writer
Draft a new LinkedIn post from scratch using one of 20 2026 hook formulas (anaphora, R.I.P., time-anchor, curiosity-gap, contrarian, controlled A/B, false-binary, and more) plus a founders-edition angle library, picked by engagement goal (comments, reposts, likes, saves). Runs the humanizer pass and schedules via…
linkedin-comment-drafter
Draft a LinkedIn comment on someone else's post from its URL, or reshare (repost) it to your feed with optional commentary. Use when the user pastes a post URL and asks to comment, engage, be first commenter, or repost with their thoughts. Produces 1-3 variants in the user's voice, picks a reaction, and publishes via…
linkedin-content-planner
Generate a 7-day LinkedIn content plan from a theme, audience, and pillars. Produces per-day post pillar, format, hook type, CTA, posting time, daily comment targets, and a weekly inbound-readiness check. Use when the user wants to plan a week or month of content, not draft a single post.