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-viral-post-analyzergit 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-viral-post-analyzer)<a href="https://agentmods.dev/skills/taplioofficial/taplio-linkedin-claude-skills/linkedin-viral-post-analyzer"><img src="https://agentmods.dev/badge/skills/taplioofficial/taplio-linkedin-claude-skills/linkedin-viral-post-analyzer/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-viral-post-analyzer"><img src="https://agentmods.dev/badge/skills/taplioofficial/taplio-linkedin-claude-skills/linkedin-viral-post-analyzer.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 | $0.00089 | $0.01401 |
| Opus 5 | $0.00044 | $0.00700 |
| Sonnet 5 | $0.00018 | $0.00280 |
| Haiku 4.5 | $0.00009 | $0.00140 |
Grade B, and why
linkedin-viral-post-analyzer scanned grade B with 1 finding 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.
Subtle steeringmediumPrompt injection
Instructions that bias recommendations or shape behaviour without the user noticing.
- Never recommend the user copy verbatim. Always extract the structure. Copies of this mod
1 near-identical copy found in the catalogue:
- linkedin-viral-post-analyzer — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Viral Post Analyzer
Reverse-engineer the post. Steal the architecture, ship your own substance.
When to trigger
The user pastes a LinkedIn post and says "why did this work ?", "analyze this viral post", "I want to write something like this", "give me the template".
Inputs to ask for
- The full post text.
- The performance numbers if available (impressions, likes, comments, shares).
- The author's typical baseline (so you can spot what made THIS post outperform).
- Optional : the time of day / day of week it was posted.
Process
- Score the post on 7 dimensions :
- Hook strength : does line 1 stop the scroll ?
- Structure : is it scannable ? Where is the white space ?
- Emotional driver : curiosity, anger, validation, hope, status, fear ?
- Specificity : real names, real numbers, real dates ?
- Audience match : does it talk to one specific person, not "everyone" ?
- CTA : does it earn the comment / share / save ?
- Format : text, list, story, contrarian take, screenshot, image ?
- Identify the 2 to 3 levers that did the heavy lifting. Not 7 levers, just the load-bearing ones.
- Strip the post down to its template : replace the substance with placeholders so the user can plug in their own topic.
Output format
POST AT A GLANCE
Author angle : [what they typically post about]
Performance : [numbers, or "above their baseline" if unknown]
Format : [story / list / opinion / contrarian / etc.]
WHAT WORKED (the load-bearing levers)
1. [lever 1 with specific quote from the post]
2. [lever 2 with specific quote]
3. [lever 3 with specific quote, optional]
WHAT DID NOT MATTER
[2-3 things that look important but were not, e.g. "post length", "emojis", "time of day"]
THE REUSABLE TEMPLATE
[
Hook : [pattern]
Setup : [pattern]
Twist : [pattern]
Payoff : [pattern]
CTA : [pattern]
]
HOW TO USE THIS TEMPLATE FOR YOUR NEXT POST
[3 specific topics from the user's world that fit this template]
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 · 93 lines · 89 tokens per session scan B a0918245c558
linkedin-viral-post-analyzer is a skill published in the GitHub repository TaplioOfficial/taplio-linkedin-claude-skills (5 stars, last pushed yesterday), licensed MIT. It adds 89 tokens to every session and 1,401 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it B with 1 finding (subtle steering). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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