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-cta-optimizergit 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-cta-optimizer)<a href="https://agentmods.dev/skills/taplioofficial/taplio-linkedin-claude-skills/linkedin-cta-optimizer"><img src="https://agentmods.dev/badge/skills/taplioofficial/taplio-linkedin-claude-skills/linkedin-cta-optimizer/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-cta-optimizer"><img src="https://agentmods.dev/badge/skills/taplioofficial/taplio-linkedin-claude-skills/linkedin-cta-optimizer.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 | $0.00101 | $0.01430 |
| Opus 5 | $0.00051 | $0.00715 |
| Sonnet 5 | $0.00020 | $0.00286 |
| Haiku 4.5 | $0.00010 | $0.00143 |
Grade A, and why
linkedin-cta-optimizer 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-cta-optimizer — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn CTA Optimizer
A great post with "Thoughts ?" at the end leaves reach on the table. This skill fixes that.
When to trigger
The user says "what should I put at the end ?", "improve my CTA", "this post is not getting comments", "make this drive DMs / clicks / follows".
Inputs to ask for
- The full current post.
- The CTA goal (only one) :
- Comments (best for reach, since LinkedIn weighs them heavily).
- Shares (good for top-of-funnel).
- DMs (best for lead gen, slow but high-intent).
- Profile visits / follows (best for audience growth).
- Link clicks (worst on LinkedIn, but sometimes needed).
CTA patterns by goal
Comments
- Ask a binary question : "Team A or Team B ?"
- Ask a polarizing opinion : "What is the worst advice you have ever heard about X ?"
- Ask for a specific example : "Drop your favorite tool below."
- Ask for a vote with emojis allowed (only if the user is OK with emojis).
Shares
- "Tag the [persona] who needs this."
- "Share this with one founder you respect."
- Make the post feel like a public service announcement.
DMs
- "DM me [keyword] and I will send you [resource]."
- "Reply 'YES' below and I will send the template."
- Always include a friction-free trigger word.
Profile visits / follows
- "Follow me for one tactic like this every week."
- "I write about X. If you are into X, hit follow."
- Bio must match the promise.
Link clicks
- Put the link in the first comment (LinkedIn buries posts with external links).
- Tease the resource, do not summarize it.
- "I wrote a 2000-word breakdown. Link in comments."
Process
- Read the current post and the CTA.
- Diagnose : is the CTA missing, generic, or mismatched with the goal ?
- Generate 3 to 5 CTA options matching the chosen goal.
- Rank them : top option = highest expected performance for this specific post and audience.
Output format
DIAGNOSIS
[One sentence on why the current CTA is leaving outcomes on the table]
CURRENT CTA
"[paste current closing]"
OPTION 1 - [pattern name] (RECOMMENDED)
"[CTA copy]"
Why : [one-liner]
OPTION 2 - [pattern name]
"[CTA copy]"
Why : [one-liner]
... up to 5 options
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 · 109 lines · 101 tokens per session scan A 79418e822b4d
linkedin-cta-optimizer is a skill published in the GitHub repository TaplioOfficial/taplio-linkedin-claude-skills (5 stars, last pushed yesterday), licensed MIT. It adds 101 tokens to every session and 1,430 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.
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