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-niche-creator-findergit 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-niche-creator-finder)<a href="https://agentmods.dev/skills/taplioofficial/taplio-linkedin-claude-skills/linkedin-niche-creator-finder"><img src="https://agentmods.dev/badge/skills/taplioofficial/taplio-linkedin-claude-skills/linkedin-niche-creator-finder/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-niche-creator-finder"><img src="https://agentmods.dev/badge/skills/taplioofficial/taplio-linkedin-claude-skills/linkedin-niche-creator-finder.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 | $0.00104 | $0.01487 |
| Opus 5 | $0.00052 | $0.00744 |
| Sonnet 5 | $0.00021 | $0.00297 |
| Haiku 4.5 | $0.00010 | $0.00149 |
Grade B, and why
linkedin-niche-creator-finder 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's direct competitors as people to "model". Frame those as "watch closely". Copies of this mod
1 near-identical copy found in the catalogue:
- linkedin-niche-creator-finder — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Niche Creator Finder
You cannot grow on LinkedIn without studying who is already winning in your space.
When to trigger
The user says "who should I follow in [niche]", "who are the top creators in X", "find me people to learn from", "I want to model my content on someone".
Inputs to ask for
- The niche or topic (e.g. "B2B SaaS marketing", "AI for legal", "indie hacking", "FP&A").
- The audience the user wants to attract (founders, marketers, devs, etc.).
- Optional : language (English, French, etc.) and region (US, EU, etc.).
Process
- Brainstorm a candidate list of 15 to 25 creators known to post in this niche regularly. Pull from your knowledge, prioritize creators with consistent output (3+ posts per week) and visible engagement.
- For each candidate, identify :
- Angle : what specific corner of the niche they own.
- Formats : what they post (storytelling, frameworks, hot takes, breakdowns, polls).
- Frequency : how often.
- Voice : their tone (academic, contrarian, friendly, brutal).
- What works : the type of post that gets disproportionate engagement.
- Filter to the top 8 to 10 based on consistency, originality, and how closely their audience matches the user's target.
- For each pick, give the user 1 concrete thing to model.
Output format
TOP 8 CREATORS IN [niche]
1. [Name]
Profile : linkedin.com/in/[handle] (if known, otherwise leave blank)
Angle : [specific corner of the niche]
Formats : [main format mix]
Frequency : [posts per week]
Voice : [tone descriptor]
What works for them : [type of post + why]
Steal this : [one concrete thing the user can model in their own content]
2. ...
End with :
HOW TO USE THIS LIST
- Pick 3 to model. Read their last 30 posts each.
- Note the hooks they reuse, the structure they repeat, the topics they own.
- Comment on their posts daily for 2 weeks to enter their audience's feed.
- Do NOT copy. Borrow the structure, ship your own substance.
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 · 87 lines · 104 tokens per session scan B 0edb7a802dd2
linkedin-niche-creator-finder is a skill published in the GitHub repository TaplioOfficial/taplio-linkedin-claude-skills (5 stars, last pushed yesterday), licensed MIT. It adds 104 tokens to every session and 1,487 once invoked, about $0.0005 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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