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-plugin --skill linkedin-audience-persona-buildergit clone --depth 1 https://github.com/TaplioOfficial/taplio-linkedin-pluginWrote 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-plugin/linkedin-audience-persona-builder)<a href="https://agentmods.dev/skills/taplioofficial/taplio-linkedin-plugin/linkedin-audience-persona-builder"><img src="https://agentmods.dev/badge/skills/taplioofficial/taplio-linkedin-plugin/linkedin-audience-persona-builder/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-plugin/linkedin-audience-persona-builder"><img src="https://agentmods.dev/badge/skills/taplioofficial/taplio-linkedin-plugin/linkedin-audience-persona-builder.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 | $0.00099 | $0.01563 |
| Opus 5 | $0.00049 | $0.00781 |
| Sonnet 5 | $0.00020 | $0.00313 |
| Haiku 4.5 | $0.00010 | $0.00156 |
Grade A, and why
linkedin-audience-persona-builder 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 11d 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.
This is a copy
100% identical to linkedin-audience-persona-builder — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Audience Persona Builder
A persona is not a marketing artifact in a slide deck. It is the person the user is talking to in every post.
When to trigger
The user says "build me an ICP", "who am I posting for", "my content is not landing", "help me write to one person", "I do not know my audience".
The 12 questions to ask
- Title and seniority. Be specific. Not "marketers" : "Senior Demand Gen Managers at B2B SaaS, 50-500 employees".
- Company stage and size. Series A vs growth vs enterprise change everything.
- Reports to whom. Their boss's expectations shape their pain.
- Top 3 KPIs they are measured on. This is what keeps them up at night.
- Top 3 jobs to be done in a typical week. What they actually spend time doing.
- Top 3 pains in those jobs. Where the friction is.
- What they buy or consider buying to solve those pains.
- Where do they consume content ? LinkedIn yes, but also newsletters, podcasts, communities.
- Who do they listen to ? The 5 to 10 voices in their head.
- What words do they use ? Their vocabulary, not the user's. ("Pipeline coverage" vs "filling the funnel".)
- What objections do they raise when offered a new idea or tool ?
- What does success look like for them in 12 months ?
Process
- Ask the questions in batches of 3. Do not dump all 12 at once.
- Push for specifics. Reject "anyone in marketing" type answers.
- Once you have all 12, synthesize into a post-ready persona card.
- Generate 10 post topic ideas that hit this persona's pain or aspiration directly.
Output format
PERSONA CARD : [Persona name, e.g. "Demand Gen Dana"]
WHO
Title : [specific]
Company : [stage, size, industry]
Reports to : [their boss]
KPIs : [top 3]
WHAT THEY DO
Jobs to be done : [top 3]
Top pains : [top 3]
Tools they consider buying : [list]
WHERE THEY HANG OUT
Content sources : [list]
Voices they trust : [list]
HOW THEY TALK
Vocabulary they use : [3-5 phrases verbatim]
Vocabulary they HATE : [3-5 phrases]
OBJECTIONS THEY RAISE
1. [objection]
2. [objection]
3. [objection]
12-MONTH ASPIRATION
"[one sentence in their voice]"
10 POST TOPICS THAT HIT THIS PERSONA
1. [topic]
2. [topic]
...
10. [topic]
WRITING RULE
Before publishing, ask : would [persona name] save this, share this, or DM me about this ?
If no, the post is not for them. Either rewrite or skip it.
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.
- 11d ago First seen · 110 lines · 99 tokens per session scan A c3576139ac88
linkedin-audience-persona-builder is a skill published in the GitHub repository TaplioOfficial/taplio-linkedin-plugin (2 stars, last pushed 2mo ago), licensed MIT. It adds 99 tokens to every session and 1,563 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to linkedin-audience-persona-builder, differing in 0 lines, and is treated as a copy.
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