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-audience-persona-buildergit 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-audience-persona-builder)<a href="https://agentmods.dev/skills/taplioofficial/taplio-linkedin-claude-skills/linkedin-audience-persona-builder"><img src="https://agentmods.dev/badge/skills/taplioofficial/taplio-linkedin-claude-skills/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-claude-skills/linkedin-audience-persona-builder"><img src="https://agentmods.dev/badge/skills/taplioofficial/taplio-linkedin-claude-skills/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 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-audience-persona-builder — 100% identical, 0 lines differ
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.
- 12d 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-claude-skills (5 stars, last pushed yesterday), 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. 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.