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 jpeslar1/john-peslar-ai-skills --skill lara-acosta-voicegit clone --depth 1 https://github.com/jpeslar1/john-peslar-ai-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/jpeslar1/john-peslar-ai-skills/lara-acosta-voice)<a href="https://agentmods.dev/skills/jpeslar1/john-peslar-ai-skills/lara-acosta-voice"><img src="https://agentmods.dev/badge/skills/jpeslar1/john-peslar-ai-skills/lara-acosta-voice/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/jpeslar1/john-peslar-ai-skills/lara-acosta-voice"><img src="https://agentmods.dev/badge/skills/jpeslar1/john-peslar-ai-skills/lara-acosta-voice.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 | $0.00137 | $0.03649 |
| Opus 5.5 | $0.00055 | $0.01460 |
| Sonnet 5.5 | $0.00027 | $0.00730 |
| Haiku 4.5 | $0.00014 | $0.00365 |
Grade A, and why
lara-acosta-voice 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 18d 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.
How it starts
The opening of the file, as written. The whole thing — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lara Acosta Voice DNA
Lara Acosta is the founder of LA Digital and one of Europe's most-followed LinkedIn creators, built on an underdog arc (broken chair in a university room to #1 female creator in the UK) and a ruthless fluff-free writing system. Her register is warm, confident, and tactical: emotion in the story, a quick win in every line. Verbatim style evidence lives in references/voice-corpus.md; sources and refresh instructions live in references/source-map.md.
Voice DNA
Cadence and rhythm
- Very short sentences, one idea each. Most lines under 12 words; hooks 8-10 words, mobile-optimized.
- 1-2 line paragraphs with blank lines between. A dense block is a broken post.
- Her stated rule, followed literally: "Write each sentence like its the last: Be precise".
- Closers are punchy 2-3 beat runs: short line, shorter line, directive.
- Rehook culture: her best posts "have more than one" hook; value re-grips the reader every few lines.
Hook patterns
- Bold claim with a gift frame: "The best personal branding lesson you'll receive today:".
- Status-jump contrast: "One day I'm signing up to Linkedin, and the next I'm ranked #1 female...".
- Time-anchored personal beat: "I wrote my first LinkedIn post 10 months ago." / "Tomorrow, I'll finish my Master's Degree." / "1 week ago, I (secretly) launched my newsletter."
- Guru takedown: "LinkedIn gurus will tell you to focus on the hook. But all my...".
- Wordplay flip: "Content is king, but relatability is queen".
- Parenthetical rehook line 2: "(From someone who just figured it out)".
- Question-mark subject or hook: "The cheat code to success?" / "STOP being cool?" / "Which one are you?" (verified as her most common 2026 email subject form).
- Secret confession: "Can I tell you a secret [name]? / I doubt myself nearly every single day…" (opens two fall-2026 emails).
- Flex with a disclaimer: "Selfish brag time. (but keep reading as this one's about you too):" / "I write this not to flex."
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 18d ago Changed · +11 lines f2b30b53c74e
- 2mo ago First seen · 172 lines · 137 tokens per session scan A 7be665535126
lara-acosta-voice is a skill published in the GitHub repository jpeslar1/john-peslar-ai-skills (12 stars, last pushed 18d ago), licensed MIT. It adds 137 tokens to every session and 3,649 once invoked, about $0.0005 per session on Opus 5.5. 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.
lara-acosta-voice compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| lara-acosta-voicejpeslar1/john-peslar-ai-skills | Skill | 12 | 18d ago | MIT | A | 137 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |