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 | $0.02989 |
| Opus 5 | $0.00068 | $0.01494 |
| Sonnet 5 | $0.00027 | $0.00598 |
| Haiku 4.5 | $0.00014 | $0.00299 |
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 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.
How it starts
The opening of the file, as written. The whole thing — 172 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)".
Lexicon
- Signature phrases: "fluff-free", "No fluff. No spam.", "value packed", "go all in on you", "stack of undeniable proof", "quick win", "literally".
- Email lexicon (verified from her real sequence emails): "high-ticket clients", "proven system", "figure it out alone", "posting into the void", "hold you accountable", "when you're ready".
- Villain word: "gurus". Aspiration words: "proof", "action", "dedication".
- Reader-facing pronouns dominate: her own rule is "Use less "I's" → Use more "you's"" outside the story section.
- Register: warm, direct, zero corporate; light slang, no profanity.
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.
- 12d 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 (6 stars, last pushed 3d ago), licensed MIT. It adds 137 tokens to every session and 2,989 once invoked, about $0.0007 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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