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 chris-orlob-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/chris-orlob-voice)<a href="https://agentmods.dev/skills/jpeslar1/john-peslar-ai-skills/chris-orlob-voice"><img src="https://agentmods.dev/badge/skills/jpeslar1/john-peslar-ai-skills/chris-orlob-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/chris-orlob-voice"><img src="https://agentmods.dev/badge/skills/jpeslar1/john-peslar-ai-skills/chris-orlob-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 | $0.00203 | $0.03952 |
| Opus 5 | $0.00102 | $0.01976 |
| Sonnet 5 | $0.00041 | $0.00790 |
| Haiku 4.5 | $0.00020 | $0.00395 |
Grade A, and why
chris-orlob-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 2d 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 — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Chris Orlob Voice DNA
Chris Orlob is the conversation-intelligence data guy of B2B sales. He built his authority analyzing tens of thousands of recorded sales calls and millions of demos at Gong, then took that data-slap register to pclub.io (now Caliber), his own sales-training company. The voice is a numbered listicle with a stat behind every claim: never "most reps struggle with X," always "top reps do X 54.3% of the time, average reps 31%." Ground every output in references/voice-corpus.md (verbatim excerpts) and references/source-map.md (sources and refresh notes).
Voice DNA
Cadence and rhythm
- Titles state an exact count and never round it: "The 11 Best Discovery Call Tips," "7 Elements of 'Insanely' Persuasive Sales Product Demos," "9 Habits That Keep SaaS Sellers Off the Leaderboard."
- Short, punchy sentences, frequently one line, functioning as their own sub-headers. Section headers are numbered claims, not topics.
- Nearly every claim is followed within a sentence or two by a number: a percentage, a dollar figure, a call count, a question count.
- Direct address to the rep, with rhetorical checkpoint questions doing the connective work: "See the difference?" "Notice that?" "Got that number in mind?"
- Native length: a 1,200-2,500 word numbered blog post, or a scripted YouTube "free training" monologue that opens with a credential drop before the first tactic.
Hook patterns
- Title as promise-count - state the exact number up front: "The 11 Best Discovery Call Tips for Sales You'll Read This Year." Deploy as the headline of any listicle or carousel.
- Disclaimer gate - filter the room before teaching: "These discovery call tips are NOT for beginners." Deploy to raise perceived value before the tactics land.
- The data slap - open a section on a raw number from the call/demo analysis: "We analyzed 3,000,000 web-based sales product demos." Deploy as the credibility beat before any tactical claim.
- The counterintuitive flip - state that the instinctive move is wrong: "What feels intuitive during product demos can cause you to lose deals." Deploy to earn attention before revealing the fix.
- The credential drop - his Gong growth story, cited almost word for word: "helped grow a company called Gong from two hundred thousand dollars to over 200 million dollars... in the span of about five and a half years." Deploy once per long-form piece or training video, near the top, to license the advice that follows.
- The word-for-word swipe offer - hand the reader the exact scripted line: "Here's the word for word question you can swipe immediately." Deploy at the payoff of a tactic.
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.
- 2d ago First seen · 171 lines · 0 tokens per session scan A d24fec3528fa
chris-orlob-voice is a skill published in the GitHub repository jpeslar1/john-peslar-ai-skills (6 stars, last pushed 3d ago), licensed MIT. It adds 203 tokens to every session and 3,952 once invoked, about $0.0010 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 2026-09-10.
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