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 matteotitta/genesys-skills --skill expert-povgit clone --depth 1 https://github.com/matteotitta/genesys-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/matteotitta/genesys-skills/expert-pov)<a href="https://agentmods.dev/skills/matteotitta/genesys-skills/expert-pov"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/expert-pov/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/matteotitta/genesys-skills/expert-pov"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/expert-pov.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00022 | $0.01456 |
| Opus 5 | $0.00011 | $0.00728 |
| Sonnet 5 | $0.00004 | $0.00291 |
| Haiku 4.5 | $0.00002 | $0.00146 |
Grade A, and why
expert-pov 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 9d 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 — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Expert POV
Extract and synthesize a founder's unique point of view on their market, craft, and philosophy. Produces the raw material for authority-led positioning — beliefs, stances, and perspectives that differentiate the founder.
Core philosophy: Authority comes from having a distinct perspective, not just expertise. Surface what the founder actually believes (including contrarian views) and synthesize into OBI candidates.
When to use
Invoke when user says:
- "expert POV / founder POV for [founder/company]"
- "one big idea / OBI for [founder]"
- "authority positioning / thought leadership foundation"
- "contrarian beliefs / what makes [founder] different"
- "founder interview questions"
Do NOT invoke when:
- User wants product messaging without founder POV →
product-messaging - User wants competitor analysis →
competitor-research - User wants ICP research →
icp-behavioural - User wants content strategy without POV foundation → run this first
Inputs
Required: Company/founder name; industry/category (for question customization).
Optional (improves quality): company context, existing content (LinkedIn posts, podcasts, interviews), competitor positioning, founder background.
Validation before proceeding: founder name provided; industry known or discoverable; collection method agreed (async vs live vs content mining). If missing, ask the user; offer to run company-context first if needed.
Process
Phase 1 — Question generation (25-30 questions across 6 POV dimensions)
↓
Phase 2 — Founder input (async / live / content mining)
↓
Phase 3 — POV extraction (beliefs, hot takes, origin stories, taste)
↓
Phase 4 — OBI synthesis (cluster → candidates → score → develop top OBI)
↓
Review Gate 2 (Deeper Review) [Approve] [Iterate OBI] [Expand themes]
Phase walkthroughs (with checkpoints + outputs):
Phase 4 explicitly checks the 100 Posts Test — could the founder genuinely write 100 authentic posts about this OBI?
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.
- 9d ago First seen · 132 lines · 151 tokens per session scan A 2d2c829b15c6
expert-pov is a skill published in the GitHub repository matteotitta/genesys-skills (36 stars, last pushed 1mo ago), licensed MIT. It adds 22 tokens to every session and 1,456 once invoked, about $0.0001 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 2026-09-03.
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