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 rianvdm/product-ai-public --skill building-exec-resumesgit clone --depth 1 https://github.com/rianvdm/product-ai-publicWrote 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/rianvdm/product-ai-public/building-exec-resumes)<a href="https://agentmods.dev/skills/rianvdm/product-ai-public/building-exec-resumes"><img src="https://agentmods.dev/badge/skills/rianvdm/product-ai-public/building-exec-resumes/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/rianvdm/product-ai-public/building-exec-resumes"><img src="https://agentmods.dev/badge/skills/rianvdm/product-ai-public/building-exec-resumes.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 | $0.00091 | $0.06840 |
| Opus 5.5 | $0.00036 | $0.02736 |
| Sonnet 5.5 | $0.00018 | $0.01368 |
| Haiku 4.5 | $0.00009 | $0.00684 |
Grade A, and why
building-exec-resumes 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 21d 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 — 267 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Building exec resumes
A framework for writing a VP+ product resume tailored to one posting. Two files hold the candidate's material. The fact base holds every claim: titles, dates, scope, achievements, each with a status tag. The rules in this file decide which facts go on the page and how they're written. Nothing reaches a resume that isn't in the fact base.
The evidence behind each rule is in research/: exec-resume-norms.md, product-exec-signals.md, and ai-tells-resumes.md. Rules that rest on reasoning alone are marked "(inference)". Rules that rest on resume-vendor material are marked "(vendor)".
Setup
Create a resume folder for the candidate with:
| Path | Contents |
|---|---|
career-fact-base.md |
Copy reference/fact-base-template.md and fill it in. |
tailored/ |
One YYYY-MM-DD-company-role.md per application, plus its .posting.md and rendered .pdf. |
public-profile-fixes.md |
A running checklist of LinkedIn or personal-site text that contradicts the fact base: where, current text, canonical text, why. |
Populate the fact base before drafting anything. Pre-fill it from LinkedIn, the candidate's site, and published posts, mark every gap needs-check, then close the gaps in one interview with the candidate. It's done when the candidate has reviewed every needs-check entry.
Rendering needs Python 3.9+, pip install markdown pypdf, and Google Chrome. The renderer lives in scripts/.
How a VP+ resume gets used
At VP level, most doors open through referrals and backchannel references. Korn Ferry's CEO says most unsolicited resumes go unopened (Burnison, 2019), and a former Daversa recruiter says some clients won't take a first call without one or two backdoor references (Gupta, 2024). Ashby's data puts referral applications at a 40% interview rate against 3% for inbound (Ashby).
What ships with it
7 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.
- 21d ago First seen · 267 lines · 91 tokens per session scan A bfa48f770635
building-exec-resumes is a skill published in the GitHub repository rianvdm/product-ai-public (18 stars, last pushed 21d ago), licensed MIT. It adds 91 tokens to every session and 6,840 once invoked, about $0.0004 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 2026-10-01.
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