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 nanparth/ai-skill-hub --skill intel-persongit clone --depth 1 https://github.com/nanparth/ai-skill-hubWrote 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/nanparth/ai-skill-hub/intel-person)<a href="https://agentmods.dev/skills/nanparth/ai-skill-hub/intel-person"><img src="https://agentmods.dev/badge/skills/nanparth/ai-skill-hub/intel-person/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/nanparth/ai-skill-hub/intel-person"><img src="https://agentmods.dev/badge/skills/nanparth/ai-skill-hub/intel-person.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 | $0.00074 | $0.01331 |
| Opus 5 | $0.00037 | $0.00665 |
| Sonnet 5 | $0.00015 | $0.00266 |
| Haiku 4.5 | $0.00007 | $0.00133 |
Grade A, and why
intel-person 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 10d 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 — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
⛔ GATE DISCIPLINE: user-decision gates = hard stops. Present options as a structured choice if the host supports it; otherwise as a numbered list in plain text. Stop and wait for the user's reply; never auto-pick.
intel-person
Setup — optional browser tooling
Browser automation is optional. If the agent-browser CLI is installed (see https://github.com/vercel-labs/agent-browser), browser-based steps are available: scraping JS-rendered sites, screenshots, headshot capture. If it is not installed, all research still runs: fetch page content directly instead, skip screenshot and headshot capture, and record the skipped assets as gap flags.
Reference Loading Map
| Need | Reference |
|---|---|
| Field checklist, gap analysis spec | references/research-checklist.md |
| URL-first research with screenshots | workflows/from-web.md |
| Existing-notes-first extraction | workflows/from-local-notes.md |
Workflow
Step 1 — Parse inputs
Determine what the user provided:
- Name (required): the person to research
- URL(s): personal site, company bio, award profile
- Note path(s): existing notes containing intel
- Pasted bio text: raw text passed inline
Step 2 — Load checklist and template schema
- Load
references/research-checklist.md. Its frontmatter field list is the default note schema. - If the user supplies their own person-note template, read it and use its frontmatter keys instead. Do NOT hardcode; read live; schemas evolve.
Step 3 — Relationship context ⛔ BLOCKING
Full relationship set: friend, acquaintance, professional peer, mentor, client, family (mapping table below). Ask the user: "How do you know this person?" Options: Friend / Professional peer / Client / Family (no recommended option; factual, no defensible default); "Other" captures acquaintance, mentor. Present as a structured choice if the host supports it; otherwise numbered options in plain text. Stop and wait for the reply. Map answer to ppl-* tag:
What ships with it
6 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.
- 10d ago First seen · 101 lines · 74 tokens per session scan A 65dca1ddb8ce
intel-person is a skill published in the GitHub repository nanparth/ai-skill-hub (23 stars, last pushed 18d ago), licensed MIT. It adds 74 tokens to every session and 1,331 once invoked, about $0.0004 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 2026-08-30.
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