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 veezeehq/veezee-skills --skill account-researchgit clone --depth 1 https://github.com/veezeehq/veezee-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/veezeehq/veezee-skills/account-research)<a href="https://agentmods.dev/skills/veezeehq/veezee-skills/account-research"><img src="https://agentmods.dev/badge/skills/veezeehq/veezee-skills/account-research/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/veezeehq/veezee-skills/account-research"><img src="https://agentmods.dev/badge/skills/veezeehq/veezee-skills/account-research.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 | $0.00052 | $0.01249 |
| Opus 5 | $0.00026 | $0.00624 |
| Sonnet 5 | $0.00010 | $0.00250 |
| Haiku 4.5 | $0.00005 | $0.00125 |
Grade A, and why
account-research 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 11d 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 — 40 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Account research with Veezee
Build a briefing on one target account before outreach: what the company is, who matters there, and what they have been posting about. LinkedIn only; this skill covers no other platform. Veezee returns no emails or phone numbers; if the user needs contact details, tell them plainly that this skill cannot provide them.
Setup (one free key, no signup)
Every call needs an API key. Minting one is free and human-free: no signup, no card. A call without a key fails with KEY_REQUIRED (401) carrying mint_url; the fix is the free mint below, never a payment. A fresh vz_trial_ key runs under a free per-IP daily budget of 200 credits, cached data only, first page only on any cursor sequence. Two equivalent surfaces; pick whichever your environment has:
- MCP: add the server
https://mcp.veezee.io/linkedin(streamable-http;https://mcp.veezee.io/allexposes every tool). Hosts that support MCP authorization (Claude Code, claude.ai) open a Veezee sign-in on connect (email code, no password): that is the whole auth step. Other hosts: mint a key withPOST https://api.veezee.io/v1/keys/mint(empty body; the key is shown once) and put it in the connection'sAuthorization: Bearerheader. - SDK:
import { VeezeeClient } from "@veezee/sdk";const client = new VeezeeClient(); await client.mint();mints and stores the free key the first time and reuses it on later runs. Platform methods live on the namespace (client.linkedin.getProfile/searchPeople/getCompany/getPosts);client.resolveUrlandclient.getUsageare top-level. The client sends retries and Idempotency-Keys for you. TheveezeeCLI (npx @veezee/sdk init) mints and stores the same key.
A full account briefing (company, posts, several people) spends more than the free daily budget, so this workflow needs purchased credits. When the budget or a trial cap runs out (TRIAL_CAP_EXCEEDED, INSUFFICIENT_CREDITS, BUDGET_EXHAUSTED), the error carries upgrade_url (https://veezee.io/upgrade) and a machine-readable offer; hand that link to your human, and purchases credit the same key directly, nothing to reconfigure.
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.
- 11d ago First seen · 40 lines · 52 tokens per session scan A dcfde624a612
account-research is a skill published in the GitHub repository veezeehq/veezee-skills (0 stars, last pushed 1mo ago), licensed MIT. It adds 52 tokens to every session and 1,249 once invoked, about $0.0003 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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