What is prospect-enrich?

A LinkedIn research skill that adds current job, company, and work-history information to a list of known people or prospect identifiers.

Use it to enrich sales leads, prospects, candidates, or other known LinkedIn profiles with their roles, companies, and experience.

It saves time when turning profile links, names, or slugs into useful background information. It does not provide email addresses or phone numbers.

prospect-enrich — Skill for Claude Code and Codex by veezeehq

Install

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.

Any agent

npx skills add veezeehq/veezee-skills --skill prospect-enrich

Clone the repo

git clone --depth 1 https://github.com/veezeehq/veezee-skills

Made for: Claude Code, Codex.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/veezeehq/veezee-skills/prospect-enrich/github.svg)](https://agentmods.dev/skills/veezeehq/veezee-skills/prospect-enrich)

Your own site

<a href="https://agentmods.dev/skills/veezeehq/veezee-skills/prospect-enrich"><img src="https://agentmods.dev/badge/skills/veezeehq/veezee-skills/prospect-enrich/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/veezeehq/veezee-skills/prospect-enrich"><img src="https://agentmods.dev/badge/skills/veezeehq/veezee-skills/prospect-enrich.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 64 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,083 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00064	$0.01083
Opus 5.5	$0.00026	$0.00433
Sonnet 5.5	$0.00013	$0.00217
Haiku 4.5	$0.00006	$0.00108

Measured 2mo ago against content hash 7d960fbcfa7c, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

prospect-enrich 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 2mo 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.

skills/prospect-enrich/SKILL.md · 41 lines

How it starts

The opening of the file, as written. The whole thing — 41 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Prospect enrichment with Veezee

Turn identifiers you already have (LinkedIn profile URLs, slugs, or names) into current role, company, and experience data. LinkedIn only; this skill covers no other platform.

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/all exposes 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 with POST https://api.veezee.io/v1/keys/mint (empty body; the key is shown once) and put it in the connection's Authorization: Bearer header.
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.resolveUrl and client.getUsage are top-level. The client sends retries and Idempotency-Keys for you. The veezee CLI (npx @veezee/sdk init) mints and stores the same key.

When the free 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; purchases credit the same key directly and it keeps working unchanged.

The loop, per prospect

Classify the identifier.
- Clean profile URL, slug (the part after /in/), or URN: go straight to step 2. Do not call resolve_url on clean identifiers; it costs credits for nothing.
- Dirty or ambiguous URL (trackers, redirects, shortened): resolve_url first, then use the returned handle.
- Name only: search_people with keywords (and current_company if known), then take the best match.
get_profile with sections: ["experience"]. The first two sections are included in the base price; each section beyond two costs extra, four sections maximum.
Record full_name, headline, current_position, and the experience entries. Every response carries usage with the exact credits charged.

Read the full file on GitHub · 41 lines

Changes

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.

2mo ago First seen · 41 lines · 64 tokens per session scan A 7d960fbcfa7c

Subscribe to this mod's changes

prospect-enrich is a skill published in the GitHub repository veezeehq/veezee-skills (0 stars, last pushed 3mo ago), licensed MIT. It adds 64 tokens to every session and 1,083 once invoked, about $0.0003 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 2026-08-31.

Marketing and SEO Research

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