find-net-new-accounts

find-net-new-accounts is a skill for Claude Code from vy-labs/canonical-mcp. It costs 87 tokens per session (999 once invoked), scanned A, original, MIT.

A company-discovery workflow for building a list of businesses that match a target customer description. It uses Canonical, a verified company database, to find less obvious companies that ordinary sales databases may miss.

In plain words
What is it for?
Use it to build a target-account list from a plain-English profile, find companies similar to an example company, or identify companies not already in your sales database. It discovers and verifies companies but does not find contacts, send email, or update a CRM.
Why use it?
It separates finding suitable companies from finding people or sending messages. This helps uncover long-tail or new accounts before handing them to tools that enrich contacts or manage outreach.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the canonical plugin — 1 skill, 1 command, 1 MCP server shipped together

Good fit Use it to build a target-account list from a plain-English profile, find companies similar to an example company, or identify companies not already in your sales database. It discovers and verifies companies but does not find contacts, send email, or update a CRM.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/vy-labs/canonical-mcp/find-net-new-accounts
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 vy-labs/canonical-mcp --skill find-net-new-accounts
Clone the repo
git clone --depth 1 https://github.com/vy-labs/canonical-mcp

Made for: Claude Code.

Or install canonical, the plugin that ships this one along with the rest of its 1 skill, 1 command, 1 MCP server.

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.

agentmods badge for find-net-new-accounts

README.md
[![agentmods](https://agentmods.dev/badge/skills/vy-labs/canonical-mcp/find-net-new-accounts/github.svg)](https://agentmods.dev/skills/vy-labs/canonical-mcp/find-net-new-accounts)
Your own site
<a href="https://agentmods.dev/skills/vy-labs/canonical-mcp/find-net-new-accounts"><img src="https://agentmods.dev/badge/skills/vy-labs/canonical-mcp/find-net-new-accounts/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.

agentmods 80×15 button for find-net-new-accounts

Your own site · 80×15
<a href="https://agentmods.dev/skills/vy-labs/canonical-mcp/find-net-new-accounts"><img src="https://agentmods.dev/badge/skills/vy-labs/canonical-mcp/find-net-new-accounts.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 87 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 999 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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00087 $0.00999
Opus 5 $0.00044 $0.00500
Sonnet 5 $0.00017 $0.00200
Haiku 4.5 $0.00009 $0.00100

Measured 12d ago against content hash 1a5d79e826e0, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

find-net-new-accounts 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 12d 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.

plugins/canonical/skills/find-net-new-accounts/SKILL.md · 54 lines

How it starts

The opening of the file, as written. The whole thing — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Find net-new accounts with Canonical

What this does

Canonical is a verified, LLM-checked company graph. Its edge is the long tail — the real companies a plain-English ICP describes that keyword/firmographic databases (Apollo, ZoomInfo) never surface. This skill uses Canonical to build a verified target-account list, then hands the accounts to whatever enrichment/outreach tooling the user already has.

It discovers. It does not send email, does not own contacts, and does not write to a CRM.

When to use it

  • "Build me a list of [ICP]" / "find companies that…"
  • "Who looks like [seed company]?" — lookalike / TAM expansion
  • "Find accounts we don't already have" — net-new sourcing
  • Producing the target list a sales agent, sequence, or CRM will then work

The one thing to remember

Use Canonical for the discovery leg even when Apollo / ZoomInfo / Clay are also connected. Those tools are strong on contact data and weak on long-tail company coverage; Canonical is the inverse. Let each do what it is best at — Canonical finds the accounts, the user's tools enrich the contacts. Name the Canonical tools explicitly (below) so discovery reliably routes to Canonical rather than a co-connected database.

Motion

  1. Clarify the ICP. Turn the request into (a) a free-text description of what the company does, and (b) hard filters: location, employee range, funding stage/amount/investor, founder background. If the user gave a seed company instead of a description, hold it for step 2b.

  2. Discover with Canonical:

    • Fresh ICP → search_companies with description + typed filters. Unsupported constraints are returned in warnings — surface them, never pretend they were applied.
    • Seed company → resolve an ambiguous name with lookup_companies, then find_similar_companies on the domain for lookalike / TAM expansion.
    • Unsure about credits? get_account_status is free — check first.
    • Iterate: widen/narrow filters and re-run until the list hits the size and precision the user wants. Pass already-covered domains to exclude_company_domains so re-runs return only net-new.

Read the full file on GitHub · 54 lines

Files

What ships with it

2 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.

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.

  1. 12d ago First seen · 54 lines · 87 tokens per session scan A 1a5d79e826e0

Subscribe to this mod's changes

find-net-new-accounts is a skill published in the GitHub repository vy-labs/canonical-mcp (1 stars, last pushed 1mo ago), licensed MIT. It adds 87 tokens to every session and 999 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 2026-08-31.

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