company-domain-resolver

company-domain-resolver is a skill for Claude Code, Codex from swan-gtm/gtm-skills. It costs 149 tokens per session (1,183 once invoked), scanned A, original, MIT.

A company-name lookup workflow that finds likely website domains for a list of companies. It returns the original rows with the matched name, domain, confidence level, and names that need human review.

In plain words
What is it for?
Use it with company names from CSV files, CRM exports, event lists, or pasted text when you need domains for later research or data cleanup.
Why use it?
It removes the manual work of finding the correct website before contact enrichment, duplicate checking, or importing companies into a CRM. Confidence labels help expose uncertain matches instead of hiding them.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it with company names from CSV files, CRM exports, event lists, or pasted text when you need domains for later research or data cleanup.

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Install with agentmods
npx agentmods add skills/swan-gtm/gtm-skills/company-domain-resolver
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 swan-gtm/gtm-skills --skill company-domain-resolver
Clone the repo
git clone --depth 1 https://github.com/swan-gtm/gtm-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.

agentmods badge for company-domain-resolver

README.md
[![agentmods](https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/company-domain-resolver/github.svg)](https://agentmods.dev/skills/swan-gtm/gtm-skills/company-domain-resolver)
Your own site
<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/company-domain-resolver"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/company-domain-resolver/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 company-domain-resolver

Your own site · 80×15
<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/company-domain-resolver"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/company-domain-resolver.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 149 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,183 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00149 $0.01183
Opus 5 $0.00075 $0.00592
Sonnet 5 $0.00030 $0.00237
Haiku 4.5 $0.00015 $0.00118

Measured 12d ago against content hash 8fd2103c0128, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

company-domain-resolver 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.

skills/boaz-descalo/company-domain-resolver/SKILL.md · 90 lines

How it starts

The opening of the file, as written. The whole thing — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Applies whenever a company list has names but no domains and the next step — enrichment, dedupe, CRM import — needs them. Produces the same list back with resolved_domain, matched_name, a confidence label per row, and the rows that need human review called out, never a bare domain list.

The play

  1. Get the input and confirm the column. Accept a CSV/TSV, plain-text list, or names pasted in chat. For a CSV, name the company column explicitly. Over ~40 names, tell the user each name is one HTTP call and confirm before starting.

  2. Resolve via the free Clearbit autocomplete endpointhttps://autocomplete.clearbit.com/v1/companies/suggest?query=<name>. No key, no auth, up to 5 matches best-guess first, [] on no match. It is an undocumented typeahead, not a contracted API: keep volume modest, pace requests, and read references/endpoint-behavior.md before your first batch — it covers the two failure modes that silently corrupt lists.

  3. For one or two names, query directly with your agent's web-fetch tool and report matches inline, flagging when several results share a name.

  4. For a batch, use the resolver script in references/resolver-script.md. It runs plan → fetch → merge: dedupes on a normalized key, strips legal suffixes before querying, then scores every row. If the environment blocks direct HTTP, the script's plan output lists the URLs so you can fetch them with your agent's web-fetch tool (5–8 in parallel per batch) and feed the responses back to the merge step.

  5. Score every row. Confidence labels and what they mean:

    confidence meaning review
    exact name and domain both match, ranked first no
    strong exactly one result's domain matches the name no
    plausible same slug on several TLDs; picked the gTLD spot-check
    ambiguous several same-named companies, different domains yes
    weak nothing lined up; top result is a guess yes
    none empty response — not in the index yes
  6. Report, don't just deliver a file. Give counts per confidence level and list the specific rows needing review with their alternatives. If weak plus none exceed a third of the list, say so plainly — the input is likely full of internal shorthand, DBA names, or companies the index doesn't cover. For those rows, offer (don't auto-run) a web search on "<company name>" official site as a second pass.

  7. Hand off. Domains are the join key for enrichment and CRM dedupe. For people-level data (titles, emails, profiles), pass the resolved domains to a real enrichment step — this endpoint returns name and domain only.

Read the full file on GitHub · 90 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 · 90 lines · 149 tokens per session scan A 8fd2103c0128

Subscribe to this mod's changes

company-domain-resolver is a skill published in the GitHub repository swan-gtm/gtm-skills (150 stars, last pushed 2d ago), licensed MIT. It adds 149 tokens to every session and 1,183 once invoked, about $0.0007 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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