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 swan-gtm/gtm-skills --skill company-domain-resolvergit clone --depth 1 https://github.com/swan-gtm/gtm-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/swan-gtm/gtm-skills/company-domain-resolver)<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.
<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>- NVIDIA SkillSpector pass
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 | $0.00149 | $0.01183 |
| Opus 5 | $0.00075 | $0.00592 |
| Sonnet 5 | $0.00030 | $0.00237 |
| Haiku 4.5 | $0.00015 | $0.00118 |
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.
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
-
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.
-
Resolve via the free Clearbit autocomplete endpoint —
https://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 readreferences/endpoint-behavior.mdbefore your first batch — it covers the two failure modes that silently corrupt lists. -
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.
-
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. -
Score every row. Confidence labels and what they mean:
confidence meaning review exactname and domain both match, ranked first no strongexactly one result's domain matches the name no plausiblesame slug on several TLDs; picked the gTLD spot-check ambiguousseveral same-named companies, different domains yes weaknothing lined up; top result is a guess yes noneempty response — not in the index yes -
Report, don't just deliver a file. Give counts per confidence level and list the specific rows needing review with their alternatives. If
weakplusnoneexceed 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 siteas a second pass. -
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.
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.
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.
- 12d ago First seen · 90 lines · 149 tokens per session scan A 8fd2103c0128
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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