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 SELAT-AI/selat-skills --skill gtm-enrichment-deepgit clone --depth 1 https://github.com/SELAT-AI/selat-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/selat-ai/selat-skills/gtm-enrichment-deep)<a href="https://agentmods.dev/skills/selat-ai/selat-skills/gtm-enrichment-deep"><img src="https://agentmods.dev/badge/skills/selat-ai/selat-skills/gtm-enrichment-deep/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/selat-ai/selat-skills/gtm-enrichment-deep"><img src="https://agentmods.dev/badge/skills/selat-ai/selat-skills/gtm-enrichment-deep.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 | $0.00135 | $0.01474 |
| Opus 5 | $0.00068 | $0.00737 |
| Sonnet 5 | $0.00027 | $0.00295 |
| Haiku 4.5 | $0.00014 | $0.00147 |
Grade A, and why
gtm-enrichment-deep 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
gtm-enrichment-deep
When To Use
Use when the user has a lead's email address (optionally a name) and wants a comprehensive GTM enrichment: person identity + LinkedIn, company profile, and funding history. Apollo people enrichment is the primary person source, Hunter is the company source, and Apollo organization enrichment fills funding gaps via fallback. Every step is a MPP on Tempo payment through the SELAT Router — every step settles through the SELAT Router.
Workflow
- Install:
selat skill install gtm-enrichment-deep - Run:
selat skill run gtm-enrichment-deep --email [email protected] [--firstName Jane] [--lastName Doe] [--company Acme] [--domain acme.com] - The CLI compiles each manifest step into a
selat-paypayment, runs them in order, and prints a per-step ✓/✗ summary.
Steps (in order):
- Step 1 — Apollo
POST /apollo/people-enrichment— MPP on Tempo — primary person data: identity, title, LinkedIn URL ($0.0084 live price). This single call also covers what was previously a separate LinkedIn-match fallback — same endpoint, so the two steps are merged. - Step 2 — Hunter
POST /hunter/company-enrichment— MPP on Tempo — primary company profile ($0.01365 live price). - Step 3 — Apollo
POST /apollo/org-enrichment— MPP on Tempo fallback — run only if Step 2 returned no funding data ($0.0084 live price; also returnstechnology_namesand headcount fields).
Derive domain from the email (text after @) when not supplied. Apollo/Hunter are primary; the Apollo org-enrichment fallback fills funding gaps. Track the source (apollo | hunter) and confidence (high = upstream field, medium = fallback, low = inferred) per field. AI/B2B-SaaS classification is not returned by any endpoint — infer it from the company description/keywords and mark it low confidence.
Inputs And Outputs
| Param | Required | Default | Description |
|---|---|---|---|
email |
yes | — | Lead's email address, e.g. [email protected] |
domain |
no | derived from email | Company domain, e.g. acme.com |
firstName |
no | empty | Lead's first name (improves Apollo match) |
lastName |
no | empty | Lead's last name (improves Apollo match) |
company |
no | empty | Company name (improves Apollo match) |
What ships with it
3 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 · 70 lines · 135 tokens per session scan A 513e2fe37f42
gtm-enrichment-deep is a skill published in the GitHub repository SELAT-AI/selat-skills (2 stars, last pushed today), licensed Apache-2.0. It adds 135 tokens to every session and 1,474 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 2026-08-31.
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