Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/SELAT-AI/selat-skillsnpx agentmods add skills/selat-ai/selat-skills/enrich-waterfallWrote 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/enrich-waterfall)<a href="https://agentmods.dev/skills/selat-ai/selat-skills/enrich-waterfall"><img src="https://agentmods.dev/badge/skills/selat-ai/selat-skills/enrich-waterfall/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/enrich-waterfall"><img src="https://agentmods.dev/badge/skills/selat-ai/selat-skills/enrich-waterfall.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 | $0.00149 | $0.03381 |
| Opus 5.5 | $0.00060 | $0.01352 |
| Sonnet 5.5 | $0.00030 | $0.00676 |
| Haiku 4.5 | $0.00015 | $0.00338 |
Grade A, and why
enrich-waterfall 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 22d 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 — 254 lines — stays where its author put it; the contents beside it link to each section on GitHub.
enrich-waterfall
When To Use
Use this skill for a full, fixed 17-call evidence bundle on one known person and their employer. It cross-checks identity and contact data, compares company records, retrieves hiring and recent-news signals, reads a supplied LinkedIn and X/Twitter profile, and verifies a supplied work email.
Despite the historical enrich-waterfall name, this is not a waterfall or a
menu: selat skill run always executes every step, and the CLI has no step
selector, stop-on-match, or tier escalation. Use this skill only when the user
wants the entire bundle and supplies every required identifier. If the user has
only one partial identifier, wants a cheaper subset, or asks for true
stop-on-match escalation, use a smaller skill, hand-built single selat-pay
calls after their own probe and approval, or free discovery instead.
All calls are read-only. The only side effect is the explicitly approved USDC spend. Use personal and contact data only for a legitimate, lawful purpose; do not use the skill for harassment, phishing, sensitive profiling, or unsolicited bulk outreach.
Workflow
-
Install the vetted recipe:
selat skill install enrich-waterfall -
Collect all eight inputs:
email,firstName,lastName,domain,company,linkedinUrl,organizationId, andxHandle. None has a default, so the CLI refuses to run without all eight. Confirm that they describe the same person and company. Do not combine convenient placeholders from unrelated identities. -
Probe all 17 payment challenges for free before wallet setup or spending:
SELAT_ROUTER_URL=https://router.selat.ai \ selat skill verify ~/.config/selat/skills/enrich-waterfall \ --email <known-work-email> \ --firstName <first-name> \ --lastName <last-name> \ --domain <bare-domain> \ --company "<company-name>" \ --linkedinUrl <person-linkedin-url> \ --organizationId <apollo-organization-id> \ --xHandle <x-username-without-at> \ --live-probe
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.
- 22d ago Changed · +104 lines · -39 tokens per session f20e81da796e
- 1mo ago Changed · -5 tokens per session 726f048d2bf2
- 1mo ago First seen · 150 lines · 193 tokens per session scan A 348eb6a27168
enrich-waterfall is a skill published in the GitHub repository SELAT-AI/selat-skills (2 stars, last pushed today), licensed Apache-2.0. It adds 149 tokens to every session and 3,381 once invoked, about $0.0006 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.
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