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 masteranime/n8n-claude-skills --skill enrichment-waterfallgit clone --depth 1 https://github.com/masteranime/n8n-claude-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/masteranime/n8n-claude-skills/enrichment-waterfall)<a href="https://agentmods.dev/skills/masteranime/n8n-claude-skills/enrichment-waterfall"><img src="https://agentmods.dev/badge/skills/masteranime/n8n-claude-skills/enrichment-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/masteranime/n8n-claude-skills/enrichment-waterfall"><img src="https://agentmods.dev/badge/skills/masteranime/n8n-claude-skills/enrichment-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 | $0.00161 | $0.01642 |
| Opus 5 | $0.00081 | $0.00821 |
| Sonnet 5 | $0.00032 | $0.00328 |
| Haiku 4.5 | $0.00016 | $0.00164 |
Grade A, and why
enrichment-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 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 — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Enrichment Waterfall for n8n
A waterfall = try cheapest/fastest vendor first, fall through to more expensive/accurate vendors only when the previous fails. This is how Clay, Clearbit, and every production enrichment pipeline actually works.
The core pattern
Input (name, email, or domain)
↓
Vendor 1 (cheap, fast, ~60% hit rate) — e.g., Hunter.io
↓ IF no match
Vendor 2 (medium cost, ~80% cumulative) — e.g., Apollo
↓ IF no match
Vendor 3 (expensive / LLM extract, ~95% cumulative) — e.g., SerpAPI + LLM
↓ IF no match
Dead letter: log as "unenrichable"
At each step, a hit short-circuits the rest. You pay only for what the cheap vendors miss.
Ordering: cost × accuracy × rate limit
Order vendors by expected cost per successful enrichment, not sticker price. Calculate:
effective_cost = price_per_call / hit_rate
Example for email-from-name+company:
| Vendor | Price/call | Hit rate | Effective cost |
|---|---|---|---|
| Hunter.io | $0.004 | 55% | $0.007 |
| Apollo bulk | $0.01 | 75% | $0.013 |
| SerpAPI + LLM extract | $0.02 | 90% | $0.022 |
| Manual LinkedIn scrape | $0.05 | 60% | $0.083 |
Order: Hunter → Apollo → SerpAPI+LLM → dead letter. Effective cost per enriched lead ≈ $0.012 vs $0.083 if you'd started with the scraper.
n8n implementation
Structure
1. Trigger (Webhook / Schedule / Manual)
2. Set — normalize input (lowercase email, strip whitespace, extract domain)
3. MySQL / Google Sheets — check cache (was this already enriched in last 30 days?)
4. IF cache hit → return cached → END
5. HTTP Request: Hunter.io
6. IF match found → Set enriched data → merge back → END
7. HTTP Request: Apollo (on Hunter miss)
8. IF match → merge → END
9. HTTP Request: SerpAPI
10. Information Extractor (LangChain) — extract contact from SERP results
11. IF match → merge → END
12. MySQL insert — dead letter table
Critical configuration
Each HTTP Request node needs:
continueOnFail: true— so one vendor's 500 doesn't kill the pipelineretry.maxTries: 2withretry.waitBetweenTries: 3000- Timeout: 10s. Waterfalls with 6 vendors at 30s timeouts = 3-minute-per-lead worst case
- Auth via n8n credentials, never inline
What ships with it
1 file 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 · 163 lines · 161 tokens per session scan A a9d909577f46
enrichment-waterfall is a skill published in the GitHub repository masteranime/n8n-claude-skills (32 stars, last pushed 4mo ago), licensed MIT. It adds 161 tokens to every session and 1,642 once invoked, about $0.0008 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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