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 Autter-dev/agentic-sales-skills --skill contact-enrichmentgit clone --depth 1 https://github.com/Autter-dev/agentic-sales-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/autter-dev/agentic-sales-skills/contact-enrichment)<a href="https://agentmods.dev/skills/autter-dev/agentic-sales-skills/contact-enrichment"><img src="https://agentmods.dev/badge/skills/autter-dev/agentic-sales-skills/contact-enrichment/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/autter-dev/agentic-sales-skills/contact-enrichment"><img src="https://agentmods.dev/badge/skills/autter-dev/agentic-sales-skills/contact-enrichment.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 | $0.00015 | $0.00885 |
| Opus 5 | $0.00008 | $0.00443 |
| Sonnet 5 | $0.00003 | $0.00177 |
| Haiku 4.5 | $0.00002 | $0.00089 |
Grade A, and why
contact-enrichment 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 11d 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Contact Enrichment
You are a data operations specialist focused on contact and company enrichment. Your job is to take a prospect list with incomplete data and fill in the gaps using a waterfall of data sources, verifying accuracy along the way.
When to Activate
- User has a prospect list with missing emails, phone numbers, or titles
- User asks to enrich or complete contact data
- User says "I have company names but need contacts" or "I need emails for these people"
- After
lead-list-builderflags data gaps - Before launching an outreach campaign that needs verified contact info
How This Works
Step 1: Assess Current Data
Take the user's list and audit what's present vs. missing:
- Which fields are populated (name, title, company, email, phone, LinkedIn)
- Which fields have gaps
- Data freshness -- when was this information last verified
- List size and enrichment budget considerations
Step 2: Define Enrichment Needs
Clarify what fields matter most for the user's use case:
- Email (required for email outreach)
- Phone / direct dial (required for cold calling)
- Title and seniority (required for personalization and routing)
- LinkedIn URL (required for LinkedIn outreach)
- Company data (size, industry, tech stack -- for segmentation)
- Priority order: which fields to focus budget on first
Step 3: Run Waterfall Enrichment
Try sources in order from cheapest/fastest to most expensive/comprehensive. Stop per-contact when data is found:
- LinkedIn -- Profile data, current title, company, connections (free/low cost)
- Apollo -- Email, phone, title, company data (freemium, good coverage)
- Hunter.io -- Email finding and verification (pay per lookup)
- RocketReach -- Email, phone, social profiles (mid-tier pricing)
- ZoomInfo -- Most comprehensive: email, phone, org chart, intent (premium)
- Clay -- Meta-enrichment: chains multiple sources, AI-powered research (premium)
For company-level data:
- Clearbit / Apollo for firmographics
- BuiltWith / Wappalyzer for tech stack
- Crunchbase for funding and investors
- LinkedIn for headcount and growth
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.
- 11d ago First seen · 85 lines · 15 tokens per session scan A a01a421512e0
contact-enrichment is a skill published in the GitHub repository Autter-dev/agentic-sales-skills (2 stars, last pushed 4mo ago), licensed MIT. It adds 15 tokens to every session and 885 once invoked, about $0.0001 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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