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 seb1n/awesome-ai-agent-skills --skill crm-data-enrichmentgit clone --depth 1 https://github.com/seb1n/awesome-ai-agent-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/seb1n/awesome-ai-agent-skills/crm-data-enrichment)<a href="https://agentmods.dev/skills/seb1n/awesome-ai-agent-skills/crm-data-enrichment"><img src="https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/crm-data-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/seb1n/awesome-ai-agent-skills/crm-data-enrichment"><img src="https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/crm-data-enrichment.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 | $0.00049 | $0.01467 |
| Opus 5 | $0.00024 | $0.00733 |
| Sonnet 5 | $0.00010 | $0.00293 |
| Haiku 4.5 | $0.00005 | $0.00147 |
Grade A, and why
crm-data-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 9d 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 — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CRM Data Enrichment
Enrich company and contact records in your CRM with up-to-date firmographic, technographic, and demographic data. This skill identifies gaps in existing records, sources enrichment data from available signals, merges and deduplicates entries, validates accuracy, and updates fields — giving sales teams cleaner data for segmentation, lead routing, and personalized outreach.
Workflow
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Identify Gaps in CRM Data — Audit the target CRM records to surface missing or outdated fields. Common gaps include annual revenue, employee count, industry classification, technology stack, direct phone numbers, verified email addresses, and current job titles. Prioritize fields that directly impact lead scoring and routing logic.
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Source Enrichment Data — Pull data from company websites, LinkedIn profiles, SEC filings, job postings, DNS/TXT records (for tech stack detection), press releases, and third-party data providers. Cross-reference at least two independent sources per data point to reduce single-source risk.
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Match and Merge Records — Align enrichment data to CRM records using deterministic matching on domain, email, or unique identifiers, supplemented by fuzzy matching on company name and location. Deduplicate records where enrichment reveals two CRM entries represent the same entity, preserving the most complete record as the primary.
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Validate Accuracy — Apply confidence scoring to each enriched field. Flag data points sourced from a single unverified origin as low-confidence. Cross-check revenue and headcount against recent earnings reports or LinkedIn company pages. Validate email deliverability and phone connectivity where possible.
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Update CRM Fields — Write validated enrichment data back to the CRM, respecting field-level permissions and avoiding overwrites of manually verified data. Log all changes with timestamps and source attribution for audit trails. Trigger downstream automations (lead scoring recalculation, territory reassignment) based on newly populated fields.
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.
- 9d ago First seen · 119 lines · 49 tokens per session scan A 738103281e83
crm-data-enrichment is a skill published in the GitHub repository seb1n/awesome-ai-agent-skills (179 stars, last pushed 1mo ago), licensed MIT. It adds 49 tokens to every session and 1,467 once invoked, about $0.0002 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 2026-09-03.
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