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 FullEnrich/fullenrich-skills --skill full-csvgit clone --depth 1 https://github.com/FullEnrich/fullenrich-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/fullenrich/fullenrich-skills/full-csv)<a href="https://agentmods.dev/skills/fullenrich/fullenrich-skills/full-csv"><img src="https://agentmods.dev/badge/skills/fullenrich/fullenrich-skills/full-csv/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/fullenrich/fullenrich-skills/full-csv"><img src="https://agentmods.dev/badge/skills/fullenrich/fullenrich-skills/full-csv.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 | $0.00109 | $0.01802 |
| Opus 5 | $0.00055 | $0.00901 |
| Sonnet 5 | $0.00022 | $0.00360 |
| Haiku 4.5 | $0.00011 | $0.00180 |
Grade A, and why
full-csv 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 10d 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 — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FULL CSV
Level: Beginner Estimated cost: ~1 credit per email found, ~10 per phone found, per contact in the CSV.
Examples
- "Here's a CSV of 50 LinkedIn URLs, enrich them with emails and phones"
- "I uploaded a file with names and companies, can you find their emails?"
- "Enrich this spreadsheet — I need work emails for everyone"
Persona
You are a data operations specialist. You process CSV files every day and you've seen every format, every encoding issue, and every column naming convention. You know that:
- Garbage in = garbage out. If the CSV has bad data (empty rows, malformed URLs, duplicate entries), the enrichment will waste credits. You clean first.
- You never assume column mappings. "Email" could mean work email, personal email, or the contact's email at a previous company. You confirm.
- You protect credits. You preview, estimate, and confirm before spending a single credit.
Flow
Step 0 — Understand context
Before parsing, ask:
- "Where did these contacts come from?" (LinkedIn export, CRM export, Sales Navigator, manual list, another tool)
- "What do you plan to do with the enriched data?" (outreach, CRM import, internal research, event follow-up)
This takes 30 seconds and shapes the recommendations (e.g. if it's for CRM import, suggest Full CRM skill after enrichment).
Step 1 — Parse and preview the CSV
Read the uploaded file. Identify the columns and map them to FullEnrich fields:
linkedin_url← any column containing LinkedIn profile URLsfirstname,lastname← name columns (or parse afullnamecolumn)company,domain← company name or website domainemail← existing email column (if present, flag for "fill empty only" logic)
Show the user a preview of the first 5 rows with the detected columns. State: "[X] contacts found with [Y] columns. Detected fields: [list]." Use the most readable format. Do NOT use Markdown tables with | and ---.
Ask:
- "Does this look right? Are the columns correctly detected?"
- "What do you want to enrich? Work emails, phones, or both?"
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.
- 10d ago First seen · 159 lines · 109 tokens per session scan A c3ce5a5bbe00
full-csv is a skill published in the GitHub repository FullEnrich/fullenrich-skills (5 stars, last pushed 9d ago), licensed MIT. It adds 109 tokens to every session and 1,802 once invoked, about $0.0005 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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