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 KirKruglov/claude-skills-kit --skill csv-data-analyzergit clone --depth 1 https://github.com/KirKruglov/claude-skills-kitWrote 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/kirkruglov/claude-skills-kit/csv-data-analyzer)<a href="https://agentmods.dev/skills/kirkruglov/claude-skills-kit/csv-data-analyzer"><img src="https://agentmods.dev/badge/skills/kirkruglov/claude-skills-kit/csv-data-analyzer/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/kirkruglov/claude-skills-kit/csv-data-analyzer"><img src="https://agentmods.dev/badge/skills/kirkruglov/claude-skills-kit/csv-data-analyzer.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 | $0.00132 | $0.01615 |
| Opus 5.5 | $0.00053 | $0.00646 |
| Sonnet 5.5 | $0.00026 | $0.00323 |
| Haiku 4.5 | $0.00013 | $0.00161 |
Grade A, and why
csv-data-analyzer 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 1mo 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 — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CSV Data Analyzer
This skill analyzes CSV files with business data for non-technical users through a conversational question flow — without code, formulas, or external tools. Claude reads the CSV directly, profiles its structure, asks one clarifying question about your goal, and delivers numbered insights with a plain-language interpretation.
Input:
- A CSV file accessible in the Cowork workspace (uploaded or in the selected folder)
Output:
- Markdown response with: dataset profile, numbered key insights, data snapshot table, plain-language interpretation, and follow-up suggestions
Language Detection
Detect the user's language from their message:
- If Russian (or contains Cyrillic): respond in Russian
- If English (or other Latin-script language): respond in English
- If ambiguous: respond in the language of the trigger phrase used
Instructions
Step 1: Locate and Read the CSV File
-
Identify the CSV file from the user's message
- If a file path or filename is provided: use it directly
- If no path given: look for a CSV file in the Cowork workspace folder
- If no CSV file found: stop and report — "I don't see a CSV file. Please upload the file or confirm it's in your selected Cowork folder, then try again."
-
Read the file content as plain text
- If the file cannot be parsed as CSV (binary, corrupted, wrong format): stop and report — "This file doesn't appear to be a valid CSV. Please check the file format and try again."
Step 2: Profile the Dataset
-
Count rows and columns (exclude the header row from row count)
- If only 1 row exists (header only, no data): stop and report — "This CSV has no data rows — only headers. Please check the file and try again with a populated dataset."
-
Identify column names from the first row
- If no header row detected (all rows look like data rows): assign generic names Column 1, Column 2, … and note: "I couldn't detect column headers — I'll use generic names."
-
Infer data type for each column: numeric, text, date, or boolean
- If a column contains mixed types (numbers and text): note the inconsistency; treat column as text; recommend cleaning before numeric analysis
What ships with it
4 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.
- 1mo ago First seen · 133 lines · 132 tokens per session scan A 78987651393c
csv-data-analyzer is a skill published in the GitHub repository KirKruglov/claude-skills-kit (22 stars, last pushed 3mo ago), licensed MIT. It adds 132 tokens to every session and 1,615 once invoked, about $0.0005 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 2026-08-30.
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