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 agentmods add skills/ericwang915/pythonclaw/csv_analyzernpx skills add ericwang915/PythonClaw --skill csv_analyzergit clone --depth 1 https://github.com/ericwang915/PythonClawWhat 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 | $0.00064 | $0.00547 |
| Opus 5 | $0.00032 | $0.00273 |
| Sonnet 5 | $0.00013 | $0.00109 |
| Haiku 4.5 | $0.00006 | $0.00055 |
Grade A, and why
csv_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 2d 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.
What it actually says
CSV Analyzer Skill
Analyze tabular data files (CSV, TSV, Excel) using pandas.
When to Use
✅ USE this skill when:
- "Show me what's in data.csv"
- "First 20 rows of sales.xlsx"
- "Statistics for revenue column"
- "Filter rows where age > 30"
- "Average sales by region"
- User wants to explore, summarize, filter, or aggregate tabular data
When NOT to Use
❌ DON'T use this skill when:
- Database queries (SQL) → use database tools
- Writing new CSV/Excel files → use code or spreadsheet tools
- Non-tabular formats (JSON, XML, etc.) → use appropriate parsers
Usage/Commands
python {skill_path}/analyze.py PATH [command] [options]
Commands:
info(default) — column types, shape, missing valueshead— first N rows (default 10)stats— descriptive statistics for numeric columnsquery— filter rows with a pandas query expressiongroupby— group-by aggregationcolumns— list column names and types
Options:
--rows N— number of rows for head (default 10)--query "col > 100"— pandas query expression--groupby COL— column to group by--agg mean|sum|count|min|max— aggregation function (default: mean)--format json— output as JSON--columns "col1,col2"— select specific columns
Examples
- "Show me what's in data.csv" →
python {skill_path}/analyze.py data.csv info - "First 20 rows of sales.xlsx" →
python {skill_path}/analyze.py sales.xlsx head --rows 20 - "Average sales by region" →
python {skill_path}/analyze.py data.csv groupby --groupby region --columns sales --agg mean
Notes
- Install dependencies:
pip install pandas openpyxl - openpyxl required for Excel (.xlsx) support
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.
- 2d ago First seen · 62 lines · 64 tokens per session scan A dc1985682157
csv_analyzer is a skill published in the GitHub repository ericwang915/PythonClaw (41 stars, last pushed 24d ago), licensed MIT. It adds 64 tokens to every session and 547 once invoked, about $0.0003 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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