data-analysis

Tools for examining CSV and JSON data, including summary statistics, row filtering, grouped totals, and relationships between numeric columns.

In plain words
What is it for?
Use it to describe a table, select matching rows, calculate sums or averages by group, count records, or compare numeric columns.
Why use it?
It removes the need to write common data-cleaning and calculation code for each dataset.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/exboys/skilllite/data-analysis
Any agent
npx skills add EXboys/skilllite --skill data-analysis
Clone the repo
git clone --depth 1 https://github.com/EXboys/skilllite

Made for: Claude Code, Codex.

Per session 22 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 177 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00022 $0.00177
Opus 5 $0.00011 $0.00088
Sonnet 5 $0.00004 $0.00035
Haiku 4.5 $0.00002 $0.00018

Measured 2d ago against content hash eebda22cb7d6, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

data-analysis 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.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/main.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

.skills/data-analysis/SKILL.md · 27 lines

What it actually says

Data Analysis Skill

Perform statistical analysis on tabular data using pandas and numpy.

Supported Operations

  • describe: Summary statistics (mean, std, min, max, etc.)
  • filter: Filter rows by column conditions
  • aggregate: Group-by aggregation (sum, mean, count, etc.)
  • correlate: Correlation matrix between numeric columns

Usage

{"operation": "describe", "data": [[1,2],[3,4]], "columns": ["a","b"]}
Files

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.

Changes

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.

  1. 2d ago First seen · 27 lines · 22 tokens per session scan A eebda22cb7d6

Subscribe to this mod's changes

data-analysis is a skill published in the GitHub repository EXboys/skilllite (167 stars, last pushed 6d ago), licensed MIT. It adds 22 tokens to every session and 177 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 2026-08-30.

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