csv-workbench

A small toolkit for reading CSV files, which are plain-text tables separated by commas, and producing concise numeric summaries.

In plain words
What is it for?
Use it to check a CSV’s columns, calculate numeric summaries, and report assumptions when the file is missing or malformed.
Why use it?
It provides a quick way to inspect a table and calculate requested totals or other aggregates without setting up a larger data-analysis system.

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/openai/openai-agents-python/csv-workbench
Any agent
npx skills add openai/openai-agents-python --skill csv-workbench
Clone the repo
git clone --depth 1 https://github.com/openai/openai-agents-python

Made for: Claude Code, Codex.

Per session 17 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 121 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 $0.00017 $0.00121
Opus 5 $0.00009 $0.00060
Sonnet 5 $0.00003 $0.00024
Haiku 4.5 $0.00002 $0.00012

Measured yesterday against content hash 96bbebd83d34, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

csv-workbench 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 yesterday.

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.

examples/tools/skills/csv-workbench/SKILL.md · 21 lines

What it actually says

CSV Workbench

Use this skill when the user asks for quick analysis of tabular data.

Workflow

  1. Inspect the CSV schema first (head, python csv.DictReader, or both).
  2. Compute requested aggregates with a short Python script.
  3. Return concise results with concrete numbers and units when available.

Constraints

  • Prefer Python stdlib for portability.
  • If data is missing or malformed, state assumptions clearly.
  • Keep the final answer short and actionable.
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. yesterday First seen · 21 lines · 17 tokens per session scan A 96bbebd83d34

Subscribe to this mod's changes

csv-workbench is a skill published in the GitHub repository openai/openai-agents-python (29,075 stars, last pushed 4d ago), licensed MIT. It adds 17 tokens to every session and 121 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.