Menu
Analyze CSV files in /mnt/data and return concise numeric summaries.
The review-first prompt is selected by default. You can switch to a direct command or download a local copy.
Read SKILL.md and any companion files shown by SkillsMP before deciding whether to install.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
A direct command skips the review prompt. Inspect the source before running it.
npx skills add https://github.com/openai/openai-agents-python --skill csv-workbenchThe command stays on one line. Scroll horizontally to inspect it before copying.
Prefer a local copy? Download the files currently available to SkillsMP.
Showing SKILL.md
| name | csv-workbench |
| description | Analyze CSV files in /mnt/data and return concise numeric summaries. |
Use this skill when the user asks for quick analysis of tabular data.
head, python csv.DictReader, or both).Based on SOC occupation classification
Analyze main branch implementation and configuration to find missing, incorrect, or outdated documentation in docs/. Use when asked to audit doc coverage, sync docs with code, or propose doc updates/structure changes. Only update English docs under docs/** and never touch translated docs under docs/ja, docs/ko, or docs/zh. Provide a report and ask for approval before editing docs.
Perform a risk-tiered zero-base final review loop before an implementation is declared complete. Use only when the user explicitly invokes $implementation-final-review or repository instructions authorize automatic invocation after implementation. Audit the complete merge-base diff for requirement fit, contract-surface coverage, await-boundary and lifecycle gaps, released compatibility, security, protocol, and persistence boundaries, unnecessary complexity, package and generated public surfaces, and adversarial test coverage; use compact self-contained reviewer packets and two concurrent no-history independent reviewers per round, defer broad final repository verification until review is clean and observable host capacity is available, preserve clean evidence for unchanged semantic components, close repeated root-cause groups instead of accumulating local patches, and enforce bounded review cycles in one task-global ledger.
Start and carry an explicitly invoked openai-agents-python implementation through a fresh isolated worktree and a local PR-ready handoff. Fetch the latest origin/main, keep task changes uncommitted, replay them onto the latest main before final review, run applicable verification and $implementation-final-review, use $pr-draft-summary to generate the complete PR draft and branch name, then create one clean local commit with takeover provenance when applicable. Use only when the user explicitly invokes this skill; never push, open a PR, or mutate GitHub.
