messy-data-cleanroom

messy-data-cleanroom is a skill for Codex from Emily2040/data-science-agent-skills. It costs 104 tokens per session (1,024 once invoked), scanned A, original, no licence file.

A guide for planning how to clean messy datasets while keeping the original evidence intact. Reversible cleaning means every change can be reviewed or undone.

In plain words
What is it for?
Use it to inspect poor-quality data and create documented, reviewable cleaning plans.
Why use it?
It helps prevent hidden data changes that could remove useful evidence or make results hard to explain.

Skill for Codex

Written for Codex: agents/openai.yaml present.

Good fit Use it to inspect poor-quality data and create documented, reviewable cleaning plans.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/emily2040/data-science-agent-skills/messy-data-cleanroom
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.

Any agent
npx skills add Emily2040/data-science-agent-skills --skill messy-data-cleanroom
Clone the repo
git clone --depth 1 https://github.com/Emily2040/data-science-agent-skills

Made for: Codex.

Wrote 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.

agentmods badge for messy-data-cleanroom

README.md
[![agentmods](https://agentmods.dev/badge/skills/emily2040/data-science-agent-skills/messy-data-cleanroom/github.svg)](https://agentmods.dev/skills/emily2040/data-science-agent-skills/messy-data-cleanroom)
Your own site
<a href="https://agentmods.dev/skills/emily2040/data-science-agent-skills/messy-data-cleanroom"><img src="https://agentmods.dev/badge/skills/emily2040/data-science-agent-skills/messy-data-cleanroom/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.

agentmods 80×15 button for messy-data-cleanroom

Your own site · 80×15
<a href="https://agentmods.dev/skills/emily2040/data-science-agent-skills/messy-data-cleanroom"><img src="https://agentmods.dev/badge/skills/emily2040/data-science-agent-skills/messy-data-cleanroom.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 104 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,024 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin unknown 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 5.1 $0.00104 $0.01024
Opus 5 $0.00052 $0.00512
Sonnet 5 $0.00021 $0.00205
Haiku 4.5 $0.00010 $0.00102

Measured 12d ago against content hash c31bf9e05024, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

messy-data-cleanroom 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 12d ago.

The scan reads SKILL.md. This mod also ships 4 executable files (scripts/checklist_score.py, scripts/profile_table.py, scripts/quick_validate_skill.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.

data-science-agent-skills/messy-data-cleanroom/SKILL.md · 92 lines

The source is not reproduced here

No licence file

A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.

Read it on GitHub

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. 12d ago First seen · 92 lines · 104 tokens per session scan A c31bf9e05024

Subscribe to this mod's changes

messy-data-cleanroom is a skill published in the GitHub repository Emily2040/data-science-agent-skills (16 stars, last pushed 3mo ago), with no licence file. It adds 104 tokens to every session and 1,024 once invoked, about $0.0005 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.

Related

Other skills, from other repositories

agent-platform-rag-engine-management

Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…

google/skills · 85 tokens

agent-platform-model-registry

Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.

google/skills · 60 tokens

foundry-config-setup

Resolve missing setup caused by a hardcoded Foundry project endpoint or model in a sample. Use when a sample fails because it uses a placeholder/hardcoded projectendpoint (for example "https://your-project.services.ai.azure.com") or a hardcoded model instead of reading them from the environment.

microsoft/agent-framework · 65 tokens

google-cloud-solution-agentic-analytics-spark-knowledge-catalog

Discovers requirements and generates guidance to design and deploy a governed, secure agentic-analytics solution for data that's distributed across Google Cloud, other cloud providers, or on-premises. Data that's outside Google Cloud (such as data from Databricks, Snowflake, Salesforce, SAP, or Oracle systems) is…

google/skills · 138 tokens

training-check

Interactively monitor training metrics from the current Codex session, periodically checking WandB or fallback logs for NaN, divergence, plateaus, and broken runs.

wanshuiyin/Auto-claude-code-research-in-sleep · 35 tokens

nemo-automodel-launcher-config

Configure NeMo AutoModel job launches for interactive runs, Slurm clusters, and SkyPilot cloud execution.

NVIDIA/skills · 30 tokens