A data acquisition and checking workflow for research papers about computer-based learning systems. It downloads the data and verifies that it is complete, usable, consistent, and organized for evaluation.

Use it to clone referenced code repositories, download datasets, organize them for each evaluation setting, and record verification results in the paper-filter results.

A paper review may only confirm that download links exist without actually checking the data. This workflow performs the deeper checks needed before building benchmark tasks from the papers.

data-check — Skill for Claude Code by FrontisAI/NatureBench

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 FrontisAI/NatureBench --skill data-check

Clone the repo

git clone --depth 1 https://github.com/FrontisAI/NatureBench

Made for: Claude Code.

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/frontisai/naturebench/data-check/github.svg)](https://agentmods.dev/skills/frontisai/naturebench/data-check)

Your own site

<a href="https://agentmods.dev/skills/frontisai/naturebench/data-check"><img src="https://agentmods.dev/badge/skills/frontisai/naturebench/data-check/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/frontisai/naturebench/data-check"><img src="https://agentmods.dev/badge/skills/frontisai/naturebench/data-check.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 72 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 3,482 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector pass 7 Sept 2026

How audits are shown

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.

Model	Per session	Once invoked
Fable 5.1	$0.00072	$0.03482
Opus 5.5	$0.00029	$0.01393
Sonnet 5.5	$0.00014	$0.00696
Haiku 4.5	$0.00007	$0.00348

Measured 2mo ago against content hash ca93db8283b4, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

data-check scanned grade A with 1 finding 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 2mo ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/inspect_data.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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

- Acquisition is **idempotent**: before downloading/copying/generating, check whether the target already exists. Use `wget -c` to resume partial downloads. If a session ends mid-download (e.g., a multi-day dataset exceed

naturegym/.agents/skills/data-check/SKILL.md · 223 lines

How it starts

The opening of the file, as written. The whole thing — 223 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Data-Check Skill

Downloads repositories and datasets referenced by papers that passed paper-filter, then performs Level 4 deep verification — checking acquisition integrity, initial state, separability, consistency, per-setting completeness. Results are written back into filter_result.json, providing a verified data foundation for downstream task package construction.

Context

paper-filter's Level 3 (Data Completeness) is lightweight — it checks link accessibility, views directory structures, and reads READMEs, but does not actually download data. This skill performs the actual download and deep verification, then writes results back into filter_result.json as Level 4.

Terminology Reference

This skill uses the same core definitions as paper-filter. See references/core_definitions.md for formal definitions of:

T = (A, Data, M, S, B): The ML task tuple
D_dev: Development data space (all prior information available to the original authors for solving the problem)
D_eval = (X_test, Y_ref): Evaluation data space with formal input/reference decomposition
Process Completeness: The three conditions (Initial State, Evaluation Loop, Evaluation Alignment) verified per evaluation setting

Input Requirements

Before invoking this skill, provide:

Paper Folder Path: Directory containing paper files and filter results
Output Directory: Directory for downloaded repositories and organized data

Paper folder must contain:

{paper_id}.pdf: Original paper PDF
{paper_id}.html: HTML version of the paper
filter_result.json (paper-filter output, must have final_result.passed == true)
preprocessed/ directory (paper-preprocess output: text.md, links.json, figures/, tables/)

The original PDF and HTML serve as additional reference when preprocessed data does not contain sufficient detail.

Output

This skill updates the existing filter_result.json (in the Paper Folder) by:

Read the full file on GitHub · 223 lines

Files

What ships with it

6 files 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.

2mo ago First seen · 223 lines · 72 tokens per session scan A ca93db8283b4

Subscribe to this mod's changes

data-check is a skill published in the GitHub repository FrontisAI/NatureBench (137 stars, last pushed 17d ago), licensed MIT. It adds 72 tokens to every session and 3,482 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Research Scientific

data-check next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
data-checkFrontisAI/NatureBench	Skill	137	17d ago	MIT	A	72	Claude Code
instrument-data-to-allotropeanthropics/knowledge-work-plugins	Skill	35k	2d ago	Apache-2.0	A	123	Claude Code
exploratory-data-analysisK-Dense-AI/scientific-agent-skills	Skill	56k	4d ago	MIT	A	83	Claude Code
matlabK-Dense-AI/scientific-agent-skills	Skill	56k	4d ago	MIT	A	44	Claude Code
phylogeneticsK-Dense-AI/scientific-agent-skills	Skill	56k	4d ago	MIT	A	71	Claude Code, Codex
research-engineerdavila7/claude-code-templates	Skill	33k	today	MIT	A	43	Claude Code, Codex
mapping-to-snomedmaziyarpanahi/openmed	Skill	5.8k	yesterday	Apache-2.0	A	205	Claude Code

data-check