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.
npx agentmods add skills/frontisai/naturebench/data-checknpx skills add FrontisAI/NatureBench --skill data-checkgit clone --depth 1 https://github.com/FrontisAI/NatureBenchWrote 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.
[](https://agentmods.dev/skills/frontisai/naturebench/data-check)<a href="https://agentmods.dev/skills/frontisai/naturebench/data-check"><img src="https://agentmods.dev/badge/skills/frontisai/naturebench/data-check.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00072 | $0.03482 |
| Opus 5 | $0.00036 | $0.01741 |
| Sonnet 5 | $0.00014 | $0.00696 |
| Haiku 4.5 | $0.00007 | $0.00348 |
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 3d ago.
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 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 paperfilter_result.json(paper-filter output, must havefinal_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:
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.
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.
- 3d ago First seen · 223 lines · 72 tokens per session scan A ca93db8283b4
data-check is a skill published in the GitHub repository FrontisAI/NatureBench (106 stars, last pushed 3d ago), licensed MIT. It adds 72 tokens to every session and 3,482 once invoked, about $0.0004 per session on Opus 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.
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