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/filter-verifynpx skills add FrontisAI/NatureBench --skill filter-verifygit 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/filter-verify)<a href="https://agentmods.dev/skills/frontisai/naturebench/filter-verify"><img src="https://agentmods.dev/badge/skills/frontisai/naturebench/filter-verify.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 | $0.00036 | $0.01139 |
| Opus 5 | $0.00018 | $0.00570 |
| Sonnet 5 | $0.00007 | $0.00228 |
| Haiku 4.5 | $0.00004 | $0.00114 |
Grade A, and why
filter-verify 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 4d 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.
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.
How it starts
The opening of the file, as written. The whole thing — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Filter-Verify Skill
Verify filter_result.json produced by paper-filter. Three core responsibilities:
- Check whether the pass/reject judgment is correct
- Validate that task_info fields are accurate and complete
- Produce an actionable corrections list to drive filter_result.json updates
Input Requirements
Paper directory must contain:
preprocessed/text.md: Full paper textpreprocessed/links.json: Extracted linkspreprocessed/figures/: Extracted figures and tablesfilter_result.json: paper-filter output
Context
The pipeline extracts ML tasks from papers to build benchmark challenges. The goal is not to reproduce the paper's algorithm — it is to test whether an AI agent (the "Solver") can independently solve the same ML problem using any method, potentially surpassing the original. The Solver receives the same initial data as the paper's authors, without knowing the paper or its algorithm. Its results are scored against ground truth using the paper's metrics, with the paper's reported scores as baselines for comparison. The benchmark must be fair: include all legitimate initial data (excluding any would make the task unfairly harder) while withholding Algorithm A's outputs (including any would leak solutions). filter-verify ensures only papers with genuine algorithmic innovation space and meaningful baselines enter the pipeline.
Workflow
Phase 0: Load & Summarize
- Read
filter_result.jsonin full - Read references/check_rules.md
- Read references/output_schema.md
- Read
preprocessed/text.mdin full - Read
preprocessed/links.json - Scan
preprocessed/figures/for main result tables and figures
Extract and record the following from the paper text (NOT from filter_result.json):
- What the paper's core contribution is
- What Algorithm A is (the method proposed by the authors in this paper)
- Which tables/figures contain main results, what metrics each uses, and which datasets are covered
- The evaluation method used (fixed split / random split / K-fold CV)
What ships with it
2 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.
- 4d ago First seen · 86 lines · 36 tokens per session scan A eadfc0c24f23
filter-verify is a skill published in the GitHub repository FrontisAI/NatureBench (111 stars, last pushed 3d ago), licensed MIT. It adds 36 tokens to every session and 1,139 once invoked, about $0.0002 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.
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