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/verify-applynpx skills add FrontisAI/NatureBench --skill verify-applygit 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/verify-apply)<a href="https://agentmods.dev/skills/frontisai/naturebench/verify-apply"><img src="https://agentmods.dev/badge/skills/frontisai/naturebench/verify-apply.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 | $0.00056 | $0.00795 |
| Opus 5 | $0.00028 | $0.00398 |
| Sonnet 5 | $0.00011 | $0.00159 |
| Haiku 4.5 | $0.00006 | $0.00080 |
Grade A, and why
verify-apply 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 5d 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.
allowed-tools: Read, Grep, Glob, Write, Bash(rm *), Bash(mkdir *), Bash(find *), Bash(ls *), Bash(git clone *), Bash(GIT_SSL_NO_VERIFY=1 git clone *), Bash(wget *), Bash(curl *), Bash(tar *), Bash(unzip *), Bash(gunzip * How it starts
The opening of the file, as written. The whole thing — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Verify-Apply Skill
Apply verification corrections to filter_result.json. Reads a verification result JSON, applies every correction/finding, and writes a change log.
Core Principle: Pipeline Continuity
After apply, filter_result.json must be directly consumable by the next pipeline stage — no re-runs of previous skills required.
- filter-verify apply → output must match paper-filter output structure (ready for data-check)
- data-verify apply → output must match data-check output structure (ready for task-build)
This means any new or structurally modified evaluation setting must have the same completeness as what the corresponding upstream skill would produce.
Note: If any concepts, operations, requirements, or explanations are unclear during implementation or adjustment, please refer to the documentation in the
paper-filteranddata-checkskills for detailed guidance.
Input Requirements
Before invoking this skill, provide:
- Source type:
filter-verifyordata-verify - Verification: Path to verification JSON
- Target: Path to
filter_result.jsonto be modified
Workflow
Phase 1: Load
- Read the verification JSON
- Read the target
filter_result.json - Read references/apply_rules.md — use the section matching the specified source type
Phase 2: Apply Corrections
For filter-verify source
Apply every correction from checks with status: "fail".
- Iterate
checks[].corrections[], apply each usingpath,action,recommendedas defined in apply_rules.md § Field-Level - If
verdict.overrideis not null, apply the judgment override as defined in apply_rules.md § Judgment Override
For data-verify source
Apply every finding from checks with status: "fail" or status: "warning".
- V1/V3/V5 findings: field-level updates using
field+recommended_valueas defined in apply_rules.md § V1/V3/V5 - V2 findings: structural operations on
evaluation_settings[]/rejected_settings[]as defined in apply_rules.md § V2 - V4 findings: file operations on
data/directory as defined in apply_rules.md § V4
What ships with it
1 file 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.
- 5d ago First seen · 74 lines · 56 tokens per session scan A 410401c6304d
verify-apply is a skill published in the GitHub repository FrontisAI/NatureBench (112 stars, last pushed today), licensed MIT. It adds 56 tokens to every session and 795 once invoked, about $0.0003 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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