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 skills add FrontisAI/NatureBench --skill task-buildgit 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/task-build)<a href="https://agentmods.dev/skills/frontisai/naturebench/task-build"><img src="https://agentmods.dev/badge/skills/frontisai/naturebench/task-build/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.
<a href="https://agentmods.dev/skills/frontisai/naturebench/task-build"><img src="https://agentmods.dev/badge/skills/frontisai/naturebench/task-build.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00045 | $0.03337 |
| Opus 5.5 | $0.00018 | $0.01335 |
| Sonnet 5.5 | $0.00009 | $0.00667 |
| Haiku 4.5 | $0.00005 | $0.00334 |
Grade A, and why
task-build 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 2mo 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 — 252 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Task Build Skill
Build a structured ML benchmark task package from a CNS paper that has completed the three preprocessing stages (paper-preprocess → paper-filter → data-check).
Input Requirements
Before invoking this skill, provide:
- Paper Folder Path: Directory containing the paper and all prior processing results
- Output Directory: Where to build the final task package (can be same as paper folder or a new location)
Paper folder must contain:
{paper_id}.pdfand{paper_id}.html: Original paper filespreprocessed/: paper-preprocess output (text.md,links.json,figures/,tables/)filter_result.json: Combined output from paper-filter + data-check (must havefinal_result.data_check_passed == true)data/: Acquired data organized per setting (data-check output)repositories/: Cloned repositories (data-check output, reference only)
Paper folder may also contain:
data_verify_result.json(optional): Independent verification report from data-verify. When present, usealgorithm_boundary.file_classificationsas supplementary reference for file role judgment (e.g., distinguishing algorithm artifacts from initial state, identifying external resources/oracles). When it conflicts with filter_result.json, verify against actual repository code and data to determine the correct classification.
Output Structure
{output_dir}/
├── problem/ # Solver-visible package
│ ├── data/ # Solver-visible data
│ │ ├── {setting_1}/ # One directory per evaluation setting
│ │ │ ├── [d_dev files] # Training data, validation data, pretrained models, etc.
│ │ │ └── [x_test files] # Test inputs
│ │ └── {setting_2}/
│ │ └── ...
│ ├── data_description.md # Technical data documentation
│ └── README.md # Task definition document
├── evaluation/ # Evaluator package (hidden from solver)
│ ├── evaluator.py # Automated evaluation script (reads from workspace/output/)
│ └── ground_truth/ # Reference answers
│ ├── {setting_1}/
│ │ └── [y_ref files]
│ └── {setting_2}/
│ └── [y_ref files]
├── environment/ # Execution environment
│ ├── Dockerfile.v3 # Task-specific Docker image definition
│ └── packages.json # Package manifest for automated verification
└── metadata.json # Task metadata and performance baselines
What ships with it
8 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.
- references/base_packages.json 2.7 KB
- references/data_description_guide.md 4.0 KB
- references/data_organization_guide.md 14 KB
- references/Dockerfile.base.v3 4.8 KB
- references/environment_guide.md 44 KB
- references/evaluator_guide.md 13 KB
- references/metadata_guide.md 10 KB
- references/readme_guide.md 8.3 KB
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 · 252 lines · 45 tokens per session scan A afdcb93266b5
task-build is a skill published in the GitHub repository FrontisAI/NatureBench (137 stars, last pushed 17d ago), licensed MIT. It adds 45 tokens to every session and 3,337 once invoked, about $0.0002 per session on Opus 5.5. 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.
task-build compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| task-buildFrontisAI/NatureBench | Skill | 137 | 17d ago | MIT | A | 45 | Claude Code |
| arboretoK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 55 | Claude Code, Codex |
| deepspot-mK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 70 | Claude Code |
| torchdrugK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 64 | Claude Code |
| pyhealthK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 84 | Claude Code, Codex |
| nemo-mbridge-perf-expert-parallel-overlapNVIDIA/skills | Skill | 3.9k | yesterday | Apache-2.0 | A | 56 | Claude Code |
| ml-training-recipesOrchestra-Research/AI-Research-SKILLs | Skill | 14k | 26d ago | MIT | A | 88 | Claude Code |