task-build

A builder that turns a suitable neuroscience research paper and its prepared data into a structured machine-learning benchmark package. The package includes the task description, scoring code, metadata, and a Docker-based environment.

In plain words
What is it for?
Use it after paper preprocessing, suitability filtering, and data checking to build the problem, evaluator, metadata, data layout, and Dockerfile.
Why use it?
It organizes the paper’s materials into a repeatable task that another solver can run and be scored against.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/frontisai/naturebench/task-build
Any agent
npx skills add FrontisAI/NatureBench --skill task-build
Clone the repo
git clone --depth 1 https://github.com/FrontisAI/NatureBench

Made for: Claude Code, Codex.

Per session 45 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,337 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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.

ModelPer sessionOnce invoked
Fable 5 $0.00045 $0.03337
Opus 5 $0.00023 $0.01669
Sonnet 5 $0.00009 $0.00667
Haiku 4.5 $0.00005 $0.00334

Measured 3d ago against content hash afdcb93266b5, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 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.

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.

naturegym/.claude/skills/task-build/SKILL.md · 252 lines

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:

  1. Paper Folder Path: Directory containing the paper and all prior processing results
  2. 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}.pdf and {paper_id}.html: Original paper files
  • preprocessed/: paper-preprocess output (text.md, links.json, figures/, tables/)
  • filter_result.json: Combined output from paper-filter + data-check (must have final_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, use algorithm_boundary.file_classifications as 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

Read the full file on GitHub · 252 lines

Files

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.

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.

  1. 3d ago First seen · 252 lines · 45 tokens per session scan A afdcb93266b5

Subscribe to this mod's changes

task-build is a skill published in the GitHub repository FrontisAI/NatureBench (106 stars, last pushed 3d ago), licensed MIT. It adds 45 tokens to every session and 3,337 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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