dpgen-simplify

dpgen-simplify is a skill for Claude Code, Codex from jinzhezenggroup/computational-chemistry-agent-skills. It costs 66 tokens per session (2,380 once invoked), scanned A, original, LGPL-3.0.

A workflow for simplifying DeePMD datasets by removing repeated or redundant training data.

In plain words
What is it for?
Use it to prepare configuration files, run DP-GEN simplify, and inspect the resulting datasets.
Why use it?
It helps reduce unnecessary data and organize experiments that compare different simplification settings.

Skill for Claude CodeCodex

Which agent this was written for is unclear — body not stored (licence); the path alone says nothing.

Good fit Use it to prepare configuration files, run DP-GEN simplify, and inspect the resulting datasets.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/jinzhezenggroup/computational-chemistry-agent-skills/dpgen-simplify
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.

Any agent
npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill dpgen-simplify
Clone the repo
git clone --depth 1 https://github.com/jinzhezenggroup/computational-chemistry-agent-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for dpgen-simplify

README.md
[![agentmods](https://agentmods.dev/badge/skills/jinzhezenggroup/computational-chemistry-agent-skills/dpgen-simplify/github.svg)](https://agentmods.dev/skills/jinzhezenggroup/computational-chemistry-agent-skills/dpgen-simplify)
Your own site
<a href="https://agentmods.dev/skills/jinzhezenggroup/computational-chemistry-agent-skills/dpgen-simplify"><img src="https://agentmods.dev/badge/skills/jinzhezenggroup/computational-chemistry-agent-skills/dpgen-simplify/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.

agentmods 80×15 button for dpgen-simplify

Your own site · 80×15
<a href="https://agentmods.dev/skills/jinzhezenggroup/computational-chemistry-agent-skills/dpgen-simplify"><img src="https://agentmods.dev/badge/skills/jinzhezenggroup/computational-chemistry-agent-skills/dpgen-simplify.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 66 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,380 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin unknown 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 5.1 $0.00066 $0.02380
Opus 5 $0.00033 $0.01190
Sonnet 5 $0.00013 $0.00476
Haiku 4.5 $0.00007 $0.00238

Measured 13d ago against content hash 721b364f4b31, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

dpgen-simplify 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 13d 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.

machine-learning-potentials/dpgen-simplify/SKILL.md · 342 lines

The source is not reproduced here

Licensed LGPL-3.0

The repository is licensed LGPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.

Read it on GitHub

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. 13d ago First seen · 342 lines · 66 tokens per session scan A 721b364f4b31

Subscribe to this mod's changes

dpgen-simplify is a skill published in the GitHub repository jinzhezenggroup/computational-chemistry-agent-skills (138 stars, last pushed today), licensed LGPL-3.0. It adds 66 tokens to every session and 2,380 once invoked, about $0.0003 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.

Related

Other skills, from other repositories

flux-analyzer

Analyse FBA flux distributions to extract biological insights. Covers gene essentiality, phenotypic phase planes, flux sampling, pathway-level aggregation, secretion product prediction, and production of publication- quality figures.

aiming-lab/AutoResearchClaw · 44 tokens

gsmm-builder

Build or load a genome-scale metabolic model (GSMM) using COBRApy. Covers loading from BIGG, constructing minimal models from scratch, setting medium constraints, and exporting validated .json model files.

aiming-lab/AutoResearchClaw · 45 tokens

stat-result-validator

Validate statistical research outputs for formulation quality, method-to- problem alignment, theory presence, experimental evidence, fair comparison, artifact completeness, and final-claim consistency.

aiming-lab/AutoResearchClaw · 36 tokens

meta-analysis

Statistical methods for combining results across multiple studies. Use when aggregating cross-study or cross-experiment results.

aiming-lab/AutoResearchClaw · 25 tokens

mixed-precision

Use FP16/BF16 mixed precision to accelerate training and reduce memory. Use when optimizing GPU performance.

aiming-lab/AutoResearchClaw · 25 tokens

adme-property-predictor

Predict ADME (Absorption, Distribution, Metabolism, Excretion) properties for drug candidates using cheminformatics models and molecular descriptors. Evaluates drug-likeness, bioavailability, and pharmacokinetic profile to guide lead optimization and candidate selection in drug discovery.

LeoYeAI/openclaw-master-skills · 61 tokens