onescience-modelscope-publish

onescience-modelscope-publish is a skill for Claude Code, Codex from onescience-ai/OneSkills. It costs 81 tokens per session (5,039 once invoked), scanned A, original, MIT.

A tool for preparing a reproduced research model for ModelScope, a platform for sharing machine-learning models. It organizes files into the platform’s expected directory structure and creates upload guidance.

In plain words
What is it for?
It helps scan reproduction outputs, create directories such as model, weight, scripts, and conf, copy files into their categories, generate platform templates, and provide an upload command.
Why use it?
It removes the manual work of sorting model files and checking that the package has the expected folders and templates. It does not upload the model itself.

Skill for Claude CodeCodex

Part of the oneskills plugin — 18 skills shipped together

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/onescience-ai/oneskills/onescience-modelscope-publish
Any agent
npx skills add onescience-ai/OneSkills --skill onescience-modelscope-publish
Clone the repo
git clone --depth 1 https://github.com/onescience-ai/OneSkills

Made for: Claude Code, Codex.

Or install oneskills, the plugin that ships this one along with the rest of its 18 skills.

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 onescience-modelscope-publish

README.md
[![agentmods](https://agentmods.dev/badge/skills/onescience-ai/oneskills/onescience-modelscope-publish.svg)](https://agentmods.dev/skills/onescience-ai/oneskills/onescience-modelscope-publish)
Your own site
<a href="https://agentmods.dev/skills/onescience-ai/oneskills/onescience-modelscope-publish"><img src="https://agentmods.dev/badge/skills/onescience-ai/oneskills/onescience-modelscope-publish.svg" alt="Measured on agentmods" height="20"></a>
Per session 81 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,039 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 $0.00081 $0.05039
Opus 5 $0.00041 $0.02520
Sonnet 5 $0.00016 $0.01008
Haiku 4.5 $0.00008 $0.00504

Measured 5d ago against content hash ea39b198db58, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

onescience-modelscope-publish 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 5d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/model_audit_checker.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/onescience-modelscope-publish/SKILL.md · 300 lines

How it starts

The opening of the file, as written. The whole thing — 300 lines — stays where its author put it; the contents beside it link to each section on GitHub.

输入获取方式

本技能支持两种输入方式:

  1. 上下文 handoff(默认):从调用方传入的 step_handoff 获取任务信息。
  2. 文件 handoff(autonomous_mode):从 .onescience/handoff/step_{step_id}.yaml 读取任务信息。执行后,将结果写入 .onescience/handoff/step_{step_id}_result.yaml

启动时优先检查 .onescience/handoff/ 目录是否存在对应的交接文件;若存在则使用文件模式,否则使用上下文模式。

文件交接格式参见 skills/onescience-orchestrator/references/file_handoff_contract.md

OneScience ModelScope 模型发布

职责边界

本技能负责将论文复现后的模型产物按 ModelScope(魔搭)仓库规范重组为标准目录结构,引导用户推送模型。不执行 modelscope upload 命令本身

  • 作为执行技能:由 onescience-orchestrator 调用,接收标准化输入,返回标准化输出。
  • 核心职责:产物扫描归类、目标目录创建、文件复制组织、空目录检查、平台模板生成、上传命令输出。
  • 不负责:模型训练、推理验证、ModelScope 账号认证、token 管理、手动分类纠错后的重新打包。

输入输出接口

输入(从 onescience-orchestrator 接收)

pack_handoff:
  source_dir: <论文复现产物根目录(绝对路径)>
  model_name: <ModelScope 上的模型名称,如 "MARIO">
  target_dir: <目标输出目录,可选,默认为当前工作目录>
  modelscope_org: <ModelScope 组织名,可选,默认为 "OneScience">

输出(返回给 onescience-orchestrator)

execution_result:
  skill: onescience-modelscope-publish
  status: <success | partial | failed>
  artifacts:
    target_dir: <目标目录绝对路径>
    directories_created: [model, weight, scripts, conf]
    files_copied_count: <归类复制的文件总数>
    files_excluded_count: <排除的文件总数>
    files_by_category:
      model: <归入 model/ 的文件列表>
      weight: <归入 weight/ 的文件列表>
      scripts: <归入 scripts/ 的文件列表>
      conf: <归入 conf/ 的文件列表>
      excluded: <排除(未复制)的文件列表>
    templates_generated: [configurations.json, README.md]
  observation:
    empty_directories: [<内容为空的子目录名称列表>]
    warnings: [<告警信息列表>]
    script_availability:
      train: <found | missing>
      inference: <found | missing>
      evaluation: <found | missing>
    upload_command: "modelscope upload {org}/{model_name} {target_dir}/{model_name} --token ***"
  notes: <补充说明>

核心流程

阶段一:输入校验

  1. 校验 source_dir 是否存在且为有效目录路径。
    • 若不存在,返回 status=failed,提示用户确认产物目录路径。
  2. 校验 source_dir 是否有可访问的文件。
    • 若目录为空,返回 status=failed,提示用户确认产物目录是否包含文件。
  3. model_name 为空,尝试从 source_dir 的父目录名推断,若仍无法推断,询问用户指定。

Read the full file on GitHub · 300 lines

Files

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.

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. 5d ago First seen · 300 lines · 81 tokens per session scan A ea39b198db58

Subscribe to this mod's changes

onescience-modelscope-publish is a skill published in the GitHub repository onescience-ai/OneSkills (20 stars, last pushed 22d ago), licensed MIT. It adds 81 tokens to every session and 5,039 once invoked, about $0.0004 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

admet_genetic

ADMET-guided genetic molecule optimization workflow from seed SMILES; use when the agent needs to build or run an RDKit/SA-Score/ADMET-AI GA pipeline for molecule optimization, enforce molecule lineage logs, render optimization-history HTML dashboards, and write candidate triage reports.

PKU-YuanGroup/OpenAI4S · 63 tokens

bioprobench

Score an LLM's biological-protocol reasoning on the BioProBench benchmark: protocol QA, step ordering, error detection, protocol generation, and LLM-judged error reasoning; or generate the responses.

PKU-YuanGroup/OpenAI4S · 46 tokens

sweep

Non-interactive end-to-end pipeline — auto-configure program.md (accept defaults), run judge+refine loop (up to 3 iterations), then run the campaign. Single command from goal to result.

Borda/AI-Rig · 45 tokens

analysis-workflow

Organize multi-step scientific analyses into reproducible, self-contained modules. Use for workflows such as QC→PCA→DEG→GSEA that produce scripts, inputs, figures, tables, and methods. Creates a stable module layout, records exact inputs/parameters/package and database versions in each module README, keeps large data…

xuzhougeng/wisp-science · 81 tokens

public-data-access

Plan, configure, validate, and document portable public-bioinformatics data acquisition. Use for GEO/GSE/GDS, SRA/ENA, TCGA/GDC, GTEx, DepMap, public expression matrices, raw reads, release files, manifests, resumable downloads, and reusable local caches. Keep the workflow provider-neutral: DepMap is one optional…

xuzhougeng/wisp-science · 83 tokens

bioinformatics-scientist

Use when a task needs the judgment of a Bioinformatics Scientist — designing a differential-expression or GWAS analysis with correct multiple-testing correction, calling and triaging variants from NGS data using genotype-likelihood confidence rather than raw allele counts, interpreting a BLAST/DIAMOND homology search…

wonsukchoi/domain-experts · 168 tokens