bio-workbench

bio-workbench is a skill for Claude Code, Codex from poplarity/dsh-science-workbench. It costs 147 tokens per session (4,067 once invoked), scanned A, original, MIT.

A set of rules for running reproducible bioinformatics analyses, which study biological data with software. It uses small self-contained scripts, recorded inputs and outputs, environment details, and version history so results can be repeated.

In plain words
What is it for?
Creating bioinformatics projects, running analyses and producing figures, recording where each result came from, and revising figures through tracked feedback and reruns.
Why use it?
Scientific results are difficult to trust or recreate when the code, data, settings, and software environment are not recorded together. These conventions keep that information linked to each result.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is python $SKILL/scripts/validate_figure.py --backend python code/cell_XXXX.py.

Good fit Creating bioinformatics projects, running analyses and producing figures, recording where each result came from, and revising figures through tracked feedback and reruns.

Compare 6 skills from other repositories ↓
Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/poplarity/dsh-science-workbench
agentmods
npx agentmods add skills/poplarity/dsh-science-workbench/bio-workbench

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 bio-workbench

README.md
[![agentmods](https://agentmods.dev/badge/skills/poplarity/dsh-science-workbench/bio-workbench/github.svg)](https://agentmods.dev/skills/poplarity/dsh-science-workbench/bio-workbench)
Your own site
<a href="https://agentmods.dev/skills/poplarity/dsh-science-workbench/bio-workbench"><img src="https://agentmods.dev/badge/skills/poplarity/dsh-science-workbench/bio-workbench/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 bio-workbench

Your own site · 80×15
<a href="https://agentmods.dev/skills/poplarity/dsh-science-workbench/bio-workbench"><img src="https://agentmods.dev/badge/skills/poplarity/dsh-science-workbench/bio-workbench.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 147 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,067 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 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 5.1 $0.00147 $0.04067
Opus 5.5 $0.00059 $0.01627
Sonnet 5.5 $0.00029 $0.00813
Haiku 4.5 $0.00015 $0.00407

Measured 9d ago against content hash 120e4fa9b6c4, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

bio-workbench 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 9d 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.

skills/bio-workbench/SKILL.md · 201 lines

How it starts

The opening of the file, as written. The whole thing — 201 lines — stays where its author put it; the contents beside it link to each section on GitHub.

生信分析工作台约定(dsh-science-workbench)

这是 agent 在执行生信分析时必须遵守的约定。核心原则:每个产物都可溯源、可重放——回答"这张图 = 哪段代码 + 哪些输入 + 什么环境 + 什么参数/种子",并能重跑。

1. 项目布局

每个分析项目是一个目录,结构固定:

<workspace>/bio-projects/<name>/
├─ manifest.json        # 唯一事实来源(账本):cells + artifacts + provenance + 反馈
├─ environment.lock     # 环境快照(interpreter 版本 + pip freeze)
├─ code/                # 每个 cell 一个自包含脚本 cell_0001.py / cell_0001_v2.py ...
├─ data/                # 输入数据(引用或副本)
├─ figures/             # 只放图:.svg / .pdf / .tiff / .png
├─ results/             # 非图产物:tsv/csv/bed/npz + source data
└─ .git/                # 项目自动 git(每次 cell 跑完/反馈 自动本地 commit,不 push)

项目里不保留 QA 产物。 审计(alignment / collision)必须照跑,但它们的产物是 scratch: alignment overlay 是测量示意图(每个可比面板一个矩形 + PASS 页脚),collision overlay 标的是 可疑的文字/描边碰撞。两者都不是图,摆在项目里就是污染分析结果,还会招来误解点击。 一律写到项目外的临时目录,例如:

import os, tempfile
QADIR = os.path.join(tempfile.gettempdir(), 'nature-figure-qa',
                     os.path.basename(os.path.abspath('.')))   # cwd = 项目根
os.makedirs(QADIR, exist_ok=True)
print(f'QA scratch dir (outside the project): {QADIR}')

并且绝不把 QA 文件写进 outputs 声明。踩过的坑:collision 审计在 0 冲突时不写 overlay PDF, 但 outputs 里仍列着那个路径,于是账本里留下一条文件不存在的幽灵 artifact,在 UI 里点开就是空的。

2. cell 契约(脚本头声明块)

每个 cell 脚本顶部有一段由工具生成的声明头,声明即契约:

# @cell: cell_0001
# @title: TSS profile
# @language: python
# @seed: 42
# @params: {"bin": 50, "upstream": 3000}
# @inputs: ["data/peaks.bed"]
# @outputs: ["figures/tss_profile.png"]

规则:

  • 图必须写到 figures/ 下(相对项目根路径),这样运行后能自动被发现并登记 provenance。
  • 审计产物写到项目外的临时目录,绝不写进项目(见 §1)。figures/ 被自动扫描并在 UI 里当图展示;alignment overlay 每面板一个矩形 + PASS 页脚,放进去会被误认成坏图。
  • QA 路径不要出现在 outputs 里,否则会留下文件不存在的幽灵 artifact。
  • 非图的中间产物(tsv/csv/bed 等)通过 outputs 显式声明,放 results/。
  • 脚本要自包含:显式读输入、写输出;cwd = 项目根;用相对路径。

3. manifest 记账(唯一事实来源)

{
  "schemaVersion": 1,
  "name": "...", "root": "...",
  "environment": {"language": "python", "interpreter": "3.12.x", "lockFile": "environment.lock"},
  "cells": [ { "id", "title", "script", "language", "params", "seed", "inputs",
               "status": "ok|error", "artifacts": [], "ranAt", "stdoutTail", "stderrTail", "derivedFrom" } ],
  "artifacts": [ { "path", "kind": "figure|data|file", "producedBy", "inputHashes",
                   "outputHash", "params", "seed", "env", "createdAt", "feedback": [], "derivedFrom" } ]
}

Read the full file on GitHub · 201 lines

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. 9d ago Changed · +105 lines · +31 tokens per session 120e4fa9b6c4
  2. 1mo ago First seen · 96 lines · 116 tokens per session scan A 2c3254267fa2

Subscribe to this mod's changes

bio-workbench is a skill published in the GitHub repository poplarity/dsh-science-workbench (11 stars, last pushed 9d ago), licensed MIT. It adds 147 tokens to every session and 4,067 once invoked, about $0.0006 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 2026-08-31.

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