bio-workbench

bio-workbench is a skill for Claude Code, Codex from poplarity/dsh-science-workbench. It costs 116 tokens per session (1,619 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.

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 with agentmods
npx agentmods add skills/poplarity/dsh-science-workbench/bio-workbench
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 poplarity/dsh-science-workbench --skill bio-workbench
Clone the repo
git clone --depth 1 https://github.com/poplarity/dsh-science-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 116 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,619 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 $0.00116 $0.01619
Opus 5 $0.00058 $0.00809
Sonnet 5 $0.00023 $0.00324
Haiku 4.5 $0.00012 $0.00162

Measured 10d ago against content hash 2c3254267fa2, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, 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 10d 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 · 96 lines

How it starts

The opening of the file, as written. The whole thing — 96 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/             # 图产物(.png 等,可配 .data.tsv 原始绘图数据)
└─ .git/                # 项目自动 git(每次 cell 跑完/反馈 自动本地 commit,不 push)

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/(相对项目根路径),这样运行后能自动被发现并登记 provenance。
  • 非图的中间产物(tsv/csv/bed 等)通过 outputs 显式声明。
  • 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" } ]
}
  • 每个 artifact 的 outputHash 与每个 inputHashes 都是 SHA-256,保证可复现性证据。
  • feedback 挂在 artifact 上;derivedFrom 记录版本派生关系(v1 → v2)。

4. 出图 → 反馈 → 重画循环

  1. 出图:调用 bio_run_cell(含 title + code + params + seed + inputs)。
  2. 视觉自检(Tier 0,默认):程序化检查——渲染是否成功(exit code)、是否有图产出、dpi/尺寸合理性。人(用户)是默认的眼睛:图永远先给用户看。
  3. 反馈:用户对图提出反馈("改 x 轴 / 换配色 / 加 p 值"),用 bio_add_feedback 把反馈作为结构化记录挂到对应 artifact 上(进 manifest + git commit)。
  4. 重画:调用 bio_rerun_cellcellId + editedCode),生成版本化新 cell(cell_0001_v2),重跑、重新发现图、登记新 artifact 并记录 derivedFrom 派生关系。

Read the full file on GitHub · 96 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. 10d 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 (9 stars, last pushed 14d ago), licensed MIT. It adds 116 tokens to every session and 1,619 once invoked, about $0.0006 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 2026-08-31.

Related

Other skills, from other repositories

structure-prediction

Protein structure prediction from sequence. ESMFold-based, single GPU, no MSA needed. Predicts 3D structures with pLDDT confidence scores for drug discovery targets.

synthetic-sciences/openscience · 42 tokens

ito-inference

Inspect the availability of model serving on a completed Itô compute booking and, when the canonical backend becomes available, hand off an explicitly confirmed serving manifest. Use after ito-compute has booked GPU nodes and the user asks for an OpenAI-compatible endpoint, ito-serve, hosted Kimi, or self-hosted…

gongyijie85/dsh-ecc · 80 tokens

ito-training

Inspect the availability of ML training on a completed Itô compute booking and, when the canonical backend becomes available, hand off an explicitly confirmed training manifest. Use after ito-compute has booked GPU nodes and the user wants pre-training, fine-tuning, or RL on that metal. ECC implements no training…

gongyijie85/dsh-ecc · 68 tokens

ito-compute

Query live GPU inventory, submit an authenticated Itô fixed-rate RFQ, inspect RFQ or procurement status, revoke device credentials, and run explicitly gated node qualification through the separately installed canonical CLI. Use when a user asks to find H100/H200 capacity, request a fixed compute rate, check Itô…

gongyijie85/dsh-ecc · 90 tokens

Jupyter Live Kernel

Guides notebook-first analysis with reproducible kernels, inspectable data loading, and explicit promotion paths back into durable code.

agentic-in/elephant-agent · 29 tokens

ehr-analysis

End-to-end EHR predictive modeling pipeline with PyHealth, covering dataset loading, task definition, model training, evaluation, calibration, and clinical interpretation.

zongtingwei/Bioclaw_Skills_Hub · 33 tokens