dsh-science-workbench

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A reproducible science workbench plugin for the DeepSeek Harness. It blends the best of three worlds:

• Jupyter — cells and inline figures you can see and re-run;
• Claude Science — an agent as the execution engine;
• Nextflow / nf-core — every artifact carries full provenance.

Core promise: every figure and artifact is traceable and replayable. You can always answer “it = which code + which inputs + which environment + which params/seed”, and re-run it in one click.

✨ Features

• Code → figure → feedback → redraw — the agent runs a self-contained cell to produce figures shown inline; you attach structured feedback to a figure, and bio_rerun_cell regenerates a derived version (v1 → v2 → v3).
• One ledger per project — a plain-text manifest.json is the single source of truth: cells, artifacts, provenance and feedback history.
• Reproducible by construction — self-contained scripts, a fresh subprocess per cell, environment.lock, SHA-256 input/output hashes and a fixed seed.
• Git-versioned automatically — each project is git init-ed on creation and auto-committed at every step (never pushed).
• Cross-platform — the Host shell layer speaks bash on macOS/Linux and PowerShell on Windows; Python resolves to python on Windows and python3 on POSIX.

🛠 Tools

Nine agent-facing tools, plus a browser workbench:

The “Analysis workbench” tab shows the notebook, artifacts, provenance and feedback in a three-panel UI with inline figure preview (PNG/JPEG/SVG/PDF/TIFF/BMP), a searchable project picker (filter by name, sorted by most-recent activity), a cell search box, a native directory picker for the projects root, and a mark-as-final badge on completed cells. Very large figures (multi-MB) load on demand and display in full.

The plugin also bundles two publication-grade figure skills (adapted from Claude Science, Apache-2.0): figure-style (figure correctness & legibility rules + apply_figure_style()) and figure-composer (multi-panel figure composition with an adversarial self-review loop). See skills/ and ATTRIBUTIONS.md.

📸 Feature showcase

The feedback → redraw loop — every figure keeps its structured feedback history, and a one-click “让 agent 重画” (let the agent redraw) produces a derived version.

The workbench tab — a three-panel layout: the analysis steps list (with status and lineage cell_0001 → cell_0001_v2 → cell_0001_v3) on the left, and the artifact detail (inline figure + provenance / code tabs + script / delete / Finder actions) on the right.

The code tab — every artifact’s generating script carries its declaration header (cell / title / language / seed / params / inputs), so it can be reviewed and reused at any time.

The provenance tab — full provenance: producing cell, output SHA-256 hash, params, seed, derived-from and created time.

📦 Install

dsh-science-workbench is a dual-face DSH plugin (Host + Client). Install it with the standard dsh plugin command — a thin pnpm forwarder that installs the package into a profile and automatically adds it to dsh.profile.bundles (because the package declares dsh.bundle.patch).

# From npm (published):
dsh plugin --profile web add dsh-science-workbench

# Local development (from a checkout):
dsh plugin --profile web add file:/path/to/dsh-science-workbench

Then restart dsh web. The bio_* tools become globally available, the workbench tab appears, and the plugin shows up under Settings → Plugins.

DSH desktop app

The desktop app owns its own desktop profile, which the dsh CLI refuses to write (profile "desktop" is managed exclusively by the Electron application). Install from inside the app instead:

1. Open Plugins in the sidebar.
2. Add plugin → paste the package name (dsh-science-workbench) or the absolute path of a checkout (/path/to/dsh-science-workbench) → Install → Enable now.
3. Restart the app so the Host half loads (module hot-reload deliberately ignores node_modules, so a linked checkout is not picked up while running).

Projects may live outside the app's session workspace — point the plugin at your own root with bio_set_projects_dir (e.g. ~/bio-projects); file writes are not fenced to the session workspace.

🚀 Quick start

After installing and restarting, just ask the agent in plain language:

“帮我用 demo_tss 项目画一个 TSS 附近的信号热图。”

The agent will drive the tools for you. The equivalent manual flow is:

1. bio_init_project { name: "demo_tss" }
2. bio_run_cell { title: "TSS profile", code: "..." }   # writes figures/*.png
3. look at the inline figure → bio_add_feedback { artifactPath, text: "把配色改成 Blues" }
4. bio_rerun_cell { cellId: "cell_0001", editedCode: "..." }  # → cell_0001_v2 + new figure

Every step is committed to the project’s git history and recorded in manifest.json, so the whole lineage (cell_0001 → cell_0001_v2 → …) stays inspectable.

🧪 Reproducibility model

Each cell is a self-contained script with a declaration header:

# @cell: cell_0001
# @title: TSS profile
# @language: python
# @seed: 42
# @params: {"colorMap": "Blues"}
# @inputs: ["data/peaks.bed"]
# @outputs: []

It runs in a fresh subprocess with cwd = project root. On completion the Host:

1. discovers figures written to figures/ and prefixes them with the cell id;
2. hashes every input and output (SHA-256) into the artifact record;
3. appends the cell + artifacts to manifest.json and updates index.md;
4. commits everything to the project’s local git repo.

📁 Project layout

<workspace>/bio-projects/<name>/
├─ manifest.json        # single source of truth: cells + artifacts + provenance + feedback
├─ environment.lock     # interpreter version + pip freeze snapshot
├─ index.md             # human-readable project index
├─ code/                # one self-contained script per cell (cell_0001.py, cell_0001_v2.py, …)
├─ data/                # input data
├─ figures/             # figures (cell-prefixed, e.g. cell_0001_tss_profile.png)
└─ .git/                # auto-created, auto-committed

🧩 Architecture

• Host (lib/index.js) is the single source of truth. It registers the bio_* tools into the host tools registry, does all execution/provenance, and serves the /biowb/* data routes through the webServer service.
• Client (lib/client.js) is a pure projection — a hand-written browser bundle that reads/writes over same-origin fetch('/biowb/<method>'). No typert Remote bridge and no harness monorepo build are required.
• The convention skill (skills/bio-workbench) teaches the agent the project layout, the cell contract and the feedback loop.

🌍 Cross-platform

🔧 Development

git clone https://github.com/poplarity/dsh-science-workbench
cd dsh-science-workbench

# lint (syntax check)
npm run lint

# install into your profile and restart
dsh plugin --profile web add file:$(pwd)

Structure: lib/index.js (Host) · lib/client.js (Client bundle) · index.js (entry re-export) · cordis.patch.yml (bundle patch) · skills/ (convention skill) · docs/ (design doc) · examples/ (example project).

License

MIT