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.

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✨ 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:

Tool	What it does
bio_init_project	Create/open a project: code/ data/ figures/ + manifest.json + environment.lock + git init.
bio_run_cell	Run one self-contained cell, discover figures, register artifacts with hashes, commit.
bio_rerun_cell	Re-run a cell with edited code as a derived version (lineage recorded).
bio_add_feedback	Attach structured feedback to an artifact (this is how a “redraw it” note becomes history).
bio_get_project	Return a project summary: cells, artifacts, provenance and feedback.
bio_list_projects	List all projects under the projects root.
bio_set_projects_dir	Set the root directory where projects live (persisted across restarts).
bio_delete_cell	Delete a cell and its produced artifacts (script + figures).
bio_mark_cell	Mark a cell as a final (成品) artifact, or unmark it — flagged in the workbench and index.

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.

🚀 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

Operation	macOS / Linux	Windows
Shell	bash	PowerShell
Hash	shasum -a 256	Get-FileHash
mkdir / move / delete	mkdir -p / mv / rm -f	New-Item / Move-Item / Remove-Item
Open in file manager	open / open -R	explorer.exe / explorer.exe /select,
Python	python3	python

🔧 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