dsh-science

A Claude Science–style research workbench for DeepSeek Harness — for genomics / pathogens / human health / bioinformatics projects.
One-liner: dsh-science — Claude Science-style research workbench for DSH: ReAct research-loop engine (research_* tools), versioned artifacts with provenance (artifact_* tools), and 10 science skills for genomics / pathogens / bioinformatics.
- ReAct research loop engine —
research_init / research_state / research_hypothesis / research_experiment / research_findings / research_phase / research_review, persisted in a research-manifest.json state machine (Question → Hypothesis → Experiment → Observe → Analyze → Conclude → Next Question).
- Versioned artifacts with provenance —
artifact_save / artifact_list / artifact_show / artifact_reproduce: every result saved as artifacts/<name>/v<N>/ with per-file SHA-256, artifact.json provenance (command / inputs / notes / environment) and an append-only provenance.md.
- 10 science skills — research-loop, science-project-setup, artifact-provenance, scientific-reviewer, literature-connector, parallel-delegation, manuscript-writing, bioinformatics-toolkit, conda-environments, data-inventory.
Both engine plugins are zero-dependency (Node built-ins only) and register plain cordis tools. Installable either as a profile bundle (dsh plugin add) or as an agent preset (科学模式).
Install
Option A — profile bundle (community standard)
dsh plugin --profile web add dsh-science
dsh plugin --profile web add "github:biociao/dsh-science"
Restart the profile (or refresh the Web GUI). The bundle inserts the two engines
into the profile layer stack; the research_* / artifact_* tools become
available to every agent on that profile.
Option B — agent preset (full 科学模式 experience, per-agent)
git clone https://github.com/biociao/dsh-science ~/.dsh/.agent-presets/science
bash scripts/install.sh
Then create a session in the DSH Web GUI and pick the 科学模式 preset — the
preset carries the research persona + engines with per-agent scoping.
Skills
The 10 skills are discovered automatically from a project's .dsh/skills/
(drop this repo's skills/ into your project), or install them machine-wide:
bash scripts/install-skills.sh
Quick start (first session)
research_init — create research-manifest.json + the project skeleton
(experiments/ literature/ artifacts/ analyses/ figures/ manuscript/ reviews/ data/ envs/).
- Read
research_state at the start of every session; the loop state persists
across sessions.
- Run the loop:
research_hypothesis (H1/H2/…) → research_experiment (E01/…,
creates experiments/<id>/{design.md,log.md,code/,results/}) → run code →
research_findings (appends to log.md, updates hypothesis status, advances
the loop) → artifact_save for anything worth citing or reproducing.
- For key claims: extract the claim, have a review subagent check it against the
execution records (see the
scientific-reviewer skill), archive with
research_review (writes reviews/R0n/report.md).
Repository layout
dsh-science/
├── package.json # dsh.bundle.patch -> ./cordis.patch.yml (+ exports)
├── cordis.patch.yml # bundle patch: inserts the two engines by subpath export
├── engines/ # canonical engine sources (bundle form)
│ ├── research-loop.mjs
│ └── artifact-registry.mjs
├── preset/ # agent-preset form (mirrors engines/ via sync-engines.sh)
│ ├── agent.cordis.yml # references ./engines/*.mjs (relative, preset mount)
│ ├── preset.yml
│ └── engines/ # mirror — keep in sync: bash scripts/sync-engines.sh
├── skills/ # 10 SKILL.md skills
├── scripts/
│ ├── install.sh # install preset -> ~/.dsh/.agent-presets/science
│ ├── install-skills.sh # install skills -> ~/.dsh/skills
│ ├── sync-engines.sh # mirror engines/ -> preset/engines/
│ ├── init-project.sh # project skeleton without a science session
│ └── smoke-test.mjs # 23 checks against a temp workspace (node >= 18)
└── test/verify-bundle.sh # isolated end-to-end bundle install + boot check
Verification
node scripts/smoke-test.mjs
bash test/verify-bundle.sh
Both are part of the release checklist and are safe to run in CI (the smoke test
writes only to a temp workspace; the bundle test uses an isolated $DSH_HOME).
FAQ
Why subpath exports and not relative paths in the bundle?
dsh plugin add installs the package into the profile and its cordis.patch.yml
rows join the profile composition. The profile loader resolves a row name
relative to the profile directory (not the package), so ./engines/x.mjs
fails with ERR_MODULE_NOT_FOUND. Referencing dsh-science/engines/x.mjs
(subpath export, exports in package.json) resolves from the profile's
node_modules and works — verified experimentally on dsh 0.1.0-rc.6.
The agent-preset mount, by contrast, resolves relative names from the preset
directory, which is why preset/agent.cordis.yml can use ./engines/*.mjs.
Bundle or preset — which should I use?
- Bundle: tools available to every agent on the profile; one command to install.
- Preset: the full 科学模式 experience (research persona, per-agent scoping).
The persona row in
cordis.patch.yml is commented out because a profile-wide
persona would apply to all agents — uncomment it before publishing if that is
what you want.
Where do the skills come from?
A project's .dsh/skills/ is auto-discovered; scripts/install-skills.sh puts
them machine-wide in ~/.dsh/skills (respecting $DSH_HOME).
Development
bash scripts/sync-engines.sh
node scripts/smoke-test.mjs
bash test/verify-bundle.sh
License
MIT — see LICENSE.