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 / research_report, 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_diff / artifact_verify / artifact_deprecate / artifact_reproduce: every result saved as artifacts/<name>/v<N>/ with per-file SHA-256, artifact.json provenance (command / inputs / environment / envFile) 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, sharing engines/core.mjs) and register plain cordis tools. Installable either as a profile bundle (dsh plugin add) or as an agent preset (科学模式).

v0.1.1 hardening (robustness update)

• Concurrency-safe state: all manifest/artifact writes go through a lightweight file lock (O_EXCL + stale reclaim) and atomic tmp+rename — parallel subagents can no longer corrupt or lose updates on research-manifest.json / artifacts.json.
• Structured error codes (ERR_NOT_INIT / ERR_NOT_FOUND / ERR_VALIDATION / ERR_PATH / ERR_QUOTA / ERR_LOCK_TIMEOUT / ERR_IO) instead of opaque strings.
• Hypothesis state machine (proposed → testing → supported/refuted/inconclusive) and forward-only phase transitions (rewind requires config).
• manifest ↔ artifacts linked: research_state merges the artifact index; artifact_save writes back to the manifest.
• Manifest schema v1→v2 migration on load, persisted on next write.
• Artifact upgrades: streaming SHA-256 (big files), identical-content dedup via hardlink, artifact_diff / artifact_verify / artifact_deprecate, envFile + input hashes in provenance.
• Structured JSON outputs (research_report, artifact_diff, artifact_verify) and an audit log at <root>/.science.log.

Install

Option A — profile bundle (community standard)

dsh plugin --profile web add dsh-science            # after npm publish
# or straight from GitHub:
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
# or from a local checkout:
bash scripts/install.sh          # copy   (or: bash scripts/install.sh link)

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          # -> ~/.dsh/skills (respects $DSH_HOME)

Quick start (first session)

1. research_init — create research-manifest.json + the project skeleton (experiments/ literature/ artifacts/ analyses/ figures/ manuscript/ reviews/ data/ envs/).
2. Read research_state at the start of every session; the loop state persists across sessions.
3. 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.
4. 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)
│   ├── core.mjs          #   shared core: locks, atomic writes, error codes, streaming sha256, structured tools, audit
│   ├── 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    # 62 checks against a temp workspace (node >= 18)
│   └── stability-test.mjs# 25 concurrency/atomicity/stress checks (locks, lost-update, soak, migration)
└── test/verify-bundle.sh # isolated end-to-end bundle install + boot check

Verification

node scripts/smoke-test.mjs       # engine logic + end-to-end loop + error codes + migration
node scripts/stability-test.mjs   # concurrency / atomicity / lock / stress stability checks
bash test/verify-bundle.sh        # pnpm pack -> isolated profile -> install -> boot check

All are part of the release checklist and are safe to run in CI (both test scripts write 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

Branching model & release workflow (main = release, dev = integration, feat/* = features, tag-triggered npm publish + GitHub Release via Actions): see docs/branching.md.

bash scripts/sync-engines.sh    # after editing engines/*.mjs — keeps preset/engines in sync
node scripts/smoke-test.mjs     # logic + static package checks
node scripts/stability-test.mjs # concurrency / atomicity / lock stability checks
bash test/verify-bundle.sh      # end-to-end bundle install + boot

Community

• Topic: github.com/topics/dsh-plugin
• Curated lists: awesome-dsh-plugin · awesome-deepseek-harness

License

MIT — see LICENSE.