DSH Session Lab
dsh-session-lab is an installable DeepSeek Harness bundle that registers three workflow Skills directly in the DSH runtime catalog:
This is an independent, pre-1.0 community project. It is not an official DeepSeek product and is not endorsed by DeepSeek. APIs and plugin behavior may need adjustment as DeepSeek Harness evolves.
dsh-teach turns a successful session into a redacted candidate Skill and validates paired baseline/treatment evaluation evidence.dsh-capsule packages session exports, selected artifacts, and a workspace patch into a verified .dshc evidence bundle.dsh-time-machine compares two controlled trajectories from a shared completed-turn cut.
The helpers use Python 3.10+ and the standard library only. They do not add a Host session-import endpoint, execute capsule contents, or claim exact model replay.
Requirements: Node.js 22.19+ (or 24+) for DSH/plugin installation and Python 3.10+ for the Skill helper scripts.
Install
Install directly from GitHub into an existing DSH profile:
dsh plugin --profile web add github:zhangguiping-xydt/dsh-session-lab
If the dsh command is not installed globally, run the latest published CLI through npx:
npx --yes @deepseek-ai/dsh@latest plugin --profile web add github:zhangguiping-xydt/dsh-session-lab
For local development from a source checkout:
dsh plugin --profile web add /absolute/path/to/dsh-session-lab
dsh plugin --profile headless add /absolute/path/to/dsh-session-lab
After an npm release, replace the path with dsh-session-lab. Restart an already running profile after installation. Verify the composed layer before use:
dsh --profile web --dump-config | rg '# == dsh-session-lab'
The bundle uses the host ctx.skills registry, so it does not depend on project-directory watchers. A source-only alternative is to copy an individual directory under <project>/.dsh/skills/ or ~/.dsh/skills/; restart DSH if an existing session catalog does not refresh.
Use
Invoke a Skill explicitly or describe a matching task:
Use $dsh-capsule to package and verify this DSH export.
Use $dsh-teach to extract and independently evaluate a Skill from this successful session.
Use $dsh-time-machine to compare these two sessions from their common completed turn.
Read the selected SKILL.md before execution. Session exports and generated reports may contain sensitive content even after pattern-based redaction.
Verification
python3 -m pip install -r requirements-dev.txt
coverage run --branch -m pytest
coverage report --fail-under=60
ruff check .
ruff format --check .
npm test
npm pack --dry-run
pyproject.toml configures pytest and Ruff only. The project does not publish a Python wheel; the standalone Python helpers are shipped inside the npm/DSH bundle.
The suite contains synthetic security and archive fixtures plus a checked-in real-model evaluation. The full dsh-teach example records 12 fresh DSH sessions across six paired tasks: baseline passed 1/6, treatment passed 6/6, with zero safety failures, false positives, or routing misses. See the evaluation report.
Build a release artifact
mkdir -p dist
npm pack --pack-destination dist
dsh plugin --profile headless add ./dist/dsh-session-lab-0.1.0.tgz
The CI workflow validates Python 3.10–3.13, package cleanliness, Skill structure, checked-in evaluation evidence, and installation into the latest published DSH profile.
Security
Treat raw exports, patches, images, and reports as sensitive. Automatic replacement is not anonymization, and SHA-256 integrity does not authenticate a publisher. See SECURITY.md before sharing artifacts.
The repository and each standalone Skill directory are licensed under MIT; per-Skill licenses allow independent copying.
See CONTRIBUTING.md and CODE_OF_CONDUCT.md before participating, CHANGELOG.md for release history, and RELEASING.md for the public-release checklist.
