Dual-model auto-routing plugin: low-cost direct / high-cost upgrade with an escape-learning closed loop (wrong direct answers auto-learn fingerprints, force-upgrading the same fingerprint next time), persisted and interoperable with the Python ModelRouter.
Install
# from npm (prebuilt)
dsh plugin --profile web add @lengquan88/dsh-dual-auto
# from a prebuilt release tarball
dsh plugin --profile web add "https://github.com/lengquan88/dsh-dual-auto/releases/download/v0.1.2/lengquan88-dsh-dual-auto-0.1.2.tgz"
# from GitHub (first run asks for allowBuilds approval — follow the hint, retry)
dsh plugin --profile web add github:lengquan88/dsh-dual-auto
Any plugin you install runs third-party code with your own permissions — it can read your files, use your credentials, and reach the network, and tool approvals don’t sandbox it. GitHub-sourced plugins also run build scripts at install time — pnpm blocks those until you allow them, so an install can stop with ERR_PNPM_GIT_DEP_PREPARE_NOT_ALLOWED or ERR_PNPM_IGNORED_BUILDS; dsh prints the exact key to add under allowBuilds in your profile’s pnpm-workspace.yaml, and the install works on the next run. Allowing a build is a trust decision: only install sources you trust, and pin a commit (github:owner/repo#sha).
README
Dual-model auto-routing plugin for the DeepSeek Harness (dsh).
Low-cost direct / high-cost upgrade with an escape-learning closed loop.
Install
pnpm add @lengquan88/dsh-dual-auto
Enable
Add one row to your profile's cordis.patch.yml:
- insert:
- id: dual-auto
name: '@lengquan88/dsh-dual-auto'
Restart dsh web. The tools dual_model_route, dual_model_run, and
dual_model_mark become available in every session.
Tools
| Tool | Purpose |
|---|---|
dual_model_route |
Six-criteria routing decision (length / context / domain coverage / rule conflict / confidence / novelty → six labels). Fingerprints that escaped once are force-upgraded. |
dual_model_run |
Decision + real model call: direct → deepseek-v4-flash, upgrade → deepseek-v4-pro (auto-degrade to flash on failure, marked degraded). Probe tasks auto-validate against a gold set — wrong direct answers trigger escape learning. |
dual_model_mark |
Mark the quality of a direct result. correct=false learns the fingerprint and rewrites the disk log marker; the same fingerprint is force-upgraded next time. |
Persistence
State persists to output/dsh_router_{fingerprints,stats}.json and
dsh_router_decision_log.jsonl — interoperable with the project's Python
dao/model_router.py (v2 dict fingerprints load directly).
Links
- npm: https://www.npmjs.com/package/@lengquan88/dsh-dual-auto
- Source mirror (atomgit): https://atomgit.com/guaikepa/zhonghua/tree/main/dsh-dual-auto
License
MIT
Links
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