dsh-jev-plugin
deepseek-harness-jev: native DeepSeek Harness (DSH) plugin integrating TypeSafe Jev / System One for selection, supervision, corrections, tool-output filtering, and approvals
一句话定位
deepseek-harness-jev: native DeepSeek Harness (DSH) plugin integrating TypeSafe Jev / System One for selection, supervision, corrections, tool-output filtering, and approvals
安装方式
在 DSH 的 web profile 中执行下面的命令;装完刷新页面即可。
$ dsh plugin --profile web add 'git+https://github.com/luobosibing2/dsh-jev-plugin.git#d2b72299ece45ee0468632e7c4c538798352ea8b'
兼容的 DSH 版本
>=0.1.7-rc.1 <=0.2.0-rc.2 || 0.2.1-alpha.2 || 0.2.1-alpha.3 || 0.2.1-rc.2 || 0.2.1-rc.5 || 0.2.2-alpha.2
作者与热度
luobosibing2 · ★ 207 · 更新于 2026-11-07
关于这个插件
deepseek-harness-jev
English | 简体中文 | 中文功能与实测网站
Native DeepSeek Harness (DSH) plugin with Jev or Luna Decisions as its judgment model.
deepseek-harness-jev connects DeepSeek Harness (DSH) to Jev by TypeSafe AI for agent skill and file selection, task supervision, shared-finding corrections, tool-output filtering, single-operation approval assistance, and historical stage navigation. Its 12 features are individually configurable from one Jev settings page and are all disabled by default.
The main model continues to plan, generate answers, and call native tools. Enabled features use the saved Jev or Luna Decisions connection at DSH extension points for skill catalogs, agent lifecycle, tool results, and approvals, then apply results according to each feature. DSH configures the main model separately. Integration uses public Cordis / DSH plugin APIs without modifying the host source.
This is an independent community project, not an official DeepSeek or Jev release. It is an early-stage plugin with a bounded DSH compatibility range; its APIs and model judgments are not a correctness guarantee.
The Chinese feature website explains each DSH integration point, the information sent to the judgment service, and the observed test cases and limits.
The Chinese principles and all features explainer covers all 12 features and includes a complete skill-selection input/output example. Its Markdown source is maintained in this repository.
The quick review directory links to short source guides. The Chinese log-admission interview guide covers generic and test logs, design choices, live-model results and their limits; its Markdown source is kept here.
The Chinese Goal supervision and completion review follows the two current flows from their hooks through judgments, adoption, limited supplements and delivery records. Its Markdown source separates current behavior from historical live-model results.
main supports Jev and Luna Decisions through OpenRouter or OpenAI, with three separate saved connections per profile. The 2026-10-07 public report separates fixed protocol/installation checks, controlled live samples, and real DeepSeek Flash tasks. Official OpenAI calls succeeded in these limited samples; the only live OpenRouter diagnostic returned a provider-access HTTP 403 and was stopped. These results do not establish general judgment accuracy or stable task benefit.
The 2026-10-09–10 Harness compatibility report records the upgrade from DSH 0.1.7-rc.2 to 0.2.1-alpha.2, official installation and Web checks, and new real DeepSeek Flash / Jev tasks. The original package was rejected by the new installer's version checks; this revision updates the dependencies and affected public APIs. The report separates real tasks, seeded completion diagnostics, fixed lifecycle tests, and untested provider channels.
The 2026-10-11 version compatibility report checks the current plugin on four older DSH releases and 0.2.1-alpha.2. It records official installation, nine Host components, native tools and local deterministic judgment flows, plus browser checks on the oldest release, 0.2.0-rc.2 and 0.2.1-alpha.2. These checks establish version integration in the tested environment, not judgment-model quality.
The 2026-10-21 Harness compatibility report covers the DSH 0.2.1-rc.2 upgrade. This run checks the official package, Host components and Web integration with deterministic local model replies; no live model service is called. Earlier reports retain their original versions and experiment conditions. What is included?
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