AI Agent Hub
Back to plugins
🤖

dsh-advisor-plugin

Model Inference Updated 2026.09.14

Run the following command in DeepSeek Harness:

dsh plugin install leeyoung1/dsh-advisor-plugin

Paste the following prompt into your AI chat to install this plugin:

Run dsh plugin install leeyoung1/dsh-advisor-plugin inside DeepSeek Harness to install the plugin; the source repository is at https://github.com/leeyoung1/dsh-advisor-plugin . After installation, restart dsh web and configure the review model on the settings page.

About this plugin

The same model playing both coder and verifier easily falls into self-validation blind spots, gradual drift, and premature completion in long-running tasks. dsh-advisor-plugin addresses this structural problem with a simple strategy: a cheap, fast execution model keeps moving along the main path, while at critical checkpoints the full session is handed to a stronger reviewer that returns a plan, a correction, or a stop signal before execution resumes.

Two complementary channels are provided. The explicit channel lets the execution model call advisor() with zero parameters; the entire session and tool list are forwarded to the reviewer automatically, so the model never has to assemble context by hand. The patrol channel samples the session on a step-and-wall-clock schedule, injecting a correction into the next request when drift is detected and demanding the execution model halt and report to the user when a STOP fires. The reviewer only outputs judgments, never touches code, never mutates state, and every failure, timeout, or context overflow is isolated so the executing turn never breaks.

Built for developers running lightweight execution models through DeepSeek Harness who want an on-demand expert review at critical moments without paying for expensive inference on every step. Setup is a simple pick-a-model exercise on the DSH settings page; with no reviewer configured the plugin registers nothing and adds zero prompt overhead.

Use Cases

  • Preventing directional drift and premature completion in long-chain coding tasks
  • Invoking a stronger reviewer at critical checkpoints for plan, correction, or stop signals
  • Daily execution with a lightweight model plus periodic patrol-based correction
  • Triggering explicit review when stuck repeatedly or evidence contradicts the plan

Best For

  • Developers running Coding Agents on DeepSeek Harness
  • Teams seeking expert-level review without per-step inference cost
  • Tech leads focused on execution-review separation and multi-model collaboration