dsh-auto-approve
Run the following command in DeepSeek Harness:
dsh plugin install DNAlec/dsh-auto-approve
Paste the following prompt into your AI chat to install this plugin:
Run dsh plugin install DNAlec/dsh-auto-approve in your terminal to install the plugin from https://github.com/DNAlec/dsh-auto-approve, then restart dsh web to activate it.
About this plugin
When running an Agent in DeepSeek Harness, every file write or shell command triggers a manual approval dialog. For low-risk, reversible day-to-day operations, this click-by-click confirmation quickly becomes a source of disruption. dsh-auto-approve steps in when the session preset is set to Auto-approve: it takes over the approval pipeline and uses keyword rules together with a configurable judge model to decide whether each tool call is allowed, rejected, or escalated to a human.
The core pipeline has three layers. The first is a set of keyword buckets (reject / human / allow) that match tool names, commands, paths, working directories, and custom-tool argument values—yet never inspect written contents or code bodies, guarding only the catastrophic-with-zero-context red lines. The second layer is the judge model, which receives the same fields as the Web tool card and returns a category id plus an independent risk level (low / medium / high); the plugin then executes the action defined in the corresponding row × level cell. The third layer is the fallback: whenever the model output is unparseable, times out, or the call simply fails, the decision reverts to the original Web approval dialog rather than silently passing through. Extra controls include model-initiated human review, a fully editable judge prompt template, and an adjustable output-token budget.
This plugin is aimed at developers who drive Agents heavily inside DeepSeek Harness and want trusted operations to flow automatically while keeping a hard safety net. If you are tired of ls ./src still demanding a manual tap while rm -rf / is caught by a single keyword, dsh-auto-approve makes the approval chain truly risk-tiered.
Use Cases
- Low-risk read-only tool calls pass silently when running Agents at high frequency, keeping the workflow uninterrupted.
- Catastrophic-zero-context operations like rm -rf / or raw-disk formatting are hard-blocked by keywords without a model vote.
- Medium-risk calls are classified by a judge model into a category and a risk level, with the row-by-level cell deciding allow, reject, or escalate.
Best For
- Developers driving Agents heavily inside DeepSeek Harness who want trusted operations to flow without friction.
- Engineering teams that need a risk-tiered approval chain with a human fallback for edge cases.
- Independent researchers needing fine-grained, configurable safety boundaries on Agent tool calls.
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