dsh-git-ai
Run the following command in DeepSeek Harness:
dsh plugin install NiuZhuang/dsh-git-ai
Paste the following prompt into your AI chat to install this plugin:
Run dsh plugin install NiuZhuang/dsh-git-ai in your terminal (source https://github.com/NiuZhuang/dsh-git-ai ); the plugin registers into the active DeepSeek Harness profile and activates in one step.
About this plugin
Once an AI agent starts editing your codebase, "who actually wrote this line?" becomes a real question. Which lines came from a model, which model, in which session — that context lives scattered across logs and terminal history, invisible the moment you look at a diff in the editor.
dsh-git-ai hooks into the Harness tools/pre-execute and tools/post-execute interception points and translates every write, edit, str_replace_editor call, as well as bash and pwsh invocations, into git-ai agent-v1 checkpoints written to git notes via stdin. Inside VS Code, hovering over any line now reveals the author (agent), model name, and session ID. git-ai additionally reports ai_additions and ai_accepted per tool-model pair, giving you a quantified picture of how much of the diff is AI-authored. Read-only str_replace_editor view operations are skipped automatically; failed edits emit no checkpoint while failed bash commands still close their before/after snapshot pair.
The plugin treats git-ai as a fully optional external dependency. A missing binary, a timeout, a non-zero exit, or an executor rejection is logged as a warning and never propagated — the tool call and the agent turn proceed exactly as if the plugin were not installed.
Built for engineers who need to audit AI-generated code, distinguish model output from human edits, and for teams that want to answer "did the agent write this or did a person?" at a glance during code review.
Screenshots
Use Cases
- Hover over a line in VS Code to see which model and which Harness session generated it
- During code review, check ai_additions per tool-model pair to separate AI output from human edits
- Audit the agent editing footprint: which files were touched and which shell commands produced the changes
Best For
- Engineers who use the DeepSeek Harness agent for daily coding
- Teams that need to quantify AI code share and audit model-level attribution
- Developers who care about line-level provenance and want to separate AI output from human edits
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