dsh-message-handoff
Run the following command in DeepSeek Harness:
dsh plugin install antonkoatl/dsh-message-handoff
Paste the following prompt into your AI chat to install this plugin:
Run dsh plugin install antonkoatl/dsh-message-handoff inside DeepSeek Harness to install; the source repository is at https://github.com/antonkoatl/dsh-message-handoff .
About this plugin
In long AI-assisted conversations and iterative development sessions, you often want to rewind to a decision point and explore an alternate path. A plain fork, however, simply truncates the history and discards everything that happened afterward. dsh-message-handoff fills exactly that gap by adding a handoff icon beside every finalized assistant message, so the new session inherits the verbatim context up to that moment while still learning what the original session did next.
Clicking the icon copies the conversation up to the chosen message into a fresh session, then makes a single auxiliary LLM call to produce a structured handoff document covering the post-fork activity: task goal, progress, decisions made, files touched, next steps, and open questions. That document is appended on top of the copied history. The child session is automatically attached to the source workspace and opened, while the origin session stays completely untouched. If any step fails (network error, timeout, empty model output), no session is created and no side effects remain; a simple retry of the icon is all it takes.
If you frequently branch from a key decision point to compare strategies, back-trace debugging, or explore multiple approaches within one long conversation, this plugin meaningfully reduces the cost of manually reconstructing context. With zero runtime dependencies and an immediate install experience, it is a natural companion for power users of DeepSeek Harness.
Use Cases
- Rewind to a decision point in a long conversation and fork with a structured summary of subsequent progress
- Branch from a key assistant message into a child session to compare strategies in parallel
- Restore full context during back-trace debugging while automatically learning what the original session accomplished later
Best For
- AI engineers who iterate deeply in DeepSeek Harness for multi-turn development
- Exploratory users who compare multiple implementation approaches within a single long conversation
- Tech leads or architects who frequently rewind and fork sessions to manage complex project context
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