AI Agent Hub
Back to plugins
dsh-context-enhancement preview

dsh-context-enhancement

Memory Updated 2026.09.16

Run the following command in DeepSeek Harness:

dsh plugin install chuxindd/dsh-context-enhancement

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

Run dsh plugin install chuxindd/dsh-context-enhancement in the DeepSeek Harness terminal to install this plugin; the project source is available at https://github.com/chuxindd/dsh-context-enhancement

About this plugin

Coding agents in long-running tasks can go through dozens or even hundreds of tool calls. Tool outputs, code snippets, and error logs eventually fill the limited context window. When /compact fires, the input is often too large and both fine-grained details and global structure are lost at once, making the agent's behavior feel like a different model before and after. dsh-context-enhancement is a DSH memory plugin built specifically to govern context within a single long session.\n\nIt replaces the blunt one-shot compaction with two parallel mechanisms. The main-thread compression chain partitions history into a memory zone, a tool-compression zone, and a recent zone: the oldest content undergoes batched semantic archival, tool outputs are summarized or pruned before being retained, and the current working context stays intact. Meanwhile, an independent checkpoint sidecar continuously updates a single task summary in the background using its own model request, input budget, output budget, and retry policy. The main agent always reads the latest successfully committed stable version, and background failures never block the primary task. After a service restart, task state can be restored directly from storage without re-invoking the model.\n\nThis plugin suits developers who run multi-turn long coding sessions in DSH, repeatedly hit information gaps after compaction, and want more robust context continuity on top of the full standard tooling and planning capabilities.

Screenshots

Use Cases

  • Long coding tasks where tool outputs fill the window and require zone-based compression instead of blunt full-history truncation
  • After a service restart, restore task facts, decisions, and constraints directly from storage without re-invoking the model
  • Agent behavior feels inconsistent after compaction; layered memory, tool-compression, and recent zones keep the context coherent

Best For

  • Developers running multi-turn long coding sessions in DSH who frequently trigger /compact
  • Engineers frustrated by information loss after bulk compaction who want gradual, single-session context governance
  • Teams that need seamless task-state recovery after service restarts without manually rebuilding context