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dsh-context-assembler

Model Inference Updated 2026.09.11

Run the following command in DeepSeek Harness:

dsh plugin install catsenior507/dsh-context-assembler

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

Run dsh plugin install catsenior507/dsh-context-assembler in your terminal to install the plugin; the source repository is https://github.com/catsenior507/dsh-context-assembler . Restart dsh web after installation to load the Assembled Context panel.

About this plugin

DeepSeek Harness gives you exactly two context states: everything in the window, or whatever a threshold fires into a summary you can neither inspect nor undo. On a long task a four-thousand-token grep dump, a stack trace you already diagnosed, a sub-task that finished and shipped all keep occupying the window until that blind compaction folds them away. dsh-context-assembler turns context from a length into a set of decisions: you walk the session log as a checkable tree and, per span, choose to keep it verbatim, compress it to a one-line summary, or drop it entirely, so the model actually receives what you intend.

The core capability is an interactive three-state editor (full / key / off) layered on the harness surface-folding primitive, an N-to-1 span replacement. The panel renders turns, steps, tool calls, and subagent sessions as a navigable tree; untouched prefix nodes stay in place so the provider KV cache remains reusable. Tool-call and tool-result pairs are treated as atomic groups to avoid feeding malformed shapes to the provider. A single-event fold restores verbatim; a multi-event fold replays as one separator-delimited message and the panel labels this honestly instead of pretending to undo. On the agent side, a registered context_assembler tool (tree, set, preset, messages) lets the model itself fold finished history into a concise conclusion after each sub-task, rather than waiting for an opaque threshold to fire.

Built for power users running long, multi-tool, multi-subtask sessions in DeepSeek Harness who want explicit, per-span control over what the model sees each turn. If all-or-nothing context feels like a coin flip and you prefer every inclusion or exclusion to be a deliberate, inspectable choice, this plugin is for you.

Screenshots

Use Cases

  • Fold finished sub-tasks span-by-span in a long session to free up the context window
  • Apply preset rules to auto-compress tool results into one-line digests, cutting token usage
  • Let the agent author a semantic conclusion after each sub-task instead of waiting for opaque compaction

Best For

  • Power users running long, multi-tool, multi-subtask sessions in DeepSeek Harness
  • Inference engineers who need precise per-turn control over what the model actually receives
  • Developers who want to replace opaque auto-compaction with inspectable, reversible decisions