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dsh-context-rollover

Model Inference Updated 2026.09.16

Run the following command in DeepSeek Harness:

dsh plugin install athif23/dsh-context-rollover

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

Run dsh plugin install athif23/dsh-context-rollover in your terminal to add the dsh-context-rollover plugin to your DeepSeek Harness profile; the source is hosted at https://github.com/athif23/dsh-context-rollover — restart the host once after install to activate it.

About this plugin

Long sessions rarely fail from running out of words — they fail when the context window saturates and the system reaches for an LLM summarizer, compressing pages of nuance into a few lossy sentences. dsh-context-rollover takes a different route: the model itself decides where to close the current window and where to start a fresh one, using a deterministic checkpoint (persistent notes plus an optional handoff note) instead of a generator. Raw events stay persisted and searchable on disk, so nothing is lost to the fog of summarization.

The core tools give the model new_context (request a boundary at the next safe lifecycle point), notes (list, read, write, append, search per-session markdown), history (targeted search and read over events that have scrolled off the active surface), and get_context_remaining (an honest token-headroom reading). Beyond model-driven rollovers, the engine handles pressure-triggered compaction, provider-confirmed overflow, and the familiar /compact command — all four paths reuse the same notes-plus-verbatim-tail checkpoint so no critical context is ever dropped.

Built for developers who run multi-phase, long-horizon tasks within a single session. No network calls, no telemetry, no writes outside the notes directory, no credentials required. On the Web profile an experimental preset makes it a one-click switch; on headless profiles it activates immediately after install.

Use Cases

  • Let the model pick the right moment to roll over a context window instead of relying on automatic summarization
  • Keep multi-phase tasks (research to design to implementation) coherent within a single session without detail loss
  • Transition safely into a fresh window via a deterministic checkpoint as the context window nears saturation
  • Retrieve early-conversation details precisely after a rollover using notes and history search

Best For

  • Developers running multi-phase, long-horizon reasoning tasks in a single session
  • Practitioners who need verbatim, traceable context and distrust summarizer output
  • DeepSeek Harness users on Web presets or headless profiles building inference workflows