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dsh-compaction-pro

Model Inference Updated 2026.08.25

Run the following command in DeepSeek Harness:

dsh plugin install helibeiqi/dsh-compaction-pro

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

Install by running dsh plugin install helibeiqi/dsh-compaction-pro in DeepSeek Harness; source code is at https://github.com/helibeiqi/dsh-compaction-pro

About this plugin

When long conversations hit the compaction threshold, the built-in dsh-compaction-basic summarises with a generic English template that routinely rewrites or drops version numbers, file paths, command strings, and error codes. Chinese sessions end up as English summaries, creating a jarring read-back. For quant or coding agents, one altered identifier downstream is enough to break the pipeline.

dsh-compaction-pro swaps out only the single documented override point, summarize(), while inheriting every pressure, retention, and token-metric policy from the official engine. The high-fidelity prompt enforces verbatim retention of numbers, paths, commands, and identifiers; the summary language follows the session (Chinese in, Chinese out, code blocks untouched); oversized segments can be recursively split and merged to survive maxTokens limits; decision summaries now include reasoning and rejected alternatives; and a customInstruction field lets you replace the entire built-in prompt.

If you run coding, quant, or any precision-critical multi-agent workflows inside DeepSeek Harness and work in Chinese, this is a drop-in upgrade over the stock compaction backend. Disable the built-in basic engine, install pro, and every call site switches transparently.

Use Cases

  • Preserve exact version numbers, file paths, and command strings verbatim during compaction
  • Generate Chinese summaries for Chinese sessions with code and identifiers kept intact
  • Recursively split oversized segments to prevent maxTokens truncation and information loss

Best For

  • Developers running coding or quant agents inside DeepSeek Harness
  • Chinese-speaking users who require precise round-trip summaries in long sessions
  • Advanced Harness users who need custom compaction policies or chunking behavior