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dsh-tool-squeeze

Model Inference Updated 2026.08.25

Run the following command in DeepSeek Harness:

dsh plugin install w2829562572-dev/dsh-tool-squeeze

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

Run dsh plugin install w2829562572-dev/dsh-tool-squeeze in your DeepSeek Harness terminal to install; the source is available at https://github.com/w2829562572-dev/dsh-tool-squeeze .

About this plugin

Anyone building an agent will eventually hit the same wall: a single tool call spits back tens of thousands of low-signal lines-a Maven log, a nested JSON payload, a crawled HTML page-and blind truncation risks erasing the one failure buried in the middle, while keeping the full blob inline burns context tokens on every subsequent model request. dsh-tool-squeeze slots into the public tools/post-execute lifecycle hook of DeepSeek Harness and applies deterministic, local, evidence-aware compression to logs, JSON, HTML, and plain text. Error lines, failed tests, stack roots, non-zero exit codes, and representative structural samples are prioritized; every omission is disclosed with exact counts; and the complete formatted original is retained through the official DSH spill store for on-demand retrieval. No LLM call, no patch to Harness Core, and a fail-open guarantee means a plugin exception never turns a tool call into an error. On the committed high-repetition benchmark fixtures, a 22K-token Maven failure compresses to 1.7K (92.5%), a 61K Gradle log to 123 tokens (99.8%), and a 159K JSON API response to 352 tokens (99.8%), with all critical-evidence gates passing. The plugin is purpose-built for developers who use DeepSeek Harness to orchestrate multi-tool agents and routinely process large tool outputs-build logs, CI transcripts, API responses, scraped pages-where predictable latency, reproducible behavior, and context-window efficiency matter as much as the model reasoning quality itself.

Use Cases

  • Compressing tens of thousands of build-log lines into a compact evidence summary before model consumption
  • Trimming oversized API JSON responses while preserving error records and key structural samples
  • Converting bulky crawled HTML into concise, model-ready Markdown for agent reasoning

Best For

  • Developers building multi-tool agents on DeepSeek Harness
  • Automation engineers routinely processing CI/CD build logs and test output
  • LLM application architects with strict context-window token budgets