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dsh-tool-turbo 项目预览
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dsh-tool-turbo

Agent 与自动化TypeScript

dsh-tool-turbo:按工具轮次自动调整 reasoning_effort 以降低工具调用延迟——多步工具链中模型每次调用前都重新思考,本插件自动优化该档位。

7 StarsElectricitysheep 更新时间 2026-08-17
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dsh-tool-turbo 项目预览

项目介绍

中文

dsh-tool-turbo:按工具轮次自动调整 reasoning_effort 以降低工具调用延迟——多步工具链中模型每次调用前都重新思考,本插件自动优化该档位。

English

Auto-tunes reasoning_effort per tool turn to cut tool-call latency — re-picks the dial before each call in multi-step tool chains.

项目信息与收录信号

快照 2026-09-11
作者 / 组织
Electricitysheep
仓库
Electricitysheep/dsh-tool-turbo
GitHub Stars
7
Forks
0
开发语言
TypeScript
许可证
未记录
最近推送
2026-08-17
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数据评分

66/ 100
维护活跃
83
文档与实用信息
74
生态关注度
26
安装便捷信号
100
项目声明信号
80

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评分来自已有数据快照的五维模型,仅作浏览参考,不是实际使用评测或安全检测结论。

安装与使用

所有项目资料与许可均以对应 GitHub 仓库为准。

dsh plugin add github:Electricitysheep/dsh-tool-turbo

复制命令不会自动执行。请先阅读仓库文档,确认环境要求与许可证。

项目截图

仓库 README

GitHub 原文

已保存的仓库文档快照;文档抓取时间未记录,可能与上方目录介绍处于不同版本。安装前请核对当前仓库说明。

dsh-tool-turbo

Cut tool-call latency in DeepSeek Harness (dsh) by auto-adjusting reasoning_effort per tool round.

中文文档 · English

In a multi-step tool chain, the model re-thinks before every tool call — and that thinking dominates the wall-clock time (a 50-step agent task can spend minutes in reasoning between tools). dsh-tool-turbo watches the recent tool calls of a step and injects the *lowest sensible* reasoning effort into the next model request, then lifts it again the moment the work gets heavy.

How it works

DeepSeek's API exposes reasoning_effort in three steps (low / high / max, shipped 2026-08-13). dsh re-resolves the request config for every step through an agent/request waterfall (see packages/core/agent-loop/src/agent.ts — "plugins propose the next request config"). dsh-tool-turbo plugs into that waterfall:

  1. Watch the step's recent tool/call records from the session.
  2. Decide: simple, deterministic tools (write, read, grep, glob, bash, fs_*, …) with small payloads → low; mixed/heavy work → high; very heavy payloads → max (opt-in).
  3. Inject the decision into the agent/request config for the next model call of that step.

Long tool chains keep the cheap rounds cheap, and never starve the hard rounds of reasoning.

Install

# 1. clone + build the plugin
git clone https://github.com/Electricitysheep/dsh-tool-turbo.git
cd dsh-tool-turbo && npm install

# 2. register into your dsh profile (web shown; any profile works)
#    ~/.dsh/profiles/web/package.json dependencies:
#      "dsh-tool-turbo": "link:<absolute path to dsh-tool-turbo>"
#    ~/.dsh/profiles/web/cordis.patch.yml:
#      - insert:
#          - id: tool-turbo
#            name: dsh-tool-turbo
cd ~/.dsh/profiles/web && pnpm install

# 3. restart dsh web
dsh web

Verified

  • Injector works in a live dsh instance (log lines from a real run):
[tool-turbo] agent/request: baseline=high calls=[]                    => reasoningEffort=high
[tool-turbo] agent/request: baseline=high calls=[{"name":"write",…}] => reasoningEffort=low
  • 6/6 unit tests on the effort policy (decideEffort): fresh prompt keeps the baseline, simple-tool chains downgrade to low, downgrades respect the user toggle, heavy payloads upgrade to max (opt-in), mixed tools lift to high.
  • tsc --noEmit clean.

Policy (pure, testable)

Recent tool callsDecision
none (fresh prompt)keep user's selected effort
≥75% simple tools, small args, downgrade allowedlow
mixed / heavy toolshigh (when upgrades allowed)
very heavy payloads, upgrade allowedmax
otherwisekeep user's selected effort

Toggles (settings namespace planned): allowDowngrade (default on), allowUpgrade (default off — keep max conservative), baseline (default high).

Roadmap

  • [x] effort-decision core + waterfall injection
  • [x] per-tool duration telemetry (host log)
  • [ ] settings namespace (dsh-settings) for the toggles
  • [ ] tool timing surfaced in the UI / agent context
  • [ ] profile-agnostic install docs (headless/tui)

License

MIT

使用前核对

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