Plugin directory / Developer / dsh-tool-turbo
dsh-tool-turbo
Verified · install-tested on dsh Electricitysheep
What it does
Per-round reasoning_effort optimizer.
Works — verified, early-stage project
Per-round reasoning_effort optimizer. It installs cleanly and boots without issues in our testing. It's early-stage but functional.
“Verified” means our automated CI actually ran dsh plugin add in a clean profile and it booted — nothing more. Feature descriptions and version compatibility are the author’s claims. This is not a security audit and not an endorsement of third-party code.
README
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:
- Watch the step's recent
tool/callrecords from the session. - Decide: simple, deterministic tools (
write,read,grep,glob,bash,fs_*, …) with small payloads →low; mixed/heavy work →high; very heavy payloads →max(opt-in). - Inject the decision into the
agent/requestconfig 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 tolow, downgrades respect the user toggle, heavy payloads upgrade tomax(opt-in), mixed tools lift tohigh. tsc --noEmitclean.
Policy (pure, testable)
| Recent tool calls | Decision |
|---|---|
| none (fresh prompt) | keep user's selected effort |
| ≥75% simple tools, small args, downgrade allowed | low |
| mixed / heavy tools | high (when upgrades allowed) |
| very heavy payloads, upgrade allowed | max |
| otherwise | keep user's selected effort |
Toggles (settings namespace planned): allowDowngrade (default on), allowUpgrade (default off — keep max conservative), baseline (default high).
Roadmap
- effort-decision core + waterfall injection
- 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
Install
Install the catalog once, then DeepSeek Harness can find and install any plugin from this site automatically:
dsh plugin add dshbase-catalog Then say "install dsh-tool-turbo for me" — your agent finds it in the directory and installs it. Docs: dshbase-catalog · verified packs.
This plugin is GitHub source (not published to npm) — install it straight from the repo:
Web profile:
dsh plugin --profile web add github:Electricitysheep/dsh-tool-turbo Headless (CLI) profile:
dsh plugin --profile headless add github:Electricitysheep/dsh-tool-turbo Test report
Verified: L1 install + L2 load + L3 runtime from GitHub source on dsh 0.1.0-rc.6.
When to use it
Extend the agent's coding surface — give it a new tool, workflow, or integration so it handles a dev task it couldn't before.
Who it's for
Developers who want dsh to behave like a teammate on real codebases — editing, running, and verifying changes rather than just answering.
For developers — extending it
The tool/command surface is the seam: expose more of the SDK, add smarter context wiring, or tighten the loop between code changes and verification.