dsh-planner-executor
Run the following command in DeepSeek Harness:
dsh plugin install mikuuuuuue/dsh-planner-executor
Paste the following prompt into your AI chat to install this plugin:
Run dsh plugin install mikuuuuuue/dsh-planner-executor in your terminal; the source repository is https://github.com/mikuuuuuue/dsh-planner-executor .
About this plugin
When the main model must both reason and step through tool calls, a flood of intermediate steps quickly inflates the session context, burns tokens, and forces a single model to handle both thinking and doing. dsh-planner-executor cleanly separates those two roles: the main model focuses on analysis, planning, and summarizing final results, while execution is delegated via the delegate_execution tool to an isolated sub-agent that works in its own session. No intermediate step ever enters the primary conversation; only the final report returns to the main model, maximising token savings while letting each role use the best-suited model.
The sub-agent LLM provider, execution model, per-call token budget, maximum delegation depth, persona, and tool allow/deny lists are all configurable in the dedicated Planning-and-Execution section of the Web settings panel; any field left blank falls back to the main model settings, and changes take effect on the very next delegation call. On the safety side, the sub-agent permissions are pinned to its sandbox scope, its approval policy is locked to never, and delegate_execution itself is force-denied in the sub-agent tool catalogue, so recursive delegation is structurally impossible even if someone attempts to construct the call.
Ideal for multi-model workflows where a high-reasoning model handles planning while a faster or more tool-capable model handles execution, as well as for teams that need fine-grained control over sub-agent permissions and costs. Zero build dependencies, pure JavaScript ESM, ready to use right after installation.
Use Cases
- Pair a high-reasoning planner model with a fast executor model to cut latency and cost
- Prevent bloated context windows caused by intermediate tool-call steps in the main session
- Enforce fine-grained control over sub-agent model, tool access, and token budget
Best For
- AI workflow developers building multi-model collaboration pipelines
- Cost-sensitive teams that need granular control over sub-agent permissions
- Architects who want to decouple planning from execution for better maintainability
Related Plugins
A method pack that makes AI coding agents plan against your real baseline, prove completion with fresh evidence, and reduce reworks and unsafe changes.
Turns the DeepSeek Harness session into a captain that builds a durable sub-agent team, splits goals into dependency-aware tasks, and coordinates work via direct messages and a live Web UI.
Gives coding agents design judgment, letting Claude Code, Cursor, and 70+ agents generate and iterate high-quality UI, presentations, and graphics right from the terminal.
Run the Pi ecosystem's plugins on DeepSeek Harness, unmodified, via a compatibility layer that implements Pi's public extension ABI on DSH's native services.