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recursive-language-model

Decompose complex agentic tasks into bounded steps with a small local model.

10|5|Updated Jun 3, 2026
Search Tags:#orchestration#decomposition#rlm#multistep#local-model#verifiers#tool-subset
One-click install
npx skills add https://github.com/understudylabs/understudy-agent-tools --skill recursive-language-model
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: recursive-language-model
Source: https://github.com/understudylabs/understudy-agent-tools/tree/main/skills/recursive-language-model
Command: npx skills add https://github.com/understudylabs/understudy-agent-tools --skill recursive-language-model

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Decompose complex agentic tasks into bounded steps by wrapping a small local model behind the incumbent call contract, enabling scalable orchestration without rearchitecting the existing system.

Core Features & Use Cases

  • Structured loop framing: frames the workload as a stateful, stepwise process with a scratchpad and a step counter.
  • Per-step tool subsetting: selects a minimal set of tools per step to minimize context.
  • Recursive decomposition: supports nested sub-tasks by spawning bounded sub-loops and returning summaries.
  • Use Case: ideal for tasks that would normally require a frontier model but can be solved by a smaller model under a robust harness.

Quick Start

Describe a complex task and deploy the recursive-language-model harness to decompose it into bounded steps using a small local model behind the incumbent call contract.

Frequently Asked Questions about recursive-language-model

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I decompose complex agentic tasks into bounded steps using a local model?▼

Decomposing complex agentic tasks into bounded steps is achieved by wrapping a small local model behind the incumbent call contract. The harness frames workloads as a stateful stepwise process with a scratchpad and step counter.

Can I use a small local model for multi-step reasoning without rearchitecting my existing agent system?▼

Yes you can use a small local model for multi-step reasoning without rearchitecting your system. The recursive-language-model wraps the model behind the incumbent call contract to enable scalable orchestration transparently.

What is the best way to minimize context size when running stateful workflows with a local model?▼

The best way to minimize context size in stateful workflows is per-step tool subsetting. The orchestration pattern selects a minimal set of tools for each bounded step to reduce overhead.

Does recursive decomposition support nested sub-tasks in local-first AI agent contexts?▼

Recursive decomposition supports nested sub-tasks in local-first AI agent contexts by spawning bounded sub-loops. These sub-loops process nested tasks and return summarized results to the main workflow.

When should I use bounded-step orchestration instead of a frontier model for tool usage tasks?▼

Use bounded-step orchestration for tool usage tasks when a robust harness can solve the problem with a smaller model. It is ideal for scenarios that would normally require a frontier model but can be decomposed effectively.

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