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researchharness

Configure and operate ResearchHarness as a tool-using LLM agent runtime.

7|1|Updated May 9, 2026
Search Tags:#cli#tooling#workspace#trace#agent runtime#python api#openai-compatible api
One-click install
npx skills add https://github.com/black-yt/skills --skill researchharness
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: researchharness
Source: https://github.com/black-yt/skills/tree/main/researchharness
Command: npx skills add https://github.com/black-yt/skills --skill researchharness

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

ResearchHarness helps you install, configure, run, embed, deploy, and debug a lightweight tool-using LLM agent runtime without guessing how its CLI, API server, Python API, tools, workspaces, or traces behave.

Core Features & Use Cases

  • Multiple execution modes: Use it from CLI, local frontend UI, Python code, or an OpenAI-compatible API server.
  • Tool and workspace management: Configure built-in tools, custom tools, workspaces, trace output, and compaction behavior for repeatable runs.
  • Safe operational guidance: Follow clear boundaries for read-only source inspection, environment setup, API compatibility, and benchmark-friendly execution.
  • Use case: A developer can launch the agent locally, inspect a project workspace, attach images or files, and retrieve a final answer while preserving traceability.

Quick Start

Ask the skill to help you set up ResearchHarness for your intended mode, validate the required environment variables and tools, and show the correct CLI, Python, or API server invocation.

Frequently Asked Questions about researchharness

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

FAQPage Schema
How do I run a lightweight AI agent runtime from the command line?▼

You can run a lightweight AI agent runtime from the CLI by invoking the configured command line interface. This mode allows you to launch the agent locally, inspect project workspaces, and retrieve answers while preserving trace output.

How do I embed a tool-using LLM agent into my Python application?▼

You can embed a tool-using LLM agent into your application using the Python API execution mode. This allows you to integrate agent runtime capabilities directly in Python code, configure custom tools, and manage compaction behavior for repeatable runs.

Can I use an OpenAI-compatible API server to call a local AI agent?▼

Yes, you can use an OpenAI-compatible API server to call a local AI agent. The runtime supports protocol-compatible tool execution, allowing you to send requests to the API server and manage environment variables with request-local overrides.

How do I debug workspaces and traces for a local LLM agent?▼

You can debug workspaces and traces for a local LLM agent by utilizing the runtime's debugging features. This involves inspecting trace output, validating tool schemas, and performing read-only source inspection to understand agent behavior.

What environment variables do I need to set up an AI agent runtime?▼

Setting up an AI agent runtime requires strict handling of environment variables to configure execution modes and tools. You must validate the required environment variables and apply request-local overrides to ensure safe, benchmark-friendly execution.

Does the agent runtime support custom tool schemas and compaction?▼

Yes, the agent runtime supports custom tool schemas and compaction. You can configure built-in tools, define custom tools, and manage compaction behavior to ensure repeatable runs and protocol-compatible tool execution across different sessions.

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