Route user requests to adaptive multi-agent execution depths with selective context compression.
npx skills add https://github.com/kobolingfeng/ntk --skill ntk
Please help me install this Agent Skill. Skill: ntk Source: https://github.com/kobolingfeng/ntk/tree/main Command: npx skills add https://github.com/kobolingfeng/ntk --skill ntk
NTK solves the problem of excessive LLM context and cost by routing each request to the minimum-depth multi-agent pipeline needed for the task’s complexity, while compressing and selectively sharing information across agents.
Use case: When you ask for a simple function or translation, NTK stays in direct mode; when you ask for architecture design, it escalates to full mode with planning, verification, and compressed research steps.
Install NTK, create/edit a .env file with your OpenAI-compatible API endpoint credentials, then start the MCP server and call ntk_run from your MCP client.
High-intent search queries and answers about installing and using this skill.
Multi-agent pipelines reduce LLM token costs by applying adaptive complexity routing to select the minimum execution depth needed. This prevents over-processing simple code generation tasks by sharing only need-to-know context between agents instead of full history.
Yes, you can integrate multi-agent execution with an MCP server in VS Code Copilot or Claude Desktop. The system exposes adaptive routing capabilities as an MCP server, allowing you to call execution directly from any MCP-compatible AI client.
Adaptive complexity routing for LLM tasks automatically selects execution depth—direct, light, standard, or full—based on request complexity. It escalates simple requests to full mode with planning and verification only when needed, avoiding unnecessary token usage for basic queries.
To set up dual-model isolation for LLM cost optimization, configure a strong model exclusively for the highest-depth planning stage, while delegating low-depth execution to a cheaper model tier. You establish this by providing OpenAI-compatible API endpoint credentials in your environment setup.
Information compression with backtracking helps prevent hallucinations in system architecture design by compressing gathered information and recovering original text if verification fails. This improves reliability without always paying full-context token costs during multi-stage pipelines.
A limitation of need-to-know context sharing for multi-agent log comprehension is that agents receive only minimal context rather than full history. If selective compression removes critical details, tee recovery backtracking is required to restore original text for accurate verification.
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