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rajnishkhatri
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agentsframework-eval-probe

Monitor and evaluate LLM-calls in software repositories with tiered L1, L2, L3 probes.

Updated Apr 17, 2026
Search Tags:#monitoring#software development#evaluation#repository#LLM-calls
One-click install
npx skills add https://github.com/rajnishkhatri/AgentsFramework --skill agentsframework-eval-probe
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agentsframework-eval-probe
Source: https://github.com/rajnishkhatri/AgentsFramework/tree/main/.cursor/skills/agentsframework-eval-probe
Command: npx skills add https://github.com/rajnishkhatri/AgentsFramework --skill agentsframework-eval-probe

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires python, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill allows for the integration of a continuous-evaluation PROBE into any repository, ensuring ongoing assessment and improvement of LLM-calls within the system.

Core Features & Use Cases

  • Continuous Evaluation: Monitor and evaluate LLM-calls in real-time to detect issues early.
  • Tiered Probes: Implement different levels of probes (L1, L2, L3) to handle different types of evaluation and data analysis.
  • Use Case: Utilize this Skill in a software development environment to ensure the reliability and accuracy of LLM-calls within your application, such as an AI agent or chatbot.

Quick Start

Deploy the agentsframework-eval-probe Skill into your repository to automatically begin monitoring and evaluating LLM-calls.

Frequently Asked Questions about agentsframework-eval-probe

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

FAQPage Schema
How do I add continuous evaluation for LLM calls in my repository?▼

You can implement continuous evaluation for LLM calls by deploying this Skill into your repository, which automatically monitors and evaluates LLM-calls in real-time to detect issues early and ensure ongoing reliability.

What are tiered probes for LLM call monitoring?▼

Tiered probes are evaluation levels (L1, L2, L3) used to handle different types of data analysis and assessment for LLM-calls within a software repository, enabling targeted real-time monitoring and improvement.

Do I need Python to set up continuous evaluation probes for LLM calls?▼

Yes, you need Python to set up continuous evaluation probes for LLM calls, as the monitoring framework requires specific Python libraries and repository setup to function correctly.

When should I use continuous evaluation for LLM calls?▼

You should use continuous evaluation for LLM calls in software development environments where maintaining the reliability and accuracy of AI agents or chatbots is critical, ensuring ongoing assessment and early issue detection.

What's the best way to monitor LLM calls in a software repository?▼

The best way to monitor LLM calls is by integrating a continuous-evaluation probe that supports tiered implementation (L1, L2, L3) for real-time assessment, automatically evaluating calls as your application runs.

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