observatory

Analyze agent execution traces to identify failure patterns and inefficiencies.

191|29|Updated Sep 2, 2025

Search Tags:#performance #ai #agent #feedback loop #observability #improvement

Ready to run

npx skills add https://github.com/juanandresgs/claude-ctrl --skill observatory

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: observatory
Source: https://github.com/juanandresgs/claude-ctrl/tree/main/skills/observatory
Command: npx skills add https://github.com/juanandresgs/claude-ctrl --skill observatory

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill addresses the challenge of AI agents not learning from their mistakes or inefficiencies, leading to repeated errors and suboptimal performance over time.

Core Features & Use Cases

Automated Analysis: Continuously analyzes agent execution traces to identify patterns of failure and inefficiency.
Actionable Suggestions: Proposes concrete, prioritized improvements to the system or agent behavior.
Feedback Loop: Tracks accepted, rejected, and deferred suggestions to refine the AI's operation.
Use Case: After an agent repeatedly fails to correctly parse a specific file format, the observatory identifies this pattern, suggests a code fix in the parsing script, and tracks whether the fix resolves the issue.

Quick Start

Run the observatory skill to analyze agent traces and suggest an improvement.

Frequently Asked Questions about observatory

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

FAQPage Schema

How do I analyze AI agent execution traces to find recurring failure patterns?▼

To analyze AI agent execution traces for recurring failure patterns, this Skill continuously processes trace summaries via bash scripts to identify recurring failure patterns and inefficiencies. It surfaces actionable improvement signals to enhance agent performance.

What is an AI agent feedback loop and how does observability improve performance?▼

An AI agent feedback loop uses observability to continuously analyze execution traces and propose targeted system enhancements. This Skill manages the feedback loop by tracking accepted, rejected, and deferred suggestions to refine agent operation.

How do I track accepted and rejected improvement suggestions for my AI agent?▼

You track accepted and rejected improvement suggestions for your AI agent by using this Skill. It manages state files to record the implementation status of proposed enhancements, creating a closed feedback loop that monitors whether fixes resolve identified issues.

Does this agent observability tool require any specific dependencies or environments?▼

This agent observability tool requires a bash environment to run its scripts. It has no external dependencies but requires agent execution trace summaries and state files to process data and manage improvement suggestion history effectively.

What is the best way to stop an AI agent from repeating the same errors?▼

The best way to stop an AI agent from repeating errors is to implement an automated observability feedback loop. This Skill analyzes execution traces, proposes targeted fixes for approval, and tracks their implementation status to prevent recurrence.

Why does my AI agent fail to learn from repeated inefficiencies over time?▼

Your AI agent fails to learn from repeated inefficiencies because it lacks an automated observability mechanism. This Skill solves that by analyzing execution traces, proposing prioritized improvements, and tracking implementation status to build a learning loop.