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stevengonsalvez
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reflect:status

Monitor reflect system health, metrics, sidecar coverage, and GraphRAG status.

2|1|Updated Jun 19, 2026
Search Tags:#metrics#knowledge base#health check#system monitoring#reflect
One-click install
npx skills add https://github.com/stevengonsalvez/ainb-reflect-memory --skill reflect-status
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: reflect:status
Source: https://github.com/stevengonsalvez/ainb-reflect-memory/tree/main/plugin/skills/status
Command: npx skills add https://github.com/stevengonsalvez/ainb-reflect-memory --skill reflect-status

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides insights into the reflect system's state, including its learning metrics, pending reviews, sidecar coverage, and GraphRAG health, helping users ensure the system is functioning optimally.

Core Features & Use Cases

  • System State Monitoring: View the current state of the reflect system, including auto-reflect status, last reflection timestamp, and pending reviews.
  • Aggregate Metrics: Access metrics such as sessions analyzed, signals detected, changes proposed/accepted, and confidence breakdown.
  • Sidecar Coverage: Check the coverage of knowledge notes with entity sidecars and identify missing sidecars.
  • GraphRAG Health: Assess the health of the GraphRAG knowledge base, including the number of indexed learnings, nodes, and edges.
  • Orphaned Memory Stats: Review orphaned memory directories and lines across all orphaned directories.
  • Knowledge Gaps: Identify knowledge gaps that users keep asking about with no learnings in the KB.
  • Learning Update History: View the update history of each learning, including the number of updates and last-updated timestamp.
  • Contradictions: Detect contradictions in learnings, showing older and newer learning pairs with similarity and titles.
  • Pending Reviews: Approve or reject pending low-confidence items.
  • Staleness Check: Flag items that have been pending for more than 7 days.

Quick Start

Use the reflect status command to view the reflect system's status.

Frequently Asked Questions about reflect:status

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

FAQPage Schema
How do I monitor system health and performance metrics for a reflect system?▼

Monitoring system health involves tracking aggregate metrics like sessions analyzed, signals detected, and changes proposed. You can assess GraphRAG health, sidecar coverage, and identify knowledge gaps or contradictions to maintain optimal reflect system performance.

How do I check GraphRAG health and sidecar coverage in my knowledge base?▼

You can check GraphRAG health and sidecar coverage by assessing indexed learnings, nodes, and edges. The system identifies knowledge notes missing entity sidecars and highlights orphaned memory directories, ensuring your knowledge base remains fully covered and healthy.

What is the best way to detect contradictions and knowledge gaps in a knowledge base?▼

Detecting knowledge base contradictions and knowledge gaps requires analyzing learning update histories and similarity pairs. The system identifies gaps where user queries have no matching learnings and highlights older and newer contradictory learning pairs by similarity and title.

How do I manage pending reviews and flag stale low-confidence items in system monitoring?▼

To manage pending reviews and flag stale low-confidence items, you can approve or reject pending items directly. A staleness check automatically flags low-confidence items that have been pending for more than 7 days, ensuring your system monitoring review queue stays updated.

Do I need Python to run system monitoring and health checks on the reflect system?▼

You need Python installed to run system monitoring and health checks on the reflect system. The health check functionality relies on Python and specific reflect system components to accurately report on system metrics, learning metrics, and sidecar coverage.

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