notebook-llmgraph-update-pattern

Synchronize NotebookLLMGraph cache writes with TaggingRules persistence after external updates.

3|Updated Mar 17, 2026
One-click install
npx skills add https://github.com/transreal/claudecode --skill notebook-llmgraph-update-pattern
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: notebook-llmgraph-update-pattern
Source: https://github.com/transreal/claudecode/tree/main/Claude%20Directives/skills/notebook-llmgraph-update-pattern
Command: npx skills add https://github.com/transreal/claudecode --skill notebook-llmgraph-update-pattern

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill prevents a subtle consistency bug where newly added or updated NotebookLLMGraph nodes are not visible immediately after being saved by external packages, due to stale in-memory cache behavior.

Core Features & Use Cases

  • Cache-correct update pattern: Ensures the LLM graph is written to both the in-memory cache and the notebook TaggingRules via the correct flush sequence.
  • Guided implementation blueprint: Provides a ready-to-adapt Mathematica-style example for recording state graph runtime nodes into NotebookLLMGraph.
  • Debugging and test validation: Offers test patterns (dynamic key detection and cache dump helpers) to quickly diagnose cache/flush mismatches and context resolution issues.

Quick Start

Use this Skill’s cache-first-then-flush order to update NotebookLLMGraph from an external package so that NotebookLLMGraphNodes immediately reflects the new nodes.

Frequently Asked Questions about notebook-llmgraph-update-pattern

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

FAQPage Schema
Why are NotebookLLMGraph nodes missing or stale after external package updates in Mathematica?

Missing NotebookLLMGraph nodes occur when external package updates are not flushed correctly, requiring synchronization of in-memory cache writes with TaggingRules persistence to guarantee immediate consistency.

How do I update NotebookLLMGraph nodes from an external package to ensure cache consistency?

To ensure cache consistency when updating NotebookLLMGraph nodes from an external package, follow the cache-update-then-flush pattern: write to $iLLMGraphCache/$iLLMGraphCacheNB, then call iLLMGraphFlush to persist to TaggingRules.

What is the correct cache and flush sequence for recording state-graph runtime nodes in Mathematica?

The correct sequence for recording state-graph runtime nodes is to write to $iLLMGraphCache/$iLLMGraphCacheNB first, then call iLLMGraphFlush to synchronize the in-memory cache with TaggingRules persistence for immediate visibility.

How can I debug cache and flush mismatches when integrating third-party packages with NotebookLLMGraph?

Debug cache and flush mismatches during third-party package integration by using dynamic key detection and cache dump helpers to verify that $iLLMGraphCache writes properly precede iLLMGraphFlush calls in the NotebookLLMGraph workflow.

Does integrating third-party state-graph recording workflows in Mathematica require special cache handling?

Yes, integrating third-party packages that mutate LLM graph nodes during state-graph recording requires the cache-update-then-flush pattern ($iLLMGraphCache/$iLLMGraphCacheNB then iLLMGraphFlush) to maintain NotebookLLMGraph consistency.

What are the limitations of relying on in-memory cache for NotebookLLMGraph updates?

Relying solely on in-memory cache without calling iLLMGraphFlush limits NotebookLLMGraph updates to temporary state, preventing TaggingRules persistence and causing newly added nodes to remain invisible in subsequent queries.