Store and retrieve memory documents and task context for Claude Code workflows.
npx skills add https://github.com/jamesaphoenix/tx --skill tx-memory-context
Please help me install this Agent Skill. Skill: tx-memory-context Source: https://github.com/jamesaphoenix/tx/tree/main/.claude/skills/tx-memory-context Command: npx skills add https://github.com/jamesaphoenix/tx --skill tx-memory-context
Memory is scattered across tools and tasks; this skill centralizes storage, recall, search, and relationship of memory documents, learnings, and task context to support prompt-aware AI interactions in Claude Code workflows.
Run tx memory context <task-id> to fetch memory relevant to a specific task and surface it for prompting.
High-intent search queries and answers about installing and using this skill.
Store and recall memory context by centralizing documents, learnings, and task context into a durable store that supports prompt-aware AI interactions. Agents retrieve task-relevant memory via context queries and linked references to improve response quality across sessions.
Memory context storage is a centralized system for saving documents, learnings, and task relationships to guide prompts. You need it when memory is scattered across tools and tasks, requiring durable context to maintain a coherent knowledge graph for AI agents.
Retrieve task-relevant memory by running `tx memory context <task-id>` to fetch stored documents, learnings, and linked references for that specific task. This surfaces context directly for prompting and decision-making within your workflow.
Yes, you can establish explicit relations between documents and tasks to maintain a coherent knowledge graph for agents. AI workflows traverse linked references and retrieve interconnected context, improving the accuracy of prompt-aware responses.
Yes, the skill supports Claude Code workflows and other AI agents requiring durable context across tasks, searches, and linked references. It provides a YAML frontmatter with an operational body and optional components like scripts, references, and assets for broad compatibility.
A centralized memory store for prompt context requires managing document relationships and task IDs manually to maintain accuracy. If memory is not properly linked or task queries are too broad, retrieved context may lack sufficient relevance for high-quality prompt-aware responses.
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