context-memory

Persist and recall session data across skill invocations using SAVE, LOAD, INJECT, and CLEAR commands.

10|2|Updated Mar 8, 2026

Search Tags:#context #memory #pipeline #session #persist #skill-invocation

Ready to run

npx skills add https://github.com/mahmoud20138/Tradecraft --skill context-memory-mahmoud20138

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: context-memory
Source: https://github.com/mahmoud20138/Tradecraft/tree/main/plugins/tradecraft/skills/context-memory
Command: npx skills add https://github.com/mahmoud20138/Tradecraft --skill context-memory-mahmoud20138

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Multi-skill workflows often lose context between invocations. Context-memory provides a persistent store to save and inject session data so conversations stay coherent across steps.

Core Features & Use Cases

Save, load, inject, and clear session data to maintain continuity across skills.
Persist a memory store to ~/.claude/session_context.json and reuse analysis results, trade setups, and context in future interactions.
Use case: in a trading assistant, remember the last analyzed pair and current regime to tailor next steps.

Quick Start

Use context-memory to persist and inject session data across multiple skill invocations.

Frequently Asked Questions about context-memory

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

FAQPage Schema

How do I persist session state across multiple skill invocations?▼

To save and load session context in a chained prompt, use dedicated lifecycle commands like SAVE, LOAD, and INJECT. This ensures analysis results and session data stay coherent and are carried over automatically during long multi-skill workflows.

Why does my multi-skill workflow lose context between chained prompts?▼

Multi-skill workflows lose context between chained prompts because session data is not persisted by default. Implementing a defined memory schema to write to disk solves this by allowing subsequent skill calls to recall previous analysis results and context.

What is the best way to carry over trade setups and analysis results in an AI assistant?▼

The best way to carry over trade setups and analysis results is using a persistent in-memory to disk storage flow. This approach remembers the last analyzed pair and current regime to tailor the next steps in your trading assistant workflow.

Does saving session context to disk work for long trading assistant workflows?▼

Yes, saving session context to disk works for long trading assistant workflows by persisting a memory store to a local JSON file. This enables context carryover, ensuring conversations stay coherent across multiple steps and future interactions.

When should I clear persistent memory in a multi-skill pipeline?▼

You should clear persistent memory in a multi-skill pipeline when switching to a completely new analysis topic or ending a long session. Using a dedicated CLEAR command removes outdated trade setups and context, preventing stale data from affecting future skill invocations.