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ehfazrezwan
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recall

Load stored user and project context from the Neuralscape memory system.

5|1|Updated Feb 10, 2026
Search Tags:#project context#ai assistant#context loading#user preferences#mcp tools#memory retrieval#neuralscape
One-click install
npx skills add https://github.com/ehfazrezwan/neuralscape --skill recall-ehfazrezwan
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: recall
Source: https://github.com/ehfazrezwan/neuralscape/tree/main/neuralscape-plugin/skills/recall
Command: npx skills add https://github.com/ehfazrezwan/neuralscape --skill recall-ehfazrezwan

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

AI assistants typically start every task without access to the user's stored preferences, project conventions, past technical decisions, and historical context, leading to repetitive questions, inconsistent outputs, and context-blind recommendations that waste the user's time.

Core Features & Use Cases

  • On-Demand Context Loading: Retrieve stored user preferences, tech stack details, and past decisions for any task without manual re-entry.
  • Project-Scoped Retrieval: Load context specific to an active project to ensure consistency with team conventions and prior project choices.
  • Flexible Retrieval Modes: Choose between relevance search for topic-specific queries, timeline views for work catch-up, and identity card loading for session grounding.
  • Use Case: When starting work on a new feature for a user's SaaS project, use this Skill to load their stored preference for Python, their team's convention of using FastAPI, and their past decision to use PostgreSQL, eliminating the need to re-ask these details.

Quick Start

Use the recall skill to load all stored context for your active project before beginning work on the new user authentication feature.

Frequently Asked Questions about recall

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

FAQPage Schema
How do I load stored user preferences and project context for an AI assistant?▼

To load stored user context, retrieve user preferences and project conventions from the Neuralscape memory system using native MCP tools. This eliminates cold starts and prevents repetitive questions during task execution by applying relevance search, timeline queries, or identity card loading.

Why does my AI assistant ask repetitive questions when starting a new feature development task?▼

AI assistants ask repetitive questions due to cold starts, lacking access to prior technical decisions and project context. Loading stored memory via MCP tools retrieves historical context and team conventions, eliminating the need for manual re-entry and ensuring consistent outputs.

Can I retrieve project-scoped memory for session initialization without manual REST API interaction?▼

Yes, you can retrieve project-scoped memory without manual REST API interaction by using native MCP tools for on-demand memory retrieval. This supports project-wide context loading, ensuring consistency with team conventions and prior project choices during session initialization.

What is the best way to catch up on past technical decisions before working on an existing SaaS project?▼

The best way to catch up on past technical decisions is using timeline views to load historical context from the Neuralscape memory system. This project-scoped retrieval mode provides work catch-up capabilities, loading stored details like tech stack choices without manual re-entry.

Does context loading support relevance search for topic-specific queries during work catch-up?▼

Yes, context loading supports relevance search for topic-specific queries alongside timeline views for work catch-up and identity card loading for session grounding. These flexible retrieval modes use native MCP tools to fetch stored user preferences and historical context on-demand.

When should I not use automated memory retrieval for AI context loading?▼

Automated memory retrieval is not suitable when no prior user preferences, project conventions, or historical context exist in the Neuralscape memory system. Without stored data to query via MCP tools, the context loading process cannot eliminate cold starts or provide project-scoped retrieval.

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