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accessing-knowledge

Retrieve architectural context and documentation from `.agent/knowledge/` files.

Updated Nov 14, 2025
Search Tags:#architecture#documentation#context#knowledge#system map
One-click install
npx skills add https://github.com/ernitpt/ernit_test --skill accessing-knowledge
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: accessing-knowledge
Source: https://github.com/ernitpt/ernit_test/tree/main/.agent/skills/accessing-knowledge
Command: npx skills add https://github.com/ernitpt/ernit_test --skill accessing-knowledge

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides the AI with essential high-level architectural context and documentation, ensuring it operates with accurate, up-to-date information about the project's systems.

Core Features & Use Cases

  • Contextual Grounding: Retrieves architectural overviews and specific system documentation.
  • Efficient Discovery: Helps the AI understand system relationships and available documentation before diving into code.
  • Use Case: When starting a new task related to the "Goals" system, the AI uses this skill to first read system-map.md and then goals.md to understand its architecture and constraints.

Quick Start

Use the accessing-knowledge skill to retrieve the architecture overview for the project.

Frequently Asked Questions about accessing-knowledge

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

FAQPage Schema
How do I provide architectural context to an AI before modifying a software system?▼

To provide architectural context, you use a skill that retrieves high-level project documentation from a `.agent/knowledge/` directory. This grounds the AI in accurate system interdependencies before it analyzes the codebase.

What is the best way to map system interdependencies for an AI assistant?▼

The best way to map system interdependencies is by maintaining a `system-map.md` file. The AI reads this architecture overview to understand project constraints and available knowledge resources efficiently.

How do I ground an AI in project truth using documentation files?▼

You ground an AI in project truth by storing specific system `.md` files within a `.agent/knowledge/` directory. The AI retrieves these documents to ensure it operates with up-to-date architectural information.

Does this architectural context retrieval require specific file formats?▼

Yes, this architectural context retrieval requires markdown files. You must structure your project knowledge into a `system-map.md` file and individual system `.md` files within a `.agent/knowledge/` directory.

When do I need to retrieve project documentation for system architecture?▼

You need to retrieve project documentation when starting a new task related to a specific project system. Reading the architecture overview first helps the AI understand constraints before diving into code.

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