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system-learn

Ingest markdown files into ChromaDB for semantic procedural memory retrieval.

108|10|Updated Dec 29, 2024
Search Tags:#markdown#ChromaDB#learn#memory-ingestion#semantic-retrieval#procedural-memory#memory_manager
One-click install
npx skills add https://github.com/Qredence/agentic-fleet --skill system-learn
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: system-learn
Source: https://github.com/Qredence/agentic-fleet/tree/main/.fleet/context/system/learn
Command: npx skills add https://github.com/Qredence/agentic-fleet --skill system-learn

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

Ingests new procedural memory into the ChromaDB vector database to enable quick semantic retrieval and reuse of knowledge across agents.

Core Features & Use Cases

  • Ingests markdown files as procedural memory into the vector store for persistent retrieval.
  • Enables reuse of patterns, guides, and documentation by indexing them for semantic search during reasoning.
  • Use Case: Onboard new processes by converting guides into searchable memory that agents can recall during tasks.

Quick Start

uv run python .fleet/context/scripts/memory_manager.py learn --file <path_to_markdown_file>

Frequently Asked Questions about system-learn

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

FAQPage Schema
How do I ingest markdown files into ChromaDB for semantic retrieval?▼

Procedural memory ingestion stores reusable patterns and guides as vectors in ChromaDB, enabling agents to perform semantic search and recall relevant documentation during reasoning tasks across multiple operations.

How do I index procedural memory patterns for agents to recall during tasks?▼

Indexed procedural memory allows agents to retrieve patterns and documentation via semantic search during reasoning, ensuring consistent reuse of established processes across different agent operations.

Can I use markdown documentation as persistent memory for agent operations?▼

Markdown files ingested into the vector store become persistent procedural memory, allowing agents to access and reuse indexed documentation, guides, and patterns without re-reading the original files.

Do I need ChromaDB set up before ingesting procedural memory with memory_manager.py?▼

ChromaDB serves as the underlying vector store. Without it, the memory_manager.py script cannot persist ingested markdown patterns or enable the semantic retrieval required for agent knowledge recall.

What's the best way to onboard new process guides so agents can search them semantically?▼

Converting guides into markdown and ingesting them via the memory_manager.py script creates a searchable vector database, allowing agents to retrieve relevant procedural knowledge through semantic queries during operations.

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