Manage AgenticFleet development sessions with dual memory and auto-learning.
npx skills add https://github.com/Qredence/agentic-fleet --skill fleet-agent
Please help me install this Agent Skill. Skill: fleet-agent Source: https://github.com/Qredence/agentic-fleet/tree/main/.fleet/context/system/fleet-agent Command: npx skills add https://github.com/Qredence/agentic-fleet --skill fleet-agent
This Skill provides a context-aware development assistant for AgenticFleet that maintains persistent memory across sessions using NeonDB and ChromaDB, enabling seamless context retention and auto-learning.
Start a new development session with the fleet-agent, then load core memory blocks and begin interactive context management. For example: /fleet-agent session start
High-intent search queries and answers about installing and using this skill.
Persistent context management works by using dual storage with NeonDB for structured data and ChromaDB for semantic search to preserve development context. This combo retains session data across tasks and enables seamless auto-learning.
Start a development session by triggering the fleet-agent session start command to load core memory blocks. This initiates interactive context management and auto-learning for ongoing code analysis and pattern extraction.
Yes, DSPy-enabled reasoning is integrated directly to assist with code analysis and session management. It accelerates development workflows by providing structured reasoning within the AgenticFleet ecosystem.
Yes, it supports structured data and semantic search simultaneously through its dual memory architecture. NeonDB handles structured data blocks while ChromaDB provides semantic search for on-demand topic blocks.
The best way to capture recurring workflows is utilizing the auto-learning and pattern extraction features. These automatically detect and store recurring development patterns to improve guidance without manual reconfiguration.
Dual memory separates structured data in NeonDB from semantic search in ChromaDB to optimize distinct context retrieval needs. This division supports both core memory blocks and on-demand topic blocks efficiently.
nedaktov-opsInitialize a MemPalace workspace with a ChromaDB backend and project scaffold.
a29paulReview, search, prune, and export project learnings from JSONL files.
mefardalesIntegrate vector databases for semantic search and RAG pipelines.
aarushlohitStores and retrieves context from past AI conversations using ChromaDB vector database.
