Store stable facts in memory and isolate transient data for retrieval.
npx skills add https://github.com/HuaGCS/Hahobot --skill memory-huagcs
Please help me install this Agent Skill. Skill: memory Source: https://github.com/HuaGCS/Hahobot/tree/main/hahobot/skills/memory Command: npx skills add https://github.com/HuaGCS/Hahobot --skill memory-huagcs
Two-layer memory system that preserves long-term facts and event history while enabling fast, grep-based search over archived observations.
Load the memory files into your context and begin recording memory entries to test retrieval and search.
High-intent search queries and answers about installing and using this skill.
To persist long-term context across sessions, store stable project facts in a memory file that automatically loads into the active context. This maintains project preferences and ensures continuity throughout your knowledge-heavy workflows.
The best way to search an append-only event history in markdown is using grep-style search over a dedicated history file. This enables chronological reconstruction and auditing of events directly from your stored text data.
A structured memory archive supports timeline reconstruction by indexing observations into JSONL files and JSON chunks. This allows you to search historical data, expand timelines, and access archived context across multiple sessions.
Yes, a two-layer memory system isolates transient data from stable facts by storing long-term preferences separately from append-only event history. This prevents temporary context from overwriting persistent project knowledge.
No, the memory system does not require external dependencies for grep-based search. It relies entirely on native text files and structured JSON archives to perform fast, grep-style retrieval over stored observations.
Structured memory archives are stored using Markdown files for facts and history, alongside JSONL index files and JSON chunks for structured data. These formats support fast grep-style search and cross-session retrieval.
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