Create and manage layered knowledge bases with hot, cold, and warm caches.
npx skills add https://github.com/Dqz00116/skill-lib --skill knowledge-base-cache
Please help me install this Agent Skill. Skill: knowledge-base-cache Source: https://github.com/Dqz00116/skill-lib/tree/main/knowledge-base-cache Command: npx skills add https://github.com/Dqz00116/skill-lib --skill knowledge-base-cache
Create and manage a layered knowledge base with hot/cold/warm caches to reduce API costs and enable scalable knowledge retrieval.
Initialize the knowledge base structure and run the cache manager to initialize and build the hot cache.
High-intent search queries and answers about installing and using this skill.
Layered knowledge caching reduces API costs by storing frequently accessed data in a hot cache and less active data in cold storage, enabling scalable semantic retrieval without repeated external queries. It uses automatic context assembly and token budgeting to optimize usage.
You build a working memory layer by running script-based cache management tools that coordinate multi-source inputs and assemble relevant context. This initializes a warm vector layer alongside hot and cold caches to enable fast knowledge access for AI agents.
The best way to manage token budgeting is using a three-tier cache architecture with automatic context assembly. This ensures AI agents retrieve only necessary structured knowledge from hot or warm caches, strictly controlling token consumption.
Yes, you can automatically refresh cached knowledge assets using built-in auto-refresh and cache-management tools. These scripts validate and update the hot cache and warm vector layers to keep structured knowledge fresh with minimal manual intervention.
Yes, this approach works for scalable multi-source semantic retrieval by implementing a warm vector layer and working memory. It coordinates multiple knowledge sources and assembles context dynamically, allowing AI agents to access structured data efficiently.
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evansantosAnalyze prompts and optimize token usage with windowing, caching, and routing.
