Load usage-weighted context summaries from the Gnosis MCP.
npx skills add https://github.com/nicholasglazer/gnosis-mcp --skill context-nicholasglazer
Please help me install this Agent Skill. Skill: context Source: https://github.com/nicholasglazer/gnosis-mcp/tree/main/skills/context Command: npx skills add https://github.com/nicholasglazer/gnosis-mcp --skill context-nicholasglazer
Load usage-weighted context from the Gnosis MCP to surface the most-accessed docs for session startup or topic primers.
Load the top-accessed documents at startup by requesting the MCP context summary.
High-intent search queries and answers about installing and using this skill.
Usage-weighted context loading fetches and prioritizes top-accessed documents from the Gnosis MCP, ranking them by access frequency and topic relevance to provide a concise summary for immediate session priming.
The best way to surface top-accessed documents for topic priming is integrating with MCP context APIs to fetch, rank by access frequency, and summarize documents, returning a concise context summary for quick retrieval.
Yes, context loading integrates directly with Gnosis MCP context APIs to fetch, rank, and summarize documents based on usage tracking, ensuring fast document retrieval and improved topic alignment.
Reduce search effort at session startup by loading a context summary that surfaces the most-accessed documents, prioritized by usage tracking and topic relevance, eliminating manual document searches.
Yes, usage tracking logs document access frequency during context loading, allowing the system to rank and surface the most relevant documents to improve agent briefing and response relevance.
Use usage-weighted context loading when you need to quickly prime an AI agent at session startup with the most relevant documents, rather than spending time manually searching and selecting context.
v36372Configure global or project MCP servers in mcp.json for stdio or HTTP connections.
frankxaiCodify prompts in the Arcanean Prompt Language with a five-pillar architecture.
JustineDevsManage a local markdown knowledge base with keyword and tag indexing.
0xRayAIConfigure chunking boundaries, embedding dimensions, and similarity metrics for retrieval-augmented generation pipelines.
