AI Decision System
Replayable, offline-verifiable records for AI-assisted go/no-go decisions: experiment promotions, typed model answers and human-approved actions.
翻译准备中
Local cognitive infrastructure for AI coding agents — semantic memory, repository intelligence, and MCP tools to reduce token usage.
翻译准备中
取自仓库 README
正在加载完整 README。 以下为已保存的摘录。
mcp-name: io.github.ashlesh-t/cognirepo
Persistent memory and context for any AI tool. Not a chatbot — infrastructure.
lookup_symbol returns file:line very quickly — grep takes 2–8 seconds. On Python repos ≥ 15K LOC, CogniRepo cuts AI coding agent token usage by ~30–78% vs. a targeted grep+read baseline (up to 96–99% vs. reading every matching file naively) — benchmarked on Flask, FastAPI, Celery, and Ansible (5,900+ files). Works with Claude Code, Cursor, and Gemini CLI. Fully offline. No API keys required for indexing or any of the 35 MCP tools.查看完整 README ↗目前无法加载完整 README。请通过原文链接阅读。 以下为已保存的摘录。
mcp-name: io.github.ashlesh-t/cognirepo
Persistent memory and context for any AI tool. Not a chatbot — infrastructure.
lookup_symbol returns file:line very quickly — grep takes 2–8 seconds. On Python repos ≥ 15K LOC, CogniRepo cuts AI coding agent token usage by ~30–78% vs. a targeted grep+read baseline (up to 96–99% vs. reading every matching file naively) — benchmarked on Flask, FastAPI, Celery, and Ansible (5,900+ files). Works with Claude Code, Cursor, and Gemini CLI. Fully offline. No API keys required for indexing or any of the 35 MCP tools.查看完整 README ↗