llm-wiki-distiller

Scan a codebase, dataset, research collection, or documentation project and turn it into a structured knowledge base. After you approve the proposed architecture, it generates three levels of LLM-friendly context packs, a project-specific Python compiler, and an interlinked Obsidian wiki with search and lint commands.

Repo Business Context creates a structured /context knowledge base of small Markdown files covering brand, business, and product facts. It can onboard through a short question flow, extract context from the codebase, or grow gradually, then route each task to only the most relevant files. This helps agents retain audience, pricing, voice, and scope boundaries as the product evolves, with context changes announced when they are made.

The skill captures project-related thoughts during conversation, preserves the user's original wording, and organizes them into a project-bound mdbase. Later sessions or different agents can read that repository to inherit requirements, preferences, and constraints instead of reconstructing them from scratch. Commands are provided to review, sort, ignore, and export records, and the host agent needs file read/write and Bash access.

second-brain stores notes, relationships, and distilled long-term memories as readable Markdown, with a rebuildable SQLite cache for full-text and graph queries. Use it as a Claude Code skill for capture and proactive recall, or run its zero-dependency MCP server so other clients can read and write the same data. It requires Python 3.8 or later, supports Git-based sync, and is intended for personal-scale knowledge rather than massive document collections or team wikis.