Build a persistent, incrementally updated wiki from ingested sources.
npx skills add https://github.com/selmakcby/knowledge-pipeline --skill llm-wiki-selmakcby
Please help me install this Agent Skill. Skill: llm-wiki Source: https://github.com/selmakcby/knowledge-pipeline/tree/main Command: npx skills add https://github.com/selmakcby/knowledge-pipeline --skill llm-wiki-selmakcby
Standard LLM retrieval-augmented generation (RAG) fails to build cumulative, persistent knowledge: every query re-discovers information from scratch, context windows fill up and compress away nuance, and manual wiki maintenance creates unsustainable overhead for teams and individuals.
Use the llm-wiki skill to set up a persistent Obsidian vault for your research project, then ingest your first source document to start building your cumulative, searchable knowledge base.
High-intent search queries and answers about installing and using this skill.
To build cumulative knowledge from LLM chats, you can use a persistent wiki that ingests sources and incrementally updates pages. This prevents context window compression and avoids re-discovering information from scratch on every query.
The best way to maintain long-term wiki consistency is enforcing strict guardrails like immutable raw source storage, source-backed claims, and append-only logging. This ensures trustworthiness by marking conflicts instead of deleting them.
To set up an Obsidian vault for academic research, use the llm-wiki skill to configure a persistent storage location, then ingest your first source document to start building your searchable, cumulative knowledge base automatically.
Yes, this knowledge management approach works for product development and competitive analysis because it features a domain-agnostic design. It supports diverse use cases by querying a persistent wiki with full source attribution.
To catch conflicts and orphan pages in an automated knowledge base, run periodic health checks. These health checks identify stale claims, orphan pages, and conflicts to maintain long-term wiki consistency and trustworthiness.
Standard RAG fails at persistent knowledge retrieval because every query re-discovers information from scratch and context windows compress away nuance. Wiki automation solves this by building an incrementally updated, source-backed knowledge structure.
Nero-kkInitialize an Obsidian vault with directories, configuration files, and sanity checks.
tommy-fcyScan wiki directories for orphan pages, broken links, and frontmatter issues.
zephyrwang6Save Markdown documents to Feishu wiki and read bitable tables via Python scripts.
aleonsaProvides design patterns and best practices for building Retrieval-Augmented Generation pipelines.
