Extract topic-specific information from an AI agent's conversation history into wiki pages.
npx skills add https://github.com/itsmarsss/memento --skill wiki-agent
Please help me install this Agent Skill. Skill: wiki-agent Source: https://github.com/itsmarsss/memento/tree/main/obsidian-wiki/.cursor/skills/wiki-agent Command: npx skills add https://github.com/itsmarsss/memento --skill wiki-agent
Users need fast, topic-specific knowledge from a different AI agent’s past work without bulk-ingesting everything they did.
Run /wiki-claude "how did I set up auth middleware" to search Claude’s history for that topic, ingest the relevant parts into the wiki, and get a synthesized answer immediately.
High-intent search queries and answers about installing and using this skill.
Extract targeted knowledge from another agent's history by querying a specific topic, scoring its past sessions, and ingesting only the relevant portions into your wiki. This provides an immediate synthesized answer for cross-agent debugging and knowledge transfer without bulk-importing raw history.
Transfer previous solutions by performing selective extraction and distillation from the other agent's raw history. This ingests only relevant blobs, updates your wiki pages with tracked sources, and returns an actionable synthesized summary directly into your current working session.
Yes, you can search another agent's raw history without bulk importing by using a query-driven approach. It efficiently reads the agent history inventory, scores and selects the top relevant sessions, and performs targeted extraction to merge only necessary information into your wiki.
Cross-agent knowledge ingestion works by retrieving topic-specific information from a chosen agent, extracting the relevant portions, and merging or updating existing wiki pages with tracked sources. This ensures your wiki remains current with cross-agent insights while maintaining source provenance.
If you lack a specific query for cross-agent history search, the system uses a recent unprocessed sessions mode. This flexible invocation mode automatically evaluates newer history entries that have not yet been ingested, ensuring ongoing knowledge transfer even without a targeted prompt.
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