Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Search and ingest knowledge from Lore, a research repository with citations
Search and ingest knowledge from Lore, a research repository with citations
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Lore is a research knowledge repository you have access to via MCP tools. It stores documents, meeting notes, interviews, and decisions with full citations โ not just summaries, but the original content linked back to its source. Use it to ground your answers in evidence and to preserve important context from your conversations.
Push content into Lore using the ingest tool whenever you encounter information worth preserving: After conversations: When a user shares meeting notes, interview transcripts, or important documents, ingest them so they're searchable later. External content: When you fetch content from Slack, Notion, GitHub, email, or other systems, ingest the relevant parts into Lore. Decisions and context: When important decisions are made or context is shared that future conversations will need. Always include: source_url: The original URL (Slack permalink, Notion page URL, GitHub issue URL) for citation linking. source_name: A human-readable label like "Slack #product-team" or "GitHub issue #42". project: The project this content belongs to. Ingestion is idempotent โ calling ingest with the same content twice is safe and cheap (returns immediately with deduplicated: true).
Before answering questions about past decisions, user feedback, project history, or anything that might already be documented: Use search for quick lookups. Pick the right mode: hybrid (default): Best for most queries keyword: For exact terms, names, identifiers semantic: For conceptual queries ("user frustrations", "pain points") Use research only when the question requires cross-referencing multiple sources or synthesizing findings. It costs 10x more than search โ don't use it for simple lookups. Use get_source with include_content=true when you need the full original text of a specific document.
Use retain (not ingest) for short, discrete pieces of knowledge: Key decisions: "We chose X because Y" Synthesized insights: "3/5 users mentioned Z as their top issue" Requirements: "Must support SSO for enterprise"
When presenting information from Lore, always cite your sources: Reference the source title and date Quote directly when possible If a source_url is available, link to the original
User asks about past decisions: search("authentication approach decisions", project: "my-app") Review results, get full source if needed: get_source(source_id, include_content: true) Present findings with citations User shares meeting notes: ingest(content: "...", title: "Sprint Planning Jan 15", project: "my-app", source_type: "meeting", source_name: "Google Meet", participants: ["Alice", "Bob"]) Confirm ingestion to user User asks a broad research question: research(task: "What do users think about our onboarding flow?", project: "my-app") Present the synthesized findings with citations
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