backlink-manager
Maintain wiki backlinks — update reverse index, related fields, and detect unlinked mentions after page creation/update.
Explore citation chains for a topic. Takes a seed paper or topic, uses web search to trace citation relationships, identifies key papers for wiki ingestion.
> /plugin marketplace add Oshayr/LLM-Wiki > /plugin install llm-wiki@llm-wiki
How it fires
How this agent gets triggered: by you, by Claude, or both.
Context preview
The summary Claude sees to decide when to auto-load this agent.
Explore citation chains for a topic. Takes a seed paper or topic, uses web search to trace citation relationships, identifies key papers for wiki ingestion.
name: citation-explorer model: sonnet tools: - Bash - Read - Grep - Glob - WebSearch - WebFetch description: "Explore citation chains for a topic. Takes a seed paper or topic, uses web search to trace citation relationships, identifies key papers for wiki ingestion."
You explore citation chains to find relevant papers for the wiki using web search.
1. **Identify the seed paper** — use the provided DOI, title, or topic 2. **Trace citations** — use `bin/citation_graph.py` for structured traversal:
3. **Analyze results** — identify the most relevant papers by:
4. **Recommend top 5-10 papers** for wiki ingestion 5. **Optionally ingest** the top papers using the wiki-writer agent
An autonomous knowledge base that grows as you work. LLM Wiki is a Claude Code plugin that captures research, ideas, and decisions into an interlinked wiki with semantic search, automatic research, and a Wikipedia-style web UI.
Repo: Oshayr/LLM-Wiki
Maintain wiki backlinks — update reverse index, related fields, and detect unlinked mentions after page creation/update.
Verify factual claims in wiki pages against external sources. Extract claims, check for corroboration or contradiction, assign verification status.
Autonomous iterative research loop — hypothesis, search, ingest, evaluate, keep/discard via checkpoint. Max 3 iterations.
Post-process research results — condense findings or deduplicate parallel agent outputs. Two modes.
Parameterized search channel — web, academic, code, docs, or wikipedia. Returns normalized result arrays.
Multi-channel search orchestration — classifies complexity, fans out to channel subagents, deduplicates and ranks results.