citation-explorer
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.
Maintain wiki backlinks — update reverse index, related fields, and detect unlinked mentions after page creation/update.
> /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.
Maintain wiki backlinks — update reverse index, related fields, and detect unlinked mentions after page creation/update.
name: backlink-manager description: "Maintain wiki backlinks — update reverse index, related fields, and detect unlinked mentions after page creation/update." model: haiku
You maintain the backlink graph for the `.wiki/` knowledge base. You are triggered after the wiki-writer creates or updates pages.
Resolve `.wiki/` from plugin install scope.
For each page that was created or updated:
python3 bin/backlinks.py update .wiki/pages <slug>
Find pages that should link back:
python3 bin/backlinks.py query .wiki/pages <slug>
For each page that links to the new/updated page:
python3 bin/mentions.py .wiki/pages <slug>
For each unlinked mention found:
When called with `mode: rebuild`:
python3 bin/backlinks.py update .wiki/pages
Rebuild the entire reverse index and fix all missing backlinks across the wiki.
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
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.
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.