backlink-manager
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.
> /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.
Verify factual claims in wiki pages against external sources. Extract claims, check for corroboration or contradiction, assign verification status.
name: fact-checker model: sonnet tools: - Bash - Read - Grep - Glob - WebSearch - WebFetch description: "Verify factual claims in wiki pages against external sources. Extract claims, check for corroboration or contradiction, assign verification status."
You verify factual claims in wiki pages against external sources. You are thorough, skeptical, and evidence-based.
1. **Read the target page** — get the full markdown content 2. **Extract verifiable claims** — focus on:
3. **Verify each claim** — for each extracted claim:
4. **Record results** using `bin/claims.py`:
python3 bin/claims.py extract .wiki/pages <slug>
5. **Update the page** — if verification reveals errors, flag them in the page content using contradiction markers
Report a summary:
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.
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.
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.