What problem does it solve? Maintaining a large structured research wiki leads to broken wikilinks, missing frontmatter fields, asymmetric cross-references, and inconsistent graph edges that are hard to find manually. This Skill audits the entire wiki and produces a tiered, prioritized fix-recommendation report. ## Core Features & Use Cases - Automated linting: Runs tools/lint.py to detect broken wikilinks, orphan pages, missing required fields, and invalid enum values across all 9 entity types (papers, concepts, topics, people, ideas, experiments, claims, summaries, graph edges). - Cross-reference and graph validation: Verifies bidirectional link symmetry and checks edges.jsonl/citations.jsonl for valid JSON, required fields, edge types, and dangling nodes. - Tiered reporting with optional auto-fix: Outputs issues classified as 🔴 fix immediately, 🟡 recommended, and 🔵 optional; supports --fix, --dry-run, and --suggest modes. - Use Case: After ingesting dozens of new papers into your research wiki, run the check to find every broken claim reference, failed idea missing a failure_reason, and asymmetric concept-paper link before they compound. ## Quick Start Run the check skill on my wiki directory and give me the prioritized lint report of all structural and link issues.
