kb-lint
The kb-lint command runs automated health checks on a knowledge base project, validating registry coverage, detecting broken wikilinks, verifying index completeness, confirming canvas file validity, and flagging consistency issues. Use this command regularly to maintain KB integrity and identify structural problems before they impact navigation or content reliability.
mkdir -p ~/.claude/commands && curl -fsSL https://raw.githubusercontent.com/Galaxy-Dawn/claude-scholar/HEAD/commands/kb-lint.md -o ~/.claude/commands/kb-lint.mdkb-lint.md
# /kb-lint
```bash
python3 "${CLAUDE_PLUGIN_ROOT}/skills/obsidian-project-kb-core/scripts/kb_lint.py" --cwd "$PWD"
```
Checks include registry coverage, broken wikilinks, index coverage, canvas validity, and several KB consistency warnings.Expert code review specialist. Proactively reviews code for quality, security, and maintainability. Use immediately after writing or modifying code. MUST BE USED for all code changes.
Use this agent when the user provides a Kaggle competition URL or asks to learn from Kaggle winning solutions. Examples:
Use this agent when the user asks to "conduct literature review", "search for papers", "analyze research papers", "identify research gaps", "review related work", or mentions starting a research project. This agent integrates with Zotero for automated paper collection, organization, and full-text analysis. Examples:
Use this agent when the user provides a research paper (PDF/DOCX/arXiv link) or asks to learn writing patterns from papers, extract venue-specific writing signals, study paper structure, or mine rebuttal strategies. The agent writes extracted knowledge into the active installed paper-miner writing memory for ml-paper-writing. It does not maintain project-specific writing memory.
Use this agent when the user asks to "write rebuttal", "respond to reviewers", "analyze review comments", or needs help with academic paper review response. This agent specializes in systematic rebuttal writing with professional tone and structured responses.
Test-driven development guide for writing tests first, implementing the smallest passing change, and keeping verification tight. Use when the user explicitly wants TDD or when a task should be driven by failing tests before code.
Run a blocker-first post-experiment workflow: validate evidence, produce strict statistical analysis when possible, and generate a decision-oriented results report only when the analysis bundle is sufficient. Uses results-analysis + results-report as a gated two-stage workflow.
Commit changes following Conventional Commits format (local only, no push).