Multi-perspective academic paper review with dynamic reviewer personas. Simulates 5 independent reviewers (EIC + 3 peer reviewers + Devil's Advocate) with field-specific expertise. Supports full review, re-review (verification), quick assessment, methodology focus, and Socratic guided modes. Triggers on: review paper, peer review, manuscript review, referee report, review my paper, critique paper, simulate review, editorial review.
Use ONLY when creating NEW registrable components in ML projects that require Factory/Registry patterns. ✅ USE when: - Creating a new Dataset class (needs @register_dataset) - Creating a new Model class (needs @register_model) - Creating a new module directory with __init__.py factory - Initializing a new ML project structure from scratch - Adding new component types (Augmentation, CollateFunction, Metrics) ❌ DO NOT USE when: - Modifying existing functions or methods - Fixing bugs in existing code - Adding helper functions or utilities - Refactoring without adding new registrable components - Simple code changes to a single file - Modifying configuration files - Reading or understanding existing code Key indicator: Does the task require @register_* decorator or Factory pattern? If no, skip this skill.
This skill should be used when the user asks to "debug this", "fix this error", "investigate this bug", "troubleshoot this issue", "find the problem", "something is broken", "this isn't working", "why is this failing", or reports errors/exceptions/bugs. Provides systematic debugging workflow and common error patterns.
Use when the user asks to "implement DID", "run difference-in-differences", "set up IV regression", "instrumental variables", "run RDD", "regression discontinuity", "synthetic control", "staggered treatment", "Callaway-Sant'Anna", "Sun-Abraham", "event study", "parallel trends test", "first stage", "Bartik instrument", "shift-share IV", "write Stata regression code", "convert Stata to Python", "use statsmodels", "use linearmodels", "use econml", "panel data regression", "fixed effects", "two-way fixed effects", "cluster standard errors", or any causal inference methodology in Python or Stata.
Master effective code review practices to provide constructive feedback, catch bugs early, and foster knowledge sharing while maintaining team morale. Use when reviewing pull requests, establishing review standards, or mentoring developers.
This skill should be used when the user asks to "create a slash command", "add a command", "write a custom command", "define command arguments", "use command frontmatter", "organize commands", "create command with file references", "interactive command", "use AskUserQuestion in command", or needs guidance on slash command structure, YAML frontmatter fields, dynamic arguments, bash execution in commands, user interaction patterns, or command development best practices for Claude Code.
Daily coding assistant that auto-triggers when writing/modifying code, providing a core checklist. ✅ Trigger scenarios: - Implementing new features, adding code, modifying existing code - User requests "write a...", "implement...", "add...", "modify..." - Any coding task involving Edit/Write tools ❌ Does not trigger: - Pure reading/understanding code (no modification intent) - Already covered by specialized skills (bug-detective, architecture-design, tdd-guide) - Configuration file changes, documentation writing
Use when the user asks to "generate daily paper", "find recent papers", "search NBER working papers", "find new econ papers", "find new polisci papers", "search arXiv economics", "check journal updates", "what's new in political economy", "find papers on causal inference", "find papers on text as data", or wants paper summaries in comparative politics, political economy, political institutions, public economics, or economic history.