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Found 1,194 Skills
Reviews Rails pull requests, focusing on controller/model conventions, migration safety, query performance, and Rails Way compliance. Covers routing, ActiveRecord, security, caching, and background jobs. Use when reviewing existing Rails code for quality, conducting a PR review, or doing a code review on Ruby on Rails (RoR) code.
Detects common LLM coding agent artifacts by spawning 4 parallel subagents
Reference knowledge for Markuplint HTML linter. Covers violation interpretation, CLI usage, config patterns, and documentation URLs. Auto-loaded when working with HTML linting.
Grill the diff. Specialists evaluate every finding internally — only high-value findings reach the user for discussion until reaching shared understanding.
Implements SDD plan tasks following specs and design, marking progress in tasks.md as it goes. Trigger: /sdd-apply <change-name>, implement change, apply SDD tasks, write code for change.
Analyzes staged and unstaged files from the full working tree, groups them into functional clusters (test, docs, chore, directory prefix), generates a conventional commit message per group, detects common issues (secrets, debug statements, large files), and executes one commit per group sequentially after presenting a multi-commit plan for confirmation. Falls through to a single-commit flow when all detected files resolve to one group. Trigger: When the user says "commit", "smart commit", or /commit.
Aggressively clean up a codebase by removing AI slop, dead code, weak types, defensive over-engineering, duplication, and legacy cruft. Orchestrates 8 specialized subagents in parallel to deduplicate code, consolidate types, kill unused code, untangle circular dependencies, strengthen weak types, remove unnecessary try/catch, delete deprecated/legacy paths, and strip unhelpful comments. Use when the user asks to 'clean up the codebase', 'remove slop', 'improve code quality', 'remove dead code', 'kill AI slop', 'tighten types', 'remove legacy code', 'deduplicate code', 'DRY this up', 'untangle dependencies', or wants a thorough code quality pass. Also use when the user mentions code smells, technical debt cleanup, or refactoring for clarity — even if they don't use the word 'slop'.
Follow this sub-process when fixing bugs—turn the verbal description of "discovered a problem" into a closed loop of verification and repair, leaving three documents in the middle: issue report, root cause analysis, and repair record. This process adds a buffer between "seeing the problem" and "starting to modify code", avoiding several common pitfalls: the problem description in your mind disappears after modification, fixing only the surface without analyzing the root cause, uncontrollable expansion of repair scope that cannot be traced, and not knowing if the fix is correct without verification after modification. This skill only acts as a router, deciding which of report / analyze / fix to proceed with based on existing outputs. For simple problems that can be identified at a glance, a fast track will be taken, skipping the two middle steps and only keeping the fix-note.
Perform a thorough quality review of a pull request or feature branch before merging. Use this skill whenever the user asks to review a PR, check if code is production-ready, assess quality, verify docs are updated, or asks "is this ready to merge?", "review this PR", "check quality", "is this production ready?", or similar. Also use when reviewing your own work before submitting.
Ultra-lightweight channel for refactor processes - used when changes are obviously too small to justify the full scan → design → apply three-stage workflow. AI directly identifies 1-3 low-risk optimization points, confirms with the user once, modifies in-place using classic methods, and validates itself by running tests. No scan checklist, no design documentation, no multi-step HUMAN verification required. Trigger scenarios: When the user says "quick refactor", "small refactor", "simply optimize XX function", "modify directly", "skip all those steps", and the scope of changes is clearly limited to a single function/single component, with tests available for self-validation.
Phase 2 of the feature workflow —— Write code according to the implementation sequence in {slug}-design.md, and submit a completion report in a unified format for user review after finishing. Prerequisites: {slug}-design.md has been approved (standard design includes test design, or fastforward design includes acceptance criteria), and {slug}-checklist.yaml exists in the same directory. Trigger scenarios: User says "The plan is confirmed, start implementation", "Write code according to the plan", "Start working". If you encounter situations not covered by the plan during implementation (new concepts, out-of-scope files, need for patch branches), proactively stop and go back to discuss the plan instead of pushing forward blindly.
Ultra-lightweight channel for refactor processes - used when changes are clearly too small to go through the full scan → design → apply three-stage workflow. AI directly identifies 1-3 low-risk optimization points, confirms with the user once, modifies in-place using classic methods, and validates itself by running tests. No scan checklist, no design documentation, no multi-step human verification required. Trigger scenarios: User says "quick refactor", "small refactor", "simply optimize XX function", "modify directly", "skip the extra steps", and the scope of changes is clearly localized to a single function / single component with test coverage for self-validation.