Loading...
Loading...
Found 209 Skills
pytest, data validation, Great Expectations, and quality assurance for data systems
Create comprehensive test plans considering all test types and dependencies
Run findings-first review for Atlan app changes and synchronize app documentation with implemented behavior. Use when completing a change set, preparing handoff, or auditing regressions.
Use this skill when you need to create high-quality test cases with normal, exception, and boundary scenarios; triggers include test case writing and test design.
Performs a final quality pass fixing alignment, spacing, consistency, and micro-detail issues before shipping. Use when the user mentions polish, finishing touches, pre-launch review, something looks off, or wants to go from good to great.
Analyzes raw requirements into visual specs and generates artifacts. Invoke when user runs /vspec:new for analysis or /vspec:verify for models and prototypes.
This skill should be used when the user asks to "fix my skill" or "audit this skill". Make sure to use this skill whenever the user mentions skill quality, structural issues, broken skills, or skill diagnostics — even if they don't explicitly say "repair-skill". Not for adding features or improving effectiveness — use improve-skill. Not for agents — use repair-agent.
Meta-skill for validating the integrity and quality of other skills. automatically checks for SKILL.md existence, script syntax errors (via Godot CLI), and metadata completeness. Use this skill to verify the entire skill library. Trigger keywords: validation, continuous_integration, quality_assurance, syntax_check, metadata_check.
Complete quality assurance workflow orchestrating validation, comprehensive review, and functional testing. Sequential workflow from quality gating through multi-dimensional review to scenario testing. Use when conducting complete skill quality assurance, pre-deployment validation, or comprehensive quality checks combining multiple review approaches.
Deep code audit that finds dead wiring, silent failures, unfinished features, placeholder stubs, bloated files, and unnecessary complexity. Produces an actionable report with file:line references grouped by severity. Think of it as a senior dev doing a thorough PR review of the entire codebase. Triggers on: "code review", "audit the code", "review the code", "find dead code", "find placeholders", "check for stubs", "prune the code", "code cleanup", "implementation review", "completeness check", "find unused code".
Comprehensive content review and quality assurance for BRD documents - validates link integrity, requirement completeness, strategic alignment, and identifies issues requiring manual attention
Review backend code for quality, security, maintainability, and best practices based on established checklist rules. Use when the user requests a review, analysis, or improvement of backend files (e.g., `.py`) under the `api/` directory. Do NOT use for frontend files (e.g., `.tsx`, `.ts`, `.js`). Supports pending-change review, code snippets review, and file-focused review.