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Found 22 Skills
Validate quality before completing tasks. Performs verification checks, testing, and quality assurance before marking work complete.
AQE skill
Validate data quality in market analysis documents and blog articles before publication. Use when checking for price scale inconsistencies (ETF vs futures), instrument notation errors, date/day-of-week mismatches, allocation total errors, and unit mismatches. Supports English and Japanese content. Advisory mode -- flags issues as warnings for human review, not as blockers.
Converts documents to markdown with multi-tool orchestration for best quality. Supports Quick Mode (fast, single tool) and Heavy Mode (best quality, multi-tool merge). Use when converting PDF/DOCX/PPTX files to markdown, extracting images from documents, validating conversion quality, or needing LLM-optimized document output.
Validate code quality, test coverage, performance, and security. Use when verifying implemented features meet all standards and requirements before marking complete.
Research and add new strategic frameworks to the system (meta-skill). Use when user wants to add framework not in library; discovered new framework in their domain; asks "Can you add [Framework]?"; or no existing framework fits their need. Uses web search for comprehensive research, validates quality, creates framework files, and integrates into system.
Use this tool to review, audit, or validate the quality and cross-platform/cross-agent compatibility of Claude Code skills. It is triggered by phrases such as "审查 skill", "review skill", "检查 skill 质量", "skill 兼容性检查", "review 兼容性"
World-class presentation creation embodying principles from Garr Reynolds, Nancy Duarte, Guy Kawasaki, Seth Godin, and TED
Create comprehensive GitHub pull requests with quality validation
Adds schema tests and data quality validation to dbt models. Use when working with dbt tests for: (1) Adding or modifying tests in schema.yml files (2) Task mentions "test", "validate", "data quality", "unique", "not_null", or "accepted_values" (3) Ensuring data integrity - primary keys, foreign keys, relationships (4) Debugging test failures or understanding why dbt test failed Matches existing project test patterns and YAML style before adding new tests.
Golden dataset lifecycle patterns for curation, versioning, quality validation, and CI integration. Use when building evaluation datasets, managing dataset versions, validating quality scores, or integrating golden tests into pipelines.
Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.