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Review source code for readability, complexity, duplication, and maintainability without changing files. Receive a structured report with prioritized issues, refactoring suggestions, positive findings, and estimates of technical debt—the work needed to address accumulated code problems.
中文简介正在准备中。当前显示英语简介。
Review blog posts, product descriptions, and landing pages for SEO content quality. Check whether they answer readers' search needs, support claims with evidence, show relevant authorship, and offer specific details, then identify areas to rewrite or expand.
中文简介正在准备中。当前显示英语简介。
Build repeatable evaluations for AI agent responses, tool choices, and multi-step tasks. Use reference datasets and scoring criteria to compare changes, detect quality regressions, and set automated checks that must pass before deployment.
中文简介正在准备中。当前显示英语简介。
Explains how PluginEval combines structural checks and experimental model assessments into quality scores. Helps interpret low scores, identify improvement areas, set marketplace thresholds, and explain what evaluation badges actually represent.
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Build data quality checks with Great Expectations, dbt tests, and data contracts, which define shared expectations between teams. Check missing values, duplicates, and freshness, then integrate validation into data pipelines and CI/CD.
中文简介正在准备中。当前显示英语简介。
Analyze customer profitability and revenue durability for SaaS, subscription, and other recurring businesses. Examine acquisition costs, retention, and revenue trends by customer group, then produce an Excel workbook, summary slide, and issues for further investigation.
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Review an AI workflow across five areas: prompts, use of context, tools, system structure, and safety. Receive scores, specific findings, and prioritized actions to address quality and reliability problems.
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Evaluate LLM application quality using automated scores, human reviews, and another language model as a judge. Compare models or prompts, establish baseline results, and detect quality drops through repeatable tests and statistical comparisons.
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Prepare training datasets for large language models with GPU-assisted cleaning. Covers duplicate removal, quality filtering, and personal information redaction, with workflows for text, images, video, and audio using NeMo Curator.
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Turn a feature into an agent skill or improve an existing one. Define purpose-specific quality criteria, review outputs with models from three providers, then add tests and check that user requests reach the intended skill.
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Audit a gbrain knowledge base for outdated pages, broken or missing links, inconsistent tags, and missing citations. Get issue counts and specific repair actions, with additional checks for search quality and maintenance status.
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Review Playwright browser tests against 20 quality checks to find unreliable patterns and missing coverage. Get scores for each file, prioritized findings, and corrected code for critical issues.
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Choose a web application stack, generate a starter project, and assess existing code quality. Uses team size, release frequency, audience, and budget to guide recommendations, with reports covering security, complexity, dependencies, and prioritized improvements.
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Design evaluation systems that measure agent results across accuracy, completeness, citations, and tool efficiency. Build repeatable tests, compare changes against a baseline, and define quality checks that catch regressions before deployment and monitor production performance.
中文简介正在准备中。当前显示英语简介。
Prepare training datasets for large language models with GPU-assisted cleaning. Covers duplicate removal, quality filtering, and personal information redaction, with workflows for text, images, video, and audio using NeMo Curator.
中文简介正在准备中。当前显示英语简介。
Review code changes and files for bugs, security risks, performance problems, design issues, and test coverage. Receive a structured report with prioritized findings, concrete improvement examples, positive feedback, and an overall review verdict.
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Help an agent periodically review whether its work stays on task, remains thorough, avoids repetition, respects earlier decisions, and covers every item. Record assessments and revisit working notes when quality declines so the approach can be adjusted.
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Plan or run quality checks and peak calling, which identifies enriched genomic regions, for ChIP-seq, CUT&RUN, and CUT&Tag. The workflow accounts for controls and replicates and prepares signal tracks, quality reports, and differential binding designs.
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Assess whether data is trustworthy enough for analysis, dashboards, experiments, or modeling. Check missing values, duplicates, consistency, freshness, and changes over time, then explain the evidence, downstream risks, likely causes, and practical fixes.
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Review web code for security, browser compatibility, and code quality. Covers HTTPS, dependency vulnerabilities, security headers, outdated APIs, HTML structure, error handling, and cleanup of event listeners, with examples and audit checklists to guide improvements.
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Check code for complex logic, duplication, naming issues, and oversized functions. Get a quality report with file locations and severity levels, plus guidance on splitting complex code and extracting shared logic during reviews or after refactoring.
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VS-Enhanced Academic Style Auditor - Academic Writing Quality Analysis Identifies 24+ writing patterns that reduce scholarly quality, adapted from Wikipedia AI Cleanup guidelines Use when: checking drafts before submission, improving academic writing quality, preparing for style improvement Triggers: writing quality, style audit, pattern check, writing review, academic style check
中文简介正在准备中。当前显示原文说明。
Comprehensive truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability.
中文简介正在准备中。当前显示原文说明。
Comprehensive truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability.
中文简介正在准备中。当前显示原文说明。