Results for “quality”

Guides production LLM operations: RAG pipelines, embeddings, vector databases, fine-tuning, prompt engineering, cost estimation, quality evals, and AI architectures.

Launches coordinated agent teams for tasks requiring persistent cross-talk, shared state, and lifecycle management, with team design, advisor coordination, and quality gates.

Runs Stanford's STORM pipeline to produce Wikipedia-quality research articles with citations, using Claude models and a search engine API.

Prevents AI agents from calling methods, imports, or variables that do not provably exist in the user's installed version, reducing hallucinated code.

Turns model work into a production ML system with data contracts, reproducible training, quality gates, deployable artifacts, and monitoring.

Runs a multi-agent adversarial verification loop where two independent reviewers must both pass before output ships, with a fix cycle for convergence.

Enforces SQL quality gates after every implementation: lint with SQLFluff, validate migrations with Flyway or Liquibase, and run database tests with pgTAP or equivalents.

Applies four disciplined mindsets—audit, debt, help, review—to cut complexity, track deferrals, surface reference, and catch over-engineering in codebases.

Analyze single-cell RNA-seq data with Scanpy, covering quality control, normalization, clustering, marker gene identification, visualization, and trajectory analysis.

Identifies high-quality leads for your product or service by analyzing your business, searching for target companies, and providing actionable contact strategies. Perfect for sales, business development, and marketing professionals.

Find bugs, security vulnerabilities, and code quality issues in local branch changes. Use when asked to review changes, find bugs, security review, or audit code on the current branch.

Evaluate Agent Skill design quality against official specifications and best practices. Use when reviewing, auditing, or improving SKILL.md files and skill packages. Provides multi-dimensional scoring and actionable improvement suggestions.

Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.

Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.

Identifies high-quality leads for your product or service by analyzing your business, searching for target companies, and providing actionable contact strategies. Perfect for sales, business development, and marketing professionals.

Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.

Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.

Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.

Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.

Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.

Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.

Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.

Enforces a disciplined Red-Green-Refactor (TDD) workflow in TypeScript/Node.js. Use this whenever creating new features, fixing bugs, or migrating logic to ensure high-quality, verifiable implementations.

Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.

Optimizes agent context setup by structuring rules, specs, source files, error output, and conversation history to improve output quality.

Write growth logs that extract reusable patterns from complex tasks, failures, and learning moments, using a structured template and quality checklist.

Provides a rigorous thinking partner profile that challenges assumptions, calibrates claims to evidence, and improves decision quality under uncertainty.

Analyze Neuropixels extracellular recordings end-to-end with SpikeInterface, covering loading, preprocessing, drift correction, spike sorting, quality metrics, and unit curation.

Manage software complexity through deep modules, information hiding, and strategic programming, with a scoring framework for evaluating design quality.

Generate production-quality React + Tailwind CSS code using the Rayden UI component library with correct props, design tokens, and layout patterns.

Audit paid-ad creative assets for quality, platform compliance, fatigue, and performance, producing prioritized refresh or experiment briefs.

Provides statistical process control guidance including control chart selection, capability indices, and out-of-control rules for quality management.

Generates images via OpenAI, Google, DashScope, and Replicate APIs, supporting text prompts, reference images, aspect ratios, and quality presets.

Routes to the right Salesforce data skill package for data model, migration, bulk loads, query optimization, deduplication, and archival tasks.

Guides the red-green-refactor cycle for implementing features with test-driven development, including test quality guidelines, framework setup, and commit patterns.

Guides the creation, editing, and verification of agent skills using a test-driven development approach, with structured documentation and quality gates.