Results for “quality”

Codified expertise for quality control, non-conformance investigation, root cause analysis, corrective action, and supplier quality management in regulated manufacturing. Informed by quality engineers with 15+ years experience across FDA, IATF 16949, and AS9100 environments. Includes NCR lifecycle management, CAPA systems, SPC interpretation, and audit methodology. Use when investigating non-conformances, performing root cause analysis, managing CAPAs, interpreting SPC data, or handling supplier quality issues.

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Develop applications through separate planning, building, and evaluation agents. The workflow tests the running app, scores its design and functionality, and feeds specific issues back into repeated development cycles with configurable quality thresholds and iteration limits.

• Turning a brief idea into an application specification
• Refining frontend design through separate evaluation
• Testing and improving full-stack application features

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.

• Review the quality of an authentication module
• Find duplicate code and overly long methods
• Assess accumulated code problems and repair effort

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.

• Reviewing blog posts for SEO content quality
• Expanding thin product descriptions
• Checking whether landing pages answer search needs

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.

• Compare agent quality before and after prompt changes
• Test tool selection and multi-step task execution
• Evaluate resistance to prompt injection

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.

• Investigating why a skill received a low score
• Improving descriptions that define when a skill should run
• Setting score thresholds for marketplace listings

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.

• Check data for missing values and duplicates
• Build a dbt test suite
• Define shared data contracts between teams

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.

• Assessing recurring revenue quality in an investment target
• Tracking revenue by customer acquisition year
• Comparing acquisition costs with customer lifetime value

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.

• Check an AI workflow before production use
• Audit an existing agent for quality and reliability
• Prioritize fixes for workflow problems

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.

• Comparing models and prompts
• Checking quality before deployment
• Evaluating search quality in RAG applications

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.

• Clean web data for model training
• Remove exact and near duplicates from large text collections
• Redact personal information from training text

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.

• Turn a reusable feature into an agent skill
• Audit an existing skill for missing parts
• Check quality after fixing or extending a skill

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.

• Audit knowledge base quality
• Find isolated pages and broken links
• Review citations and inconsistent tags

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.

• Reviewing a specific Playwright test file
• Auditing test quality across a project
• Finding causes of unreliable test runs

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.

• Generating a starter project for a web application
• Choosing a technology stack within team and budget constraints
• Reviewing code security and complexity

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.

• Compare agent configurations against a baseline
• Catch quality regressions before deployment
• Measure the effects of context strategies

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.

• Clean web data for model training
• Remove exact and near duplicates from large text collections
• Redact personal information from training text

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.

• Reviewing a pull request
• Auditing code quality and security
• Finding refactoring opportunities

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.

• Check for drift during a long-running task
• Review repeated or contradictory output
• Track whether requested items have been dropped

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.

• Check data quality and enrichment signals
• Choose narrow or broad peaks for the biological target
• Create bigWig tracks and quality dashboards

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.

• Check missing values and duplicate records
• Investigate sudden changes in data quality
• Reconcile conflicting sources and metric definitions

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.

• Review HTTPS, security headers, and cookies
• Check dependencies and handling of user input
• Replace outdated APIs and improve browser compatibility

Inspect production AI agents in FailproofAI Cloud using the fp CLI. Review errors, run histories, quality scores, and usage, then manage issues, audits, user access, and scoped API keys for the team's deployment.

• Investigating failed agent runs
• Checking for drops in quality scores
• Running audits and triaging findings

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.

• Checking code quality before a commit
• Reviewing code after refactoring
• Finding complex functions and duplicated code