example-data-processor
A modular data processing tool for cleaning, validating, and analyzing CSV files with support for custom transformations and automated dependency management.
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A modular data processing tool for cleaning, validating, and analyzing CSV files with support for custom transformations and automated dependency management.
Collaborative PR review using a swarm of three specialized AI agents (Correctness, Health, UX) that discuss findings and reach consensus before posting a structured summary with inline comments.
Validates Claude Code plugins against architectural standards, checking manifest files, frontmatter, and tool invocation patterns to ensure high-quality, compliant plugin development.
Synthesizes multi-agent research findings into coherent, citation-backed reports, resolving contradictions and identifying consensus.
Anthropic Claude integration patterns: streaming, RAG with pgvector, tool use, model selection (Haiku/Sonnet/Opus), prompt caching, and cost management for AI-powered engineering.
Comprehensive ABAP development support for SAP systems, covering classic ABAP, ABAP Cloud, CDS views, RAP, EML, and modern syntax patterns.
Build a cohesive, constraint-based design system using the Design Graph methodology. Automate the creation of design tokens, typography scales, components, variants, and themes.
Comprehensive SEO optimization tool for web applications. Performs automated site audits, meta tag management, structured data implementation, and technical performance analysis for Next.js, Astro, React, and static sites.
Systematic security assessment using STRIDE threat modeling, OWASP top 10 review, and secure coding practices for code, architecture, and infrastructure.
GitHub operations via gh CLI. Use for repository inspection, issues, PRs, releases, and deep codebase analysis including cloning for architectural insights.
Automates the documentation of solved technical issues using YAML frontmatter, categorized directories, and institutional knowledge indexing for JUCE plugin development.
Applies current Go testing best practices, including concurrent testing, mocking, and table-driven design for robust software development.