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ADK Agent Development And Engineering

jeremylongshore/claude-code-plugins-plus-skills

A specialized skill for architecting, building, and productionizing complex Agent Development Kit (ADK) agents and multi-agent systems. This process emphasizes best practices, including clean module structure, robust tool integration, comprehensive regression testing, and deployment automation for reliable, shippable AI solutions. Use this when requiring the creation of structured, enterprise-grade agent code.

Agent C++ Testing

affaan-m/everything-claude-code

Guides an agent through C++17/20 testing with CMake, CTest, and GoogleTest/Mock, covering new or flaky tests, CI gates, coverage builds, and sanitizer diagnostics to keep regression safety in place.

Performance Regression Detector

detecting-performance-regressions

jeremylongshore/claude-code-plugins-plus-skills

Automatically analyzes CI/CD performance metrics, compares them against baselines and thresholds, and flags statistical regressions so teams catch response-time or throughput degradations before deployment.

Differential Security Review

differential-review

trailofbits/skills

Performs security-focused differential review of PRs, commits, and diffs, adapting depth to repo size, tracing git history, estimating blast radius, checking coverage, and producing detailed markdown reports to stop regressions.

End-to-End Testing Patterns Guide

e2e-testing-patterns

sickn33/antigravity-awesome-skills

This skill provides comprehensive guidance on building reliable, fast, and maintainable end-to-end (E2E) test suites. It ensures code quality by validating critical user journeys and catching regressions in complex workflows before deployment. Essential for setting up robust CI/CD pipelines, cross-browser compatibility, and establishing rigorous QA standards.

Claude Eval Harness

affaan-m/everything-claude-code

Formal evaluation framework for Claude Code sessions that applies evaluation-driven development to define capability and regression checks, run deterministic and model graders, and track pass@k/pass^k reliability metrics before shipping.

Verify Fix Commits Against Audit Findings

sickn33/antigravity-awesome-skills

This skill provides a structured methodology for reviewing code commits intended to fix security vulnerabilities or address audit findings. It ensures that the original root cause is properly resolved, verifies the fix hasn't introduced new bugs or vulnerabilities (regressions), and validates the overall completeness of the remediation effort. Ideal for security-critical code reviews.

Git Advanced Workflows

rohitg00/awesome-claude-code-toolkit

Documents advanced Git workflows—worktrees, bisect, interactive rebase, hooks, recovery tactics, aliases, and anti-patterns—so developers can manage parallel branches, pinpoint regressions, and enforce commit hygiene in complex repos.

Automating Video Generation in CI/CD

klingai-ci-integration

jeremylongshore/claude-code-plugins-plus-skills

This tool allows developers to integrate advanced AI video generation (using Kling AI) directly into continuous integration and continuous deployment pipelines (e.g., GitHub Actions, GitLab CI). Automate the creation of marketing videos, product demos, or regression video testing whenever code changes or a release occurs, ensuring consistent and timely video asset updates.

Langfuse CI/CD for LLM Quality Testing

langfuse-ci-integration

jeremylongshore/claude-code-plugins-plus-skills

Integrate Langfuse into CI/CD pipelines (e.g., GitHub Actions) to automate the testing and validation of Large Language Models (LLMs). This guide covers prompt regression testing, tracing validation, and setting up experiment-driven quality gates, ensuring reliable and high-performing AI applications throughout the development lifecycle.

LangSmith LLM Observability

langsmith-observability

Orchestra-Research/AI-Research-SKILLs

LangSmith centralizes tracing, evaluation, and monitoring for LLM applications, letting teams debug prompts, run regression tests, collect latency/cost metrics, and integrate with OpenAI, Anthropic, LangChain, or LlamaIndex to keep production systems observable.

LLM Evaluation and Performance Testing

sickn33/antigravity-awesome-skills

Master comprehensive strategies for evaluating Large Language Model (LLM) applications. This skill covers automated metrics (BLEU, ROUGE, BERTScore), structured human evaluation dimensions, and advanced LLM-as-Judge techniques. Use it to systematically measure model performance, compare prompts, detect regressions, and build confidence in production systems.

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