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Hardening Docker Container Images Best Practices

performing-container-image-hardening

mukul975/Anthropic-Cybersecurity-Skills

This comprehensive guide details industry best practices for container image hardening. It covers minimizing attack surface through multi-stage builds, utilizing distroless images, enforcing non-root users, and configuring read-only root filesystems. The techniques ensure compliance with standards like the CIS Docker Benchmark, making images secure and production-ready.

Cosmos Policy Evaluation

evaluating-cosmos-policy

Orchestra-Research/AI-Research-SKILLs

Run NVIDIA Cosmos Policy evaluations on LIBERO and RoboCasa simulations, enabling headless EGL GPU rendering, inference profiling, and benchmark workflows via public eval entrypoints.

Venture Capital Model

financial-model

davepoon/buildwithclaude

Executes a structured VC valuation workflow that ingests SaaS or startup metrics, runs DCF, comparable revenue multiples, and SaaS health models via Python, and synthesizes a defensible valuation, runway, and capital-efficiency narrative for users planning fundraising or benchmarking.

GitHub Repository Audit and Optimization Tool

openclaw-github-repo-commander

sickn33/antigravity-awesome-skills

A comprehensive 7-stage workflow designed to manage, audit, and optimize GitHub repositories. This tool automates critical tasks including identifying hardcoded secrets, cleaning up junk files, performing deep documentation reviews, benchmarking against competitors, and validating the final state, transforming messy codebases into production-ready assets.

Performance Benchmarking and Regression Detection

affaan-m/everything-claude-code

A comprehensive tool for establishing performance baselines across multiple dimensions. It measures Core Web Vitals (LCP, CLS, INP), API latency (p50, p95, p99), and development build efficiency. Essential for detecting performance regressions before merging code, ensuring stability in CI/CD pipelines, and comparing alternative stack implementations.

Go Performance Benchmarking and Profiling

golang-benchmark

samber/cc-skills-golang

This skill guides the complete performance measurement workflow for Go applications. Learn how to write robust benchmarks using b.Loop(), run tests with specific flags (-benchmem, -count), generate detailed profiles (CPU, memory, trace), and statistically compare results using tools like benchstat. It ensures optimization decisions are data-driven, preventing performance regressions in CI/CD.

Go Performance Optimization Methodology

golang-performance

samber/cc-skills-golang

A comprehensive methodology guide for optimizing Go applications. It teaches engineers how to systematically identify and resolve performance bottlenecks, covering CPU, memory allocation, I/O latency, and algorithmic complexities. The core philosophy emphasizes profiling first, followed by iterative benchmarking and applying specific optimization patterns.

AI Skill Creation And Optimization

CherryHQ/cherry-studio

A comprehensive workflow for the full lifecycle management of AI skills. This tool guides users through creating skills from scratch, modifying existing ones, and rigorously benchmarking performance. It supports iterative refinement, quantitative evaluation, and optimizing skill descriptions for maximum triggering accuracy.

Golang Testing Guidelines

samber/cc-skills-golang

Comprehensive Go testing guidance that spans table-driven subtests, testify suites, mocking, unit/integration tests, benchmarks, coverage, fuzzing, goleak leak detection, snapshot checks, and GitHub Actions CI to keep production suites reliable.

Eval-Driven Development for LLM Applications

eval-driven-dev

github/awesome-copilot

This skill guides the end-to-end process of building automated evaluation pipelines for Python applications powered by Large Language Models (LLMs). It teaches how to define evaluation criteria, instrument the application, build golden datasets, and run real-world evaluations using tools like pixie, ensuring the app's logic (not just the LLM) is rigorously tested. Ideal for QA, benchmarking, and improving LLM-based services.

Adobe API Performance, Load, and Scale Testing

adobe-load-scale

jeremylongshore/claude-code-plugins-plus-skills

Comprehensive solution for conducting load testing, performance benchmarking, and auto-scaling setup for various Adobe APIs (Firefly, PDF Services, Photoshop). This guide utilizes k6 for stress testing, simulating real-world user traffic, and includes Kubernetes HPA configuration to ensure the application maintains high availability and stability under peak loads.

Claude API Load Testing and Capacity Planning

anth-load-scale

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive guide and set of scripts for performing load testing and capacity planning for Anthropic's Claude API. This resource helps developers benchmark performance, anticipate traffic spikes, manage rate limits (RPM/ITPM/OTPM), and implement robust horizontal scaling strategies for mission-critical AI services using Python.

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