github-copilot

Systematically scan Substrate runtime modules (pallets) for platform-specific security vulnerabilities that can cause node crashes, DoS attacks, or unauthorized access. This skill encodes 7 critical vulnerability patterns unique to Substrate/FRAME-based chains.

Wycheproof is an extensive collection of test vectors designed to verify the correctness of cryptographic implementations and test against known attacks. Originally developed by Google, it is now a community-managed project where contributors can add test vectors for specific cryptographic constructions.

LibAFL is a modular fuzzing library that implements features from AFL-based fuzzers like AFL++. Unlike traditional fuzzers, LibAFL provides all functionality in a modular and customizable way as a Rust library. It can be used as a drop-in replacement for libFuzzer or as a library to build custom fuzzers from scratch.

Comprehensive schema validation guide for Zod in TypeScript applications. Contains 43 rules across 8 categories, prioritized by impact to guide automated refactoring and code generation.

Generate a complete favicon package from a logo, initials, or brand colours. Produces all required formats and HTML integration code.

Authentication module for Nuxt 4+ built on Better Auth . Provides composables, server utilities, and route protection.

Connect Claude to 1000+ apps. Actually send emails, create issues, post messages - not just generate text about it.

Process documents with the Nutrient DWS Processor API . Convert formats, extract text and tables, OCR scanned documents, redact PII, add watermarks, digitally sign, and fill PDF forms.

AFL++ is a fork of the original AFL fuzzer that offers better fuzzing performance and more advanced features while maintaining stability. A major benefit over libFuzzer is that AFL++ has stable support for running fuzzing campaigns on multiple cores, making it ideal for large-scale fuzzing efforts.

Batteries-included agent component from ui.inference.sh .

Ruzzy is a coverage-guided fuzzer for Ruby built on libFuzzer. It enables fuzzing both pure Ruby code and Ruby C extensions with sanitizer support for detecting memory corruption and undefined behavior.

Terraform Stacks simplify infrastructure provisioning and management at scale by providing a configuration layer above traditional Terraform modules. Stacks enable declarative orchestration of multiple components across environments, regions, and cloud accounts.

Systematically scan TON blockchain smart contracts written in FunC for platform-specific security vulnerabilities related to boolean logic, Jetton token handling, and gas management. This skill encodes 3 critical vulnerability patterns unique to TON's architecture.

Source details, install context, and public review data are available on the full page.

After Design: Confirm every component has a redundancy strategy and no single points of failure exist in the topology.

You diagnose requirements-level problems in software projects. Your role is to help solo developers distinguish stated wants from underlying problems, discover real constraints, and avoid premature solution thinking.

Market research reports are comprehensive strategic documents that analyze industries, markets, and competitive landscapes to inform business decisions, investment strategies, and strategic planning. This skill generates professional-grade reports of 50+ pages with extensive visual content, modeled after deliverables...

Port existing Semgrep rules to new target languages with proper applicability analysis and test-driven validation.

Source details, install context, and public review data are available on the full page.

This skill transforms markdown, HTML, or text content into beautifully styled HTML pages with AI-generated cover images, then captures them as sequential screenshots at 3:4 ratio for Xiaohongshu posting.

Provides project management expertise specializing in planning, execution, monitoring, and closure of projects. Masters traditional and agile methodologies to deliver projects on time, within budget, and to quality standards.

Apply a consistent structure and dependency pattern to SwiftUI views, with a focus on ordering, Model-View (MV) patterns, careful view model handling, and correct Observation usage.

Create self-contained, reusable Convex components with proper isolation, exports, and dependency management for sharing across projects.

Help the user generate and evaluate startup ideas using frameworks and insights from 2 product leaders.