Total 31,333 skills
Showing 12 of 31333 skills
Perl testing patterns using Test2::V0, Test::More, prove runner, mocking, coverage with Devel::Cover, and TDD methodology.
Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns.
Kotlin Coroutines and Flow patterns for Android and KMP — structured concurrency, Flow operators, StateFlow, error handling, and testing.
Compose Multiplatform and Jetpack Compose patterns for KMP projects — state management, navigation, theming, performance, and platform-specific UI.
Comprehensive Perl security covering taint mode, input validation, safe process execution, DBI parameterized queries, web security (XSS/SQLi/CSRF), and perlcritic security policies.
Turn a one-line objective into a step-by-step construction plan for multi-session, multi-agent engineering projects. Each step has a self-contained context brief so a fresh agent can execute it cold. Includes adversarial review gate, dependency graph, parallel step detection, anti-pattern catalog, and plan mutation protocol. TRIGGER when: user requests a plan, blueprint, or roadmap for a complex multi-PR task, or describes work that needs multiple sessions. DO NOT TRIGGER when: task is completable in a single PR or fewer than 3 tool calls, or user says "just do it".
Amazon Movers & Shakers data acquisition tool. This skill is used when users need to find hot products with recently soaring sales on Amazon, discover bestseller trends, and obtain the soaring list product list. It supports filtering by category, outputs data such as basic product information, price, ranking trend, etc., providing original market data for Temu product selection.
Set up and optimize context management for any project. Use this skill when the user says "set up context management", "optimize my CLAUDE.md", "context setup", "configure compact instructions", "set up rules", or when starting a new project and wanting best practices for long sessions, memory, compaction, and subagent delegation. Also trigger when the user mentions problems with context loss, compaction losing info, or sessions getting slow.
Engineering operating model for teams where AI agents generate a large share of implementation output.
Operate long-lived agent workloads with observability, security boundaries, and lifecycle management.
Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates.
Patterns for continuous autonomous agent loops with quality gates, evals, and recovery controls.