Explore our collection of Agent Skills to enhance your AI workflow.
Ensures code accuracy and avoids deprecated implementations by performing targeted real-time web research on APIs and libraries.
Architects high-conversion newsletter referral programs and reward structures optimized for organic growth.
Implements a prompt-native engineering philosophy where AI agents achieve outcomes using primitive tools and natural language definitions rather than hard-coded workflows.
Facilitates structured design sessions to transform vague ideas into concrete, validated architectural plans before coding begins.
Standardizes the development of high-quality, reusable Claude Code skills through structured patterns and token-efficient documentation principles.
Provides expert frameworks and best practices for developer advocacy, community management, and technical content creation.
Optimizes context window usage by prioritizing essential information and reducing token consumption during autonomous coding tasks.
Applies structural code changes using AST-aware edits to minimize syntax errors and maintain code integrity.
Builds high-performance read models and materialized views from event streams for event-sourced systems.
Automates application versioning, build number increments, and deployment workflows for web and mobile platforms.
Guides community managers through conflict resolution, rule enforcement, and standardized moderation frameworks for platforms like Discord.
Validates implementation plans against research findings and technical requirements to catch errors before code execution.
Orchestrates multiple specialized AI agents to execute complex, multi-component engineering tasks through intelligent decomposition and mission control.
Improves code quality and team collaboration by applying structured review frameworks, checklists, and constructive feedback patterns.
Automates scientific hypothesis generation and testing on tabular datasets by combining empirical data patterns with literature insights.
Streamlines the creation, structuring, and distribution of professional Python packages using modern PEP standards and pyproject.toml configuration.
Integrates real-time account performance metrics and drawdown penalties into Reinforcement Learning models to optimize trading risk management.
Streamlines the feature development lifecycle by converting raw ideas into validated, dependency-mapped Linear stories.
Manipulates PDF documents programmatically to extract data, merge files, fill forms, and generate automated reports.
Enforces unique identifier usage across process and asynchronous boundaries to eliminate race conditions and ensure data integrity.
Implements robust saga patterns for managing distributed transactions and complex multi-step workflows across microservices.
Extracts clean, readable text from URLs by stripping away ads, navigation menus, and web clutter.
Guides users through statistical test selection, assumption verification, and APA-formatted research reporting.
Manages the full lifecycle of autonomous coding sessions by automating initialization, state resumption, and iterative execution flows.
Standardizes code review processes to catch bugs, improve architecture, and foster constructive team collaboration.
Simulates and analyzes open and closed quantum systems using the Quantum Toolbox in Python.
Facilitates structured 5 Whys analysis to identify systemic root causes of software incidents through neutral guidance and real-time visualization.
Generates visually engaging, research-backed slide decks for conferences, seminars, and academic presentations using AI.
Provides a comprehensive command-line interface for managing autonomous coding tasks, spec creation, and QA validation within the Auto-Claude framework.
Implements comprehensive meta-analysis workflows in R, including effect size calculation, heterogeneity assessment, and publication bias detection.
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