GitHub
networking
Retrofit, OkHttp, REST APIs, JSON serialization, network security.
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Lifestyle AI Skills
Discover Lifestyle skills, compare source repositories and read the instructions for each workflow.
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GitHub
production
Unit testing, performance optimization, security implementation, Play Store deployment.
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GitHub
architecture
MVVM pattern, Clean Architecture, Repository pattern, dependency injection, SOLID principles.
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GitHub
data
Room ORM, SQLite, SharedPreferences, DataStore, encryption.
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GitHub
platform
Android core components lifecycle, Activities, Fragments, Services, Intent system.
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GitHub
fundamentals
Master Kotlin syntax, OOP principles, SOLID practices, functional programming, and data structures.
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GitHub
ui
XML layouts, ConstraintLayout, Jetpack Compose, Material Design 3.
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GitHub
vulnerability-discovery
Systematic vulnerability finding, threat modeling, and attack surface analysis for AI/LLM security assessments
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GitHub
defense-implementation
Implement mitigations, create input filters, design output guards, and build defensive prompting for LLM security
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GitHub
responsible-disclosure
Ethical vulnerability reporting, coordinated disclosure, and bug bounty participation for AI systems
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GitHub
red-team-frameworks
Tools and frameworks for AI red teaming including PyRIT, garak, Counterfit, and custom attack automation
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GitHub
adversarial-examples
Generate adversarial inputs, edge cases, and boundary test payloads for stress-testing LLM robustness
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GitHub
safety-filter-bypass
Techniques to test and bypass AI safety filters, content moderation systems, and guardrails for security assessment
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GitHub
rag-exploitation
Attack techniques for Retrieval-Augmented Generation systems including knowledge base poisoning
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GitHub
infrastructure-security
Securing AI/ML infrastructure including model storage, API endpoints, and compute resources
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GitHub
data-poisoning
Test AI training pipelines for data poisoning vulnerabilities and backdoor injection
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GitHub
continuous-monitoring
Real-time monitoring and detection of adversarial attacks and model drift in production
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GitHub
model-inversion
Privacy attacks to extract training data and sensitive information from AI models
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GitHub
input-output-guardrails
Implementing safety filters, content moderation, and guardrails for AI system inputs and outputs
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GitHub
testing-methodologies
Structured approaches for AI security testing including threat modeling, penetration testing, and red team operations
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GitHub
secure-deployment
Security best practices for deploying AI/ML models to production environments
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GitHub
prompt-hacking
Advanced prompt manipulation including direct attacks, indirect injection, and multi-turn exploitation
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GitHub
llm-jailbreaking
Advanced LLM jailbreaking techniques, safety mechanism bypass strategies, and constraint circumvention methods
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GitHub
adversarial-training
Defensive techniques using adversarial examples to improve model robustness and security
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The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.