GitHub
postmortem-analysis
Comprehensive guide to conducting blameless postmortems and learning from incidents to improve system reliability
AmnadTaowsoam AI Skills
GitHub ↗Explorez les skills IA publiés de AmnadTaowsoam et consultez leurs instructions source.
Statistiques de l’auteur
Skills indexés
417Étoiles des dépôts des skills indexés
223Forks du dépôt
0Moyenne des étoiles par skill indexé
1Les étoiles d’un dépôt peuvent se répéter entre ses skills.
Catégories populaires
Indexation récente
Explorer
GitHub
retention-and-deletion
Comprehensive guide to data retention policies, deletion workflows, and implementing GDPR/CCPA right to erasure across distributed systems.
GitHub
lakehouse-patterns
Comprehensive guide to data lakehouse architecture combining data lake flexibility with data warehouse performance using Delta Lake, Iceberg, and Hudi
GitHub
data-validation-rules
Implementing comprehensive validation rules across database, application, and pipeline layers to ensure data integrity.
GitHub
security-questionnaires
Comprehensive guide to handling enterprise security questionnaires, building standard response libraries, and streamlining the vendor assessment process
GitHub
graphql-best-practices
Comprehensive guide to GraphQL API design, schema design, resolvers, performance optimization, and security.
GitHub
state-machines-with-xstate
Implementing robust state management using finite state machines and statecharts with XState.
GitHub
cost-modeling
See the main Cloud Cost Models skill for comprehensive coverage of cost modeling and pricing strategies.
GitHub
release-checklist-gate
Checklist gate สำหรับ production release ครอบคลุม testing, documentation, rollback plan, และ stakeholder sign-off
GitHub
load-balancing-strategies
Comprehensive guide to load balancing algorithms, health checks, and high-availability patterns.
GitHub
migration-planning
Systematic planning and execution of system migrations including database migrations, platform changes, and technology stack transitions.
GitHub
failure-modes-analysis
Comprehensive guide to identifying, analyzing, and documenting failure modes in distributed systems
GitHub
multi-tenancy-advanced-patterns
Advanced patterns for building scalable multi-tenant SaaS applications.
GitHub
rollout-and-kill-switch
Comprehensive guide to safe agent deployment strategies including canary releases, feature flags, kill switches, and automated rollback mechanisms
GitHub
users
Data contracts สำหรับกำหนด schema, quality expectations และ SLAs ระหว่าง data producers และ consumers
GitHub
development-environment-setup
Standardized development environment configuration including tools, IDE settings, and automation for consistent developer experience.
GitHub
event-style-guide
Event-driven design conventions: event envelope, naming, versioning, schema evolution rules, idempotency, ordering/partitioning, retry and dead-letter handling
GitHub
anti-bloat-checklist
Checklist สำหรับตรวจสอบและกำจัด bloat ใน context, prompt, และ response เพื่อลด token cost และเพิ่ม signal-to-noise ratio
GitHub
error-boundaries-in-react
Implementing error boundaries for graceful error handling and improved user experience in React applications.
GitHub
config-environment-conventions
Conventions for service configuration and environment variables: naming, precedence, validation at startup, secrets handling, environment detection, and documentation patterns
GitHub
model-risk-management
Identifying, assessing, and mitigating risks associated with the use of machine learning models in business processes.
GitHub
rag-evaluation
Comprehensive guide to evaluating Retrieval-Augmented Generation systems including retrieval metrics, generation quality, faithfulness, and end-to-end evaluation frameworks
GitHub
model-bias-and-fairness
Identifying, measuring, and mitigating algorithmic bias to ensure equitable outcomes in AI systems.
GitHub
storage-and-egress-optimization
Strategies for reducing cloud storage costs and minimizing expensive data transfer (egress) fees.
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.