GitHub
dependency-mapping
Comprehensive guide to identifying, visualizing, and managing dependencies in complex projects to reduce risks and enable coordination
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
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GitHub
saga-pattern
Implementing distributed transactions across microservices using the Saga pattern with choreography and orchestration approaches.
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connection-pooling
Implementing and optimizing database connection pools for high-performance applications.
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environment-matrix-devstgprod
Environment standards for development, staging, and production: parity principles, data and access policies, configuration boundaries, and deployment controls
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tenant-aware-ops
Operational patterns for multi-tenant systems: tenant-safe debugging and monitoring, per-tenant maintenance and migrations, fairness controls, and compliance-aware access
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llm-judge-patterns
Comprehensive guide to using LLMs as judges for automated evaluation including prompt patterns, calibration, bias reduction, and multi-judge ensembles
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architecture-review
Structured process for reviewing and validating architectural decisions before implementation to catch issues early.
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schema-management
Strategies for managing data structures, versioning, and evolutions across databases and event streams.
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repo-map-ssot
Single source of truth for repository structure and entry points, including key directories, domain boundaries, shared code, integrations, and build/deploy references
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lineage-and-provenance
See the main Data Lineage skill for comprehensive coverage of data lineage tracking and provenance.
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db-migration-generator
Generator สำหรับสร้าง database migration files พร้อม up/down scripts, validation และ rollback safety
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ground-truth-management
Comprehensive guide to creating, managing, and maintaining ground truth datasets for AI evaluation including annotation, quality control, and versioning
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bulkhead-patterns
Resource isolation strategies to prevent cascading failures and limit blast radius in distributed systems
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event-sourcing
Implementing event sourcing pattern for maintaining application state as a sequence of immutable events.
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data-quality-checks-and-validation
Implementing comprehensive data quality checks across the data pipeline to ensure accuracy, completeness, and reliability.
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retry-timeout-backoff-strategies
Comprehensive guide to implementing robust retry logic, timeout handling, and backoff algorithms for resilient distributed systems
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ai-risk-assessment
Identifying, assessing, and mitigating risks in AI systems including bias, safety, privacy, security, and ethical concerns.
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agent-self-correction
Protocols for the agent to autonomously detect failures, analyze root causes, and attempt recovery strategies before requesting user intervention.
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endpoint-generator
Generator สำหรับสร้าง API endpoint พร้อม controller, service, repository, validation, tests และ documentation โดยอัตโนมัติ
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security-baseline-controls
Minimum security controls every service must implement: authentication/authorization, input validation, secrets management, security headers, dependency security, audit logging, and operational guardrails
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prompt-library-minimal
คลัง prompts แบบ minimal ที่ใช้ token น้อยที่สุดแต่ได้ผลลัพธ์ดี พร้อม templates และ best practices
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proposal-sow-delivery-management
Comprehensive guide to professional services delivery including proposal writing, SOW creation, pricing strategies, scope management, and client delivery
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ai-model-registry
Centralized management of machine learning models throughout their lifecycle, including versioning, metadata, and production deployment.
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tech-stack-selection
Systematic approach to choosing technologies, frameworks, and tools based on requirements, team capabilities, and long-term sustainability.
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.