GitHub
dependency-mapping
Comprehensive guide to identifying, visualizing, and managing dependencies in complex projects to reduce risks and enable coordination
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AmnadTaowsoam AI Skills
GitHub ↗浏览 AmnadTaowsoam 维护的已发布 AI 技能，比较用途与说明，并通过来源仓库了解安装方式。
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已收录技能
417收录技能的仓库 Stars 合计
223仓库 Forks
0平均每个技能的仓库 Stars
1同一仓库中的多个技能可能共享 Stars，合计包含重复仓库数据。
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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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GitHub
connection-pooling
Implementing and optimizing database connection pools for high-performance applications.
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GitHub
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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GitHub
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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GitHub
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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GitHub
architecture-review
Structured process for reviewing and validating architectural decisions before implementation to catch issues early.
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GitHub
schema-management
Strategies for managing data structures, versioning, and evolutions across databases and event streams.
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GitHub
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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GitHub
lineage-and-provenance
See the main Data Lineage skill for comprehensive coverage of data lineage tracking and provenance.
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GitHub
db-migration-generator
Generator สำหรับสร้าง database migration files พร้อม up/down scripts, validation และ rollback safety
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GitHub
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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GitHub
bulkhead-patterns
Resource isolation strategies to prevent cascading failures and limit blast radius in distributed systems
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GitHub
event-sourcing
Implementing event sourcing pattern for maintaining application state as a sequence of immutable events.
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GitHub
data-quality-checks-and-validation
Implementing comprehensive data quality checks across the data pipeline to ensure accuracy, completeness, and reliability.
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GitHub
retry-timeout-backoff-strategies
Comprehensive guide to implementing robust retry logic, timeout handling, and backoff algorithms for resilient distributed systems
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GitHub
ai-risk-assessment
Identifying, assessing, and mitigating risks in AI systems including bias, safety, privacy, security, and ethical concerns.
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GitHub
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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GitHub
endpoint-generator
Generator สำหรับสร้าง API endpoint พร้อม controller, service, repository, validation, tests และ documentation โดยอัตโนมัติ
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GitHub
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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GitHub
prompt-library-minimal
คลัง prompts แบบ minimal ที่ใช้ token น้อยที่สุดแต่ได้ผลลัพธ์ดี พร้อม templates และ best practices
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GitHub
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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GitHub
ai-model-registry
Centralized management of machine learning models throughout their lifecycle, including versioning, metadata, and production deployment.
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GitHub
tech-stack-selection
Systematic approach to choosing technologies, frameworks, and tools based on requirements, team capabilities, and long-term sustainability.
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默认按已收录数据中的 GitHub 仓库 Stars 排序。Stars 属于来源仓库，同一仓库的多个技能可能共享相同数值；它反映仓库关注度，不代表单个技能的安装量或质量评分。