severity-levels
Comprehensive guide to incident severity classification and response requirements
Browse published AI skills maintained by AmnadTaowsoam. Compare skill descriptions and follow each source repository for setup instructions.
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223Repository forks
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Comprehensive guide to incident severity classification and response requirements
Techniques and tools for understanding how machine learning models make decisions and explaining those decisions to stakeholders.
The master skill for analyzing, designing, and standardizing the creation of new AI skills (Skill 72).
Organization-wide REST/GraphQL API conventions: URL and naming rules, versioning, pagination/filtering, status codes, authentication headers, and compatibility guidance
Policy สำหรับ automated security scanning ใน CI/CD pipeline รวม dependency audit, SAST, และ secret detection
See the main Incident Triage skill for comprehensive triage workflows and procedures.
Comprehensive guide to consumer-driven contract testing with Pact, bi-directional contracts, and integration with CI/CD pipelines
Comprehensive guide to offline and online evaluation strategies including A/B testing, shadow mode, canary deployments, and bridging offline-online metrics
Standardized local development environment setup ensuring consistency across team members and reducing "works on my machine" issues.
Comprehensive guide to Agile Scrum methodology including roles, ceremonies, artifacts, sprint planning, and best practices for iterative software development
See the main Model Explainability skill for comprehensive coverage of confidence scoring and calibration.
Protecting personal and sensitive data throughout the machine learning lifecycle, from training to inference.
Implementing fast feedback loops with hot reload, live reload, and rapid iteration to maximize developer productivity.
Architecture and implementation strategies for consumption-based billing models in SaaS products.
SSOT blueprint for creating consistent services: folder structure, required endpoints, dependency conventions, interface contracts, docs/testing/security/observability requirements
Design patterns for building autonomous AI agents that can reason, plan, and execute tasks.
Systematic approach to testing system resilience through controlled failure injection and experimentation
Comprehensive guide to creating operational runbooks including incident response, deployment procedures, troubleshooting guides, and on-call playbooks
Standardize and package logs, metrics, and traces into services by default using a lightweight stack and shared instrumentation with dashboards and alerting
Comprehensive guide to preventing PII and secrets from appearing in logs through redaction strategies, safe logging practices, and automated filtering.
Production Kubernetes platform patterns covering cluster architecture, security, GitOps, observability, autoscaling, and operational guardrails
Comprehensive guide to delivery governance including quality gates, standards, compliance, and balancing governance with agility
Implementing Command Query Responsibility Segregation for separating read and write operations in distributed systems.
See the main Data Validation Rules skill for comprehensive coverage of data quality rule implementation.
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