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This skill provides AWS cost optimization, monitoring, and operational best practices with integrated MCP servers for billing analysis, cost estimation, observability, and security assessment.
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Based on SOC occupation classification
| name | aws-cost-operations |
| description | This skill provides AWS cost optimization, monitoring, and operational best practices with integrated MCP servers for billing analysis, cost estimation, observability, and security assessment. |
| skills | ["aws-mcp-setup"] |
| allowed-tools | ["mcp__pricing__*","mcp__costexp__*","mcp__cw__*","mcp__aws-mcp__*","mcp__awsdocs__*","Bash(aws ce *)","Bash(aws cloudwatch *)","Bash(aws logs *)","Bash(aws budgets *)","Bash(aws cloudtrail *)","Bash(aws sts get-caller-identity)"] |
| hooks | {"PreToolUse":[{"matcher":"Bash(aws ce *)","command":"aws sts get-caller-identity --query Account --output text","once":true}]} |
This skill provides comprehensive guidance for AWS cost optimization, monitoring, observability, and operational excellence with integrated MCP servers.
CRITICAL: This skill requires AWS MCP tools for accurate, up-to-date AWS information.
Always verify using AWS MCP tools (if available):
mcp__aws-mcp__aws___search_documentation or mcp__*awsdocs*__aws___search_documentation - Search AWS docsmcp__aws-mcp__aws___read_documentation or mcp__*awsdocs*__aws___read_documentation - Read specific pagesmcp__aws-mcp__aws___get_regional_availability - Check service availabilityIf AWS MCP tools are unavailable:
aws-mcp-setup skill (auto-loaded as dependency)This skill includes 8 MCP servers automatically configured with the plugin:
Purpose: Real-time billing and cost management
Purpose: Pre-deployment cost estimation and optimization
Purpose: Detailed cost analysis and reporting
Purpose: Metrics, alarms, and logs analysis
Purpose: Application monitoring and performance insights
Purpose: Prometheus-compatible monitoring
Purpose: AWS API activity and audit analysis
Purpose: Security assessment against Well-Architected Framework
Use this skill when:
Always estimate costs before deploying:
Example workflow:
"Estimate the monthly cost of running a Lambda function with
1 million invocations, 512MB memory, 3-second duration in us-east-1"
Regular cost reviews:
Cost optimization strategies:
Track spending against budgets:
Implement comprehensive monitoring:
Example alarm scenarios:
Monitor application health:
For containerized workloads:
Audit AWS activity:
Common audit scenarios:
Regular security reviews:
Security assessment areas:
For detailed operational patterns and best practices, refer to the comprehensive reference:
File: references/operations-patterns.md
This reference includes:
File: references/cloudwatch-alarms.md
Common alarm configurations for:
AWS Cloud Development Kit (CDK) expert for building cloud infrastructure with TypeScript/Python. Use when creating CDK stacks, defining CDK constructs, implementing infrastructure as code, or when the user mentions CDK, CloudFormation, IaC, cdk synth, cdk deploy, or wants to define AWS infrastructure programmatically. Covers CDK app structure, construct patterns, stack composition, and deployment workflows.
Provides comprehensive GitHub operations using gh CLI and GitHub API. Activates when working with pull requests, issues, repositories, workflows, or GitHub API operations including creating/viewing/merging PRs, managing issues, querying API endpoints, and handling GitHub workflows in enterprise or public GitHub environments.
Use this skill for general PostgreSQL table design. **Trigger when user asks to:** - Design PostgreSQL tables, schemas, or data models when creating new tables and when modifying existing ones. - Choose data types, constraints, or indexes for PostgreSQL - Create user tables, order tables, reference tables, or JSONB schemas - Understand PostgreSQL best practices for normalization, constraints, or indexing - Design update-heavy, upsert-heavy, or OLTP-style tables **Keywords:** PostgreSQL schema, table design, data types, PRIMARY KEY, FOREIGN KEY, indexes, B-tree, GIN, JSONB, constraints, normalization, identity columns, partitioning, row-level security Comprehensive reference covering data types, indexing strategies, constraints, JSONB patterns, partitioning, and PostgreSQL-specific best practices.
Use this skill to analyze an existing PostgreSQL database and identify which tables should be converted to Timescale/TimescaleDB hypertables. **Trigger when user asks to:** - Analyze database tables for hypertable conversion potential - Identify time-series or event tables in an existing schema - Evaluate if a table would benefit from Timescale/TimescaleDB - Audit PostgreSQL tables for migration to Timescale/TimescaleDB/TigerData - Score or rank tables for hypertable candidacy **Keywords:** hypertable candidate, table analysis, migration assessment, Timescale, TimescaleDB, time-series detection, insert-heavy tables, event logs, audit tables Provides SQL queries to analyze table statistics, index patterns, and query patterns. Includes scoring criteria (8+ points = good candidate) and pattern recognition for IoT, events, transactions, and sequential data.
Use this skill to migrate identified PostgreSQL tables to Timescale/TimescaleDB hypertables with optimal configuration and validation. **Trigger when user asks to:** - Migrate or convert PostgreSQL tables to hypertables - Execute hypertable migration with minimal downtime - Plan blue-green migration for large tables - Validate hypertable migration success - Configure compression after migration **Prerequisites:** Tables already identified as candidates (use find-hypertable-candidates first if needed) **Keywords:** migrate to hypertable, convert table, Timescale, TimescaleDB, blue-green migration, in-place conversion, create_hypertable, migration validation, compression setup Step-by-step migration planning including: partition column selection, chunk interval calculation, PK/constraint handling, migration execution (in-place vs blue-green), and performance validation queries.
Use this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. **Trigger when user asks to:** - Store or search vector embeddings in PostgreSQL - Set up semantic search, similarity search, or nearest neighbor search - Create HNSW or IVFFlat indexes for vectors - Implement RAG (Retrieval Augmented Generation) with PostgreSQL - Optimize pgvector performance, recall, or memory usage - Use binary quantization for large vector datasets **Keywords:** pgvector, embeddings, semantic search, vector similarity, HNSW, IVFFlat, halfvec, cosine distance, nearest neighbor, RAG, LLM, AI search Covers: halfvec storage, HNSW index configuration (m, ef_construction, ef_search), quantization strategies, filtered search, bulk loading, and performance tuning.