querying-aws-s3
This skill queries S3 object metadata, tracks bucket activity, and analyzes storage metrics using S3 Metadata system tables (journal, inventory, annotation) and Storage Lens tables through Athena SQL. Use it to count objects, find recent uploads or deletions, identify who accessed a prefix, break down storage classes, search by tag, and audit bucket changes—all faster than raw S3 APIs at scale.
Querying AWS S3 lets you analyze object metadata and bucket activity via Athena system tables.
AI-generated summary based on this skill's SKILL.md
Decision gist · record as of 2026-07-27
Querying AWS S3 lets you analyze object metadata and bucket activity via Athena system tables. This skill queries S3 object metadata, tracks bucket activity, and analyzes storage metrics using S3 Metadata system tables (journal, inventory, annotation) and Storage Lens tables through Athena SQL. Use it to count objects, find recent uploads or deletions, identify who accessed a prefix, break down storage classes, search by tag, and audit bucket changes—all faster than raw S3 APIs at scale.
Use it when
- querying-aws-s3 uses the S3 Metadata journal table to track bucket changes and deletions.
- querying-aws-s3 leverages S3 Metadata annotation tables and tag-based filtering in Athena queries.
Verify before relying
Read SKILL.md below before installing (1 file). Open directory: indexed for reading, not audited.
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Install
aws/agent-toolkit-for-aws/querying-aws-s3 · repository language: Python
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Frequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
How can I query S3 bucket object metadata with Athena?
querying-aws-s3 enables you to query S3 object metadata using S3 Metadata system tables (journal, inventory, and annotation tables) through Athena SQL. These tables provide structured access to object properties, timestamps, and storage details without needing to scan raw S3 APIs, making large-scale queries much faster and more efficient.
How do I track S3 bucket deletions and changes?
querying-aws-s3 uses the S3 Metadata journal table to track bucket changes and deletions. The journal captures object lifecycle events, allowing you to audit who deleted what, when changes occurred, and build a complete change history. Combined with Storage Lens tables, you can correlate deletions with requester identity and access patterns.
What does querying-aws-s3 use to find S3 objects by tag?
querying-aws-s3 leverages S3 Metadata annotation tables and tag-based filtering in Athena queries. You can search and filter objects by their tags or annotations directly through SQL, enabling fast discovery of objects matching your labeling scheme without iterating through the entire bucket.
Can querying-aws-s3 help analyze S3 storage metrics and trends?
Yes. querying-aws-s3 integrates with S3 Storage Lens tables to analyze storage metrics, object distribution, and storage class breakdowns. You can query trends over time, identify cost optimization opportunities, and understand how your data is distributed across storage classes—all through standard Athena SQL.
How do I enable S3 metadata tracking for querying-aws-s3?
querying-aws-s3 requires enabling S3 Metadata configuration on your bucket. This involves setting up the journal, inventory, and annotation tables that feed Athena. Once configured, these system tables automatically populate with object metadata and activity, ready for SQL queries through Athena.
What can querying-aws-s3 tell me about who accessed my S3 bucket?
querying-aws-s3 combines S3 Metadata tables with Storage Lens data to track requester identity, IP addresses, and access patterns. You can identify who uploaded files, accessed specific prefixes, or triggered deletions—providing audit trails and accountability for bucket activity at scale.
SKILL.md
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Query AWS S3 System Tables
Overview
Works best with the AWS MCP server for sandboxed execution and audit logging. All commands below use the AWS CLI and work in any environment with configured
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skills/specialized-skills/system-table-skills/querying-aws-s3/SKILL.md
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