Modeler
sidequery/sidemantic
Build, validate, and manage semantic models using Sidemantic.
Selects, investigates, and compares AWS object, file, and block storage services, and answers cost, performance, configuration, security, and troubleshooting questions about storage services.
$ npx skills add aws/agent-toolkit-for-aws --skill aws-storage -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install aws/agent-toolkit-for-aws aws-storage --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/core-skills/aws-storage .claude/skills/aws-storage && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "aws-storage" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/skills/core-skills/aws-storage into .claude/skills/aws-storage/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aws-storage", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/aws/agent-toolkit-for-aws/tree/main/skills/core-skills/aws-storageType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add aws/agent-toolkit-for-aws --skill aws-storage -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install aws/agent-toolkit-for-aws aws-storage --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/core-skills/aws-storage .agents/skills/aws-storage && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "aws-storage" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/skills/core-skills/aws-storage into .agents/skills/aws-storage/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aws-storage", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add aws/agent-toolkit-for-aws --skill aws-storage -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install aws/agent-toolkit-for-aws aws-storage --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/core-skills/aws-storage .cursor/skills/aws-storage && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "aws-storage" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/skills/core-skills/aws-storage into .cursor/skills/aws-storage/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aws-storage", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/aws/agent-toolkit-for-aws.git --path skills/core-skills/aws-storage--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add aws/agent-toolkit-for-aws --skill aws-storage -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install aws/agent-toolkit-for-aws aws-storage --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/core-skills/aws-storage .gemini/skills/aws-storage && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "aws-storage" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/skills/core-skills/aws-storage into .gemini/skills/aws-storage/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aws-storage", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install aws/agent-toolkit-for-aws aws-storageInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add aws/agent-toolkit-for-aws --skill aws-storage -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/core-skills/aws-storage .github/skills/aws-storage && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "aws-storage" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/skills/core-skills/aws-storage into .github/skills/aws-storage/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aws-storage", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add aws/agent-toolkit-for-aws --skill aws-storage -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install aws/agent-toolkit-for-aws aws-storage --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/core-skills/aws-storage .opencode/skills/aws-storage && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "aws-storage" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/skills/core-skills/aws-storage into .opencode/skills/aws-storage/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aws-storage", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
aws-storageSelects, investigates, and compares AWS object, file, and block storage services, and answers cost, performance, configuration, security, and troubleshooting questions about storage services.
AWS Storage is an agent skill from aws/agent-toolkit-for-aws, published by the product's own GitHub organization. Selects, investigates, and compares AWS object, file, and block storage services, and answers cost, performance, configuration, security, and troubleshooting questions about storage services. Applies when a user asks where to store or archive data based on their usage patterns; which storage service to choose or how two compare; how to migrate data from on-premises or between AWS services; how to protect, replicate, or recover data; how to optimize storage costs; where to deploy shared NFS, SMB, or POSIX file…
Its SKILL.md is about 5.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 13 other files, including reference files (for example `references/data-movement-and-protection-knowledge.md`, `references/ebs-knowledge.md` and `references/efs-knowledge.md`).
It sits in Databases, covering Data pipelines and ETL, Event-driven systems and SQL. It works with Amazon Web Services, Amazon DynamoDB, SQL and Apache Kafka. The repository describes itself as: Official, AWS-supported MCP servers, skills, and plugins to help AI agents build on AWS. The licence is Apache-2.0.
4 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit bd49cc8. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Links to these hosts (documentation or services it may open):
docs.aws.amazon.comaws.amazon.comFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
AWS Storage loads about 5.8k tokens when it runs, and up to ~24k if it reads all its reference files. Until then it costs about 259 tokens; SKILL.md has 2,746 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from aws/agent-toolkit-for-aws at commit bd49cc8, republished under its Apache-2.0 licence (© aws). 2,746 words, ~5,769 tokens.
.claude/skills/aws-storage/SKILL.md (or your agent's skills folder). This skill also uses 12 other files; get the full folder from GitHub.This skill provides domain expertise for choosing among AWS storage services, selecting storage classes, optimizing cost, and routing to resources for operating storage services. It covers object storage (S3 General Purpose buckets and their storage classes, S3 Express One Zone on directory buckets, S3 Tables, S3 Vectors), file storage (Amazon EFS, S3 Files, FSx for Lustre, FSx for NetApp ONTAP, FSx for OpenZFS, and FSx for Windows File Server), block storage (EBS volume types and EC2 instance store), and the data-movement and protection services that connect them (DataSync, Storage Gateway, Transfer Family, and AWS Backup). It does not advise on databases or analytics query engines. It works with or without the AWS MCP server; when available, the AWS MCP server is recommended for verifying current specifications and pricing, and all guidance also works with the standard AWS CLI. For deep single-service tasks, route to the specialized skills listed in the Routing section below.
When this skill is triggered, classify the user's request and follow the appropriate path.
These apply to all responses regardless of path:
billing-and-cost-management skill.Determine what the user needs:
| Intent | Example Triggers | What this means |
|---|---|---|
| SELECT | "What should I use?", "Which service?", "Compare Service A vs Service B", "Help me choose", "I want to migrate X to AWS" (workload description without a named service) | User needs help choosing a storage service or approach |
| INVESTIGATE | "How do I configure Service X?", "Why is Service Y failing?", "What are the limits of Service Z?", "How do I get started with Service X?" (names a specific service and asks an operational question) | User knows what they are using and needs getting started, troubleshooting, or operational help |
You MUST follow the interaction logic in the corresponding path instructions below.
Ambiguous cases: If the user's primary ask is a recommendation, it is SELECT regardless of context. If they need help executing a known plan, it is INVESTIGATE.
The user needs help choosing. You MUST use the following decision factors to inform your recommendation, asking questions to fill gaps that would change the choice.
Decision Factors:
| # | Decision Factor | What to understand |
|---|---|---|
| 1 | Workload Context | Is this a new workload or a migration of an existing workload? If migrating, what is the source system (e.g., NetApp, ZFS, Windows File Server, Lustre, GPFS, etc.)? What application or workload will access this storage? How does it access data (API, file protocol, block device)? What OS or platform are clients running? What is the data model (structured/tabular, vector embeddings, unstructured objects, file system)? |
| 2 | Capacity and Access Patterns | How much data, how many files or objects, what are the typical sizes? Sequential or random access pattern? Read-heavy, write-heavy, or mixed? |
| 3 | Performance Requirements | Are there specific latency, throughput, or IOPS requirements? What is the expected concurrency (number of clients or compute nodes accessing simultaneously)? |
| 4 | Durability and Data Protection | What is the recovery time objective (RTO)? Recovery point objective (RPO)? Compliance retention mandates? Cross-region resilience? Immutability needs? |
| 5 | Availability | Multi-AZ or Single-AZ acceptable? Co-located with specific compute? |
Use the Storage Options table to identify candidate services, not to cut services; keyword matches against the Common Workloads column are not the only answers. You MUST retrieve the reference files for each of the candidate services using the Routing section below.
Considering these factors, recommend specific AWS storage service(s) and:
The user knows their service or approach and needs operational help.
| Question Domain | Example Triggers | What to clarify |
|---|---|---|
| Migration and Data Transfer | "How do I move my data to AWS?", "Set up DataSync", "Sync from on-premises NFS to EFS", source-to-destination questions | Source system and protocol, destination service, data volume, network path to AWS (Direct Connect, VPN, internet) |
| Data Protection and Resiliency | "Set up backup", "Cross-region replication", "What's my DR strategy?", "RTO under 1 hour", failover, immutability | Failure scenario (deletion, corruption, AZ/region loss, compliance hold), RTO and RPO targets, replication scope (same-region, cross-region, cross-account) |
| Cost and Lifecycle | "Reduce my storage bill", "Right-size my volumes", "Cost optimize", lifecycle rules, tiering decisions, storage class selection | Current service and configuration, access frequency (daily, weekly, rarely), data volume and growth trajectory |
| Performance | "My reads are slow", "Throughput bottleneck", "Need more IOPS", sizing | Observed vs. required (latency, IOPS, or throughput), access pattern (random/sequential, read/write mix), whether storage or compute/network is the suspected bottleneck |
| Security and Compliance | "Encrypt at rest", "Restrict bucket access", "Meet HIPAA", access control, compliance frameworks | Security objective (restrict access, audit access, encrypt, isolate network, meet compliance mandate), compliance framework if any |
| Configuration and Guidance | "Mount EFS on EKS", "Set up replication", "Does Service X support Y?", deployment steps, best practices | Client environment (OS, compute type, VPC/on-premises), target operation or feature |
| Troubleshooting | "Getting AccessDenied errors", "Mount is hanging", "Unexpected latency spike", "Why is my lifecycle rule not transitioning?" | Error message or symptom, what changed recently, what they have attempted |
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AWS storage services covered by this skill, grouped by storage category. For more information on storage categories, see Block, file, and object storage compared.
| Service | Key Characteristics | Common Workloads |
|---|---|---|
| S3 General Purpose | Virtually unlimited-scale object storage with multiple storage classes spanning frequent-access to low-cost archive; lifecycle rules move data to lower-cost storage classes optimized for less-frequently accessed data. Regional availability. Accessed over a REST/HTTP API from anywhere. | Data lakes and analytics, backup and archive targets, ML training data, media storage and content distribution, log and event data, static website and application assets, regulatory and compliance archives |
| S3 Express One Zone | Single-AZ directory buckets optimized for single-digit millisecond latency on frequently accessed, latency-sensitive data. | large-scale ML training and inference, Spark and EMR shuffle, ML checkpoints, scratch, and model loading, ETL intermediate data, interactive analytics on hot partitions, observability and log analytics (hot tier), Kafka tiered storage, media and video editing, high-frequency transactional access, caching for machine learning inference |
| S3 Tables | Managed Apache Iceberg tables on S3 with automatic compaction and query optimization. Regional availability. Built for structured, tabular data queried with SQL engines. | Data lake tables, structured analytics data, ETL pipeline outputs, streaming into tables for SQL analysis, migration of open table format data outside of S3 or self-managed Iceberg on S3 |
| S3 Vectors | Vector storage and similarity search on S3 with native support to cost-effectively store and query vector embeddings. Provides the same elasticity, durability and availability as S3 General Purpose buckets. Regional availability. | RAG pipelines, semantic search, recommendation systems, vector deduplication and matching, anomaly and fraud detection, AI agent memory, cost-effective storage of large vector datasets |
| S3 Metadata | Queryable object metadata in fully managed, read-only Apache Iceberg tables including system-defined details, user-defined metadata, object tags, and annotations | Business analytics, content cataloging, data governance and compliance, storage optimization, real-time inference applications, AI agents |
When naming a service, you MUST always specify the full name (FSx for Lustre, FSx for Windows File Server, FSx for NetApp ONTAP, or FSx for OpenZFS). EFS and S3 Files can be mounted by Lambda and Fargate. Verify additional services mountable from serverless compute with the latest AWS documentation. FSx for NetApp ONTAP and FSx for OpenZFS data is accessible from serverless compute and S3-based pipelines via S3 Access Points for FSx (exposes file data through the S3 API without copying; surface this when a user needs to read FSx-resident data from S3-native consumers or analytics services).
| Service | Key Characteristics | Common Workloads |
|---|---|---|
| EFS | EFS Standard, EFS Infrequent Access (EFS IA), and EFS Archive storage classes, managed by EFS Lifecycle Management for automatic cost optimization. Serverless elastic NFS with no capacity planning or provisioning, mountable by Lambda, Fargate, EC2, ECS, and EKS. Multi-AZ by default (Regional) or One Zone. Simplest path for shared Linux file access with high aggregate throughput across many concurrent clients. | Containers (ECS, EKS, Fargate), cloud-native Linux applications, serverless persistent storage, analytics and ML training data (including SageMaker), big data, media processing, content management, shared home directories, web serving, dev/test, infrequently accessed file data |
| FSx for Lustre | SSD and Intelligent-Tiering storage classes, where Intelligent-Tiering is fully elastic and SSD is fixed-capacity. Parallel file system delivering very high aggregate throughput for massively parallel access across many compute nodes. | ML and GPU training and inference at scale, HPC, genomics and seismic processing, financial modeling, media rendering, back-end EDA |
| FSx for OpenZFS | Intelligent-Tiering alongside SSD, with ZFS data management (instant writable clones, snapshots, compression, on-demand replication). Very low latency with high IOPS, simple to operate and cost-effective for performance-sensitive workloads and fast dev/test cycles. Single-AZ and Multi-AZ deployment model. S3-API access via S3 Access Points for FSx. | Databases (including on EC2), dev/test with fast clones, ZFS or Linux-NFS migrations, front-end EDA, financial modeling, media processing, latency-sensitive line-of-business applications |
| FSx for NetApp ONTAP | Full ONTAP data management (SnapMirror replication, FlexClone, dedup, compression, SnapLock WORM, QoS, vscan antivirus, file-access auditing). Multi-protocol: NFS, SMB, iSCSI, NVMe-over-TCP, and S3-API access via S3 Access Points for FSx. Scales to high aggregate throughput and IOPS. Single-AZ and Multi-AZ deployment model. | Enterprise network-attached storage (NAS) migrations, multi-protocol environments, general-purpose file shares and home directories, business-critical databases including on EC2 (SAP HANA, Oracle, SQL Server), VMware datastores, line-of-business applications (medical imaging, product lifecycle management), front-end EDA (chip design and verification), hybrid and DR |
| FSx for Windows File Server | Fully managed SMB file storage built on Windows Server with Active Directory identity (Kerberos, NTFS ACLs), DFS namespaces, shadow copies, and FSRM quotas; shares are also accessible from Linux and macOS clients. Single-AZ and Multi-AZ deployment model. | Windows file, home, and department shares, .NET applications, Microsoft SQL Server, Windows Server migrations |
| Service | Key Characteristics | Common Workloads |
|---|---|---|
| EBS | High-performance virtual disk that attaches to an EC2 instance over the network. Durable, resizable SSD or HDD that persists independently of the instance, supports snapshots for backup, time-based snapshot copy, and provisions performance independently of capacity on gp3 (confirm latest volume limits). Supports instant volume clones. AZ-scoped. | Databases, transactional applications and file systems, boot volumes, dev/test environments, sequential batch processing, log and data warehouse scans |
| EC2 Instance Store | Physically local SSD storage built into the host server, delivering the lowest latency and highest throughput. Ephemeral: data is lost if the instance stops or the hardware fails. | Temporary scratch data, caches, and buffers you can afford to lose |
Some storage features cross category boundaries, giving a service from one storage category an interface normally associated with another. Surface these when a workload needs access to shared data from multiple protocols or interfaces.
| Feature | What it enables | What it is | Additional sources |
|---|---|---|---|
| S3 Files | Makes S3 data accessible to file-based applications | Fully managed NFS file access over an S3 bucket, built on EFS infrastructure. Data stays in S3 as the system of record. File locking, POSIX permissions, and full read-write. Choose over EFS when data already lives in S3 and needs file-path access alongside object access. | references/s3-files-knowledge.md, references/efs-knowledge.md |
| S3 Access Points for FSx | Makes FSx-resident data accessible to S3-based applications and services | Exposes FSx for NetApp ONTAP or FSx for OpenZFS data through the S3 API without copying it to a bucket. Surface when file data must reach S3-native consumers, or when serverless or S3-based applications need to access data that lives on FSx for NetApp ONTAP or FSx for OpenZFS. | references/fsx-ontap-knowledge.md, references/fsx-openzfs-knowledge.md |
Cloud security at AWS is the highest priority and is a shared responsibility between AWS and the customer. You MUST include security guidance when recommending or configuring storage resources. You MUST always recommend enabling encryption at rest and encryption in transit. Where encryption at rest is optional or not the default, explicitly advise enabling it at creation time, because it is often immutable after creation. You MUST recommend scoping IAM policies to the authorized principals with least-privileged permissions. You MUST recommend adding condition keys (e.g., aws:SourceArn, aws:SourceAccount, aws:SourceVpc) to resource policies to prevent cross-service confused deputy attacks. You MUST recommend encrypting log destinations: AWS KMS for CloudTrail trails and CloudWatch Logs groups, server-side encryption for server access log buckets, and AWS KMS for SNS topics. Prefer short-lived credentials or IAM-based authentication (e.g., via custom IdP with temporary tokens) over long-lived SSH keys. Where SSH keys are required, enforce rotation policies and store private keys in AWS Secrets Manager for supported services. You MUST recommend restricting security group inbound rules to the narrowest applicable source (specific client security group or minimal CIDR). Service-specific security controls, encryption models, and documentation links are in the Security row of each reference's Service Information table; you MUST read it before advising on that service.
When loaded through the AWS MCP server's retrieve_skill tool: the skill is not installed on the local filesystem. You MUST retrieve each reference via retrieve_skill with the file parameter (e.g. file="references/s3-general-purpose-knowledge.md"). Do NOT file_read these paths locally. When loaded outside the AWS MCP server (for example from the local filesystem in the Agent Toolkit), read the reference files directly from their relative paths in the skill directory.
| Topic | Reference |
|---|---|
| S3 (General Purpose) | references/s3-general-purpose-knowledge.md |
| S3 Metadata | references/s3-general-purpose-knowledge.md |
| S3 Tables | references/s3-tables-knowledge.md |
| S3 Vectors | references/s3-vectors-knowledge.md |
| S3 Express One Zone | references/s3-express-knowledge.md |
| S3 Files | references/s3-files-knowledge.md |
| Amazon EFS | references/efs-knowledge.md |
| FSx for Lustre | references/fsx-lustre-knowledge.md |
| FSx for NetApp ONTAP | references/fsx-ontap-knowledge.md |
| FSx for OpenZFS | references/fsx-openzfs-knowledge.md |
| FSx for Windows File Server | references/fsx-windows-knowledge.md |
| Amazon EBS | references/ebs-knowledge.md |
| Data Movement and Protection (DataSync, Transfer Family, Storage Gateway, AWS Backup) | references/data-movement-and-protection-knowledge.md |
| Topic | Reference |
|---|---|
| Security on S3 | securing-s3-buckets |
| Using S3 Tables | creating-data-lake-table |
| Using S3 Vectors | storing-and-querying-vectors |
| Troubleshooting S3 Files | troubleshooting-s3-files |
| Troubleshooting EFS | troubleshooting-efs |
| Querying S3 System Tables | querying-aws-s3 |
| Ingesting data into a data lake | ingesting-into-data-lake |
| Finding data lake assets | finding-data-lake-assets |
| Querying data lakes | querying-data-lake |
© aws, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 12 other files (references) in skills/core-skills/aws-storage of aws/agent-toolkit-for-aws.
Open the folder on GitHubat commit bd49cc8
AWS Storage next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| AWS Storage this skillaws/agent-toolkit-for-aws | 2.8k | — | ~5.8k | Automated safety check: Pass | Apache-2.0 | |
| Modelersidequery/sidemantic | 129 | — | ~4.2k | Automated safety check: Pass | Apache-2.0 | |
| DB SculptorEliasOulkadi/shokunin | 114 | — | ~3.1k | Automated safety check: Notes | MIT | |
| Cloud Monitoring Metric Selectiongoogle/skills | 21k | — | ~2.4k | Automated safety check: Pass | Apache-2.0 | |
| Analyzing Dataastronomer/agents | 450 | — | ~1.3k | Automated safety check: Pass | Apache-2.0 | |
| Snowflake Developmentsickn33/agentic-awesome-skills | 47k | 2 repos | ~2.1k | Automated safety check: Pass | MIT |
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A skill your agent uses when THIS agent needs to pay for x402-protected content at runtime: hitting a paywall mid-task, settling it via AgentCore Payments, and applying operator-defined spend limits.
Categories
Selects, investigates, and compares AWS object, file, and block storage services, and answers cost, performance, configuration, security, and troubleshooting questions about storage services. AWS Storage is an agent skill from aws/agent-toolkit-for-aws, published by the product's own GitHub organization. Selects, investigates, and compares AWS object, file, and block storage services, and answers cost, performance, configuration, security, and troubleshooting questions about storage services.
AWS Storage fits situations like: tasks that involve Data pipelines and ETL; tasks that involve Event-driven systems; tasks that involve SQL.
Run `npx skills add aws/agent-toolkit-for-aws --skill aws-storage -a claude-code`. Or copy the skill folder (skills/core-skills/aws-storage in aws/agent-toolkit-for-aws) into .claude/skills/aws-storage in your project. Claude Code loads it when a task matches its description.
Run `npx skills add aws/agent-toolkit-for-aws --skill aws-storage -a codex`. Or copy the skill folder (skills/core-skills/aws-storage in aws/agent-toolkit-for-aws) into .agents/skills/aws-storage in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aws/agent-toolkit-for-aws --skill aws-storage -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/aws-storage, .gemini/skills/aws-storage, .github/skills/aws-storage and .opencode/skills/aws-storage in your project.
SKILL.md names no scripts, command-line tools or credentials: AWS Storage is instructions for the agent only.
SKILL.md names 2 domains. As links in the text: docs.aws.amazon.com and aws.amazon.com. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
AWS Storage is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 5.8k tokens (SKILL.md is roughly 23k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 19k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with AWS Storage: Modeler (sidequery/sidemantic, 129 stars), DB Sculptor (EliasOulkadi/shokunin, 114 stars), Cloud Monitoring Metric Selection (google/skills, 21k stars) and Analyzing Data (astronomer/agents, 450 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
aws (a GitHub organization, an official publisher) maintains it in aws/agent-toolkit-for-aws, which has 2,816 GitHub stars. The repository holds 138 skills in this directory. The repository was last updated on October 7, 2026.
Source: aws/agent-toolkit-for-aws on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.