2.3K skills ・ Works with: AWS · sorted by popularity
Choose tools and workflows for 3D modeling, dimensioned CAD parts, scan processing, rendering, and fabrication. Capability tables and recipes help connect tasks from photographs or initial designs to printable parts, CNC toolpaths, or game assets.
Compare job requirements with your current skills and experience to identify learning priorities. Get a report with ranked gaps, study resources found through web searches, estimated learning time, and a recommended study sequence.
Work with large scientific arrays by storing data in smaller blocks and reading selected portions. Guides compression, cloud storage, format migration, and NumPy, Dask, or Xarray integration while preserving dimensions, units, and other metadata.
Retrieve, validate, and export protocols.io experimental procedures while preserving version and attribution details. Check saved JSON offline, save protocol PDFs, or prepare reviewable plans for updates and publication; bundled scripts do not execute changes.
Find and download public cancer imaging data from NCI Imaging Data Commons. Select CT, MR, PET, or pathology datasets using metadata, view images in a browser, and check licenses and citations before using the data.
Optimize scientific Python workloads for NVIDIA GPUs by choosing suitable libraries and reducing bottlenecks. Compare outputs with the CPU version and measure complete processing time, including data transfers, to determine whether the GPU implementation provides a useful improvement.
Process molecular datasets in Python through a simpler interface to RDKit, a chemistry toolkit. Convert and standardize structures, calculate molecular properties, compare and cluster compounds, generate 3D conformations, and visualize results for drug discovery workflows.
Manage scientific datasets with DataLad and git-annex, which tracks large files separately from Git history. Retrieve selected data, record analysis commands and their inputs and outputs, rerun recorded work, and publish history alongside file content.
Analyze a project’s code and existing settings to recommend useful Claude Code automations. Receive tailored suggestions for event-triggered actions, specialist agents, skills, plugins, and external tool connections, with reasons and setup locations.
Create or update reusable skills for AI agents. Plan clear instructions, organize scripts, reference documents, and templates, then validate the skill and package its files for distribution.
Configure MCP connections, which link AI agents to tools, for AWS documentation search and API access. Check existing settings, choose between documentation-only and full API access, verify available tools, and troubleshoot connection or credential problems.
Identify unused AWS resources, estimate potential savings, and prepare cleanup scripts with preview steps. Covers storage retention policies and scheduled cleanup so teams can review dependencies before removing resources and track the resulting savings.
Store database credentials and API keys in AWS Secrets Manager, configure automatic credential changes, and connect applications to stored secrets. Follow examples for access policies, Lambda rotation functions, and integration with ECS and EKS.
Design AWS applications that run without managing servers and respond to events. Get guidance on Lambda APIs, asynchronous workflows, retries, monitoring, and scaling, with implementation patterns based on the AWS Well-Architected Framework.
Deploy and manage virtual servers on Amazon EC2. Covers instance selection, reusable machine images and launch templates, automatic capacity changes, Spot instances, and troubleshooting, with AWS CLI commands and Terraform configuration examples.
Analyze AWS spending by service, region, and resource tags to find unused resources and oversized configurations. Get specific cost reduction recommendations, estimated savings, and guidance for budgets and ongoing cost monitoring.
Build and refactor AWS infrastructure with TypeScript or Python using AWS CDK, a toolkit for defining cloud resources in code. Get guidance on resource selection, Lambda packaging, stack organization, and checks before deployment.
Build AWS applications that run without managing servers directly. Covers Lambda functions, APIs, DynamoDB storage, message-driven workflows with SQS/SNS, deployment through SAM/CDK, and reducing the delay when a function starts for the first time.
Build and deploy AWS Lambda functions without managing servers. Guidance covers APIs, scheduled jobs, event triggers, permissions, shared dependencies, and performance tuning, with code and command examples for deployment and troubleshooting.
Deploy and operate AI agents with AWS Bedrock AgentCore. Choose services for hosting agents, connecting external APIs, managing credentials and conversation state, monitoring activity, and assessing agent quality through automated evaluations.
Deploy container applications on AWS using ECS and Fargate, which runs containers without managing servers. Covers image publishing, task configuration, load balancing, automatic scaling, deployment updates, and troubleshooting failed or unhealthy containers.
Configure and audit AWS users, roles, and access policies. Work through least-privilege permissions, granting only the access needed, along with GitHub Actions authentication, cross-account access, permission boundaries, and checks for unused or excessive access.
Provision and manage Amazon RDS relational databases on AWS. Follow guidance for backups, recovery, security, monitoring, multi-zone redundancy, and read replicas—database copies that handle read traffic—with AWS CLI commands and a Terraform configuration example.
Configure Amazon S3 storage with encryption, access restrictions, and version history. Set rules for moving or deleting older files, replicate data across regions, create temporary access links, and investigate permission or policy problems.