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Configure Django file storage on AWS S3 using django-storages and boto3. Separate public and private files, create temporary download or upload links, configure CloudFront delivery, and test storage behavior without accessing real S3 buckets.

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• Moving Django static files and uploads to S3
• Separating public files from private documents
• Creating temporary download and direct upload links

Prepare an isolated Python environment for SageMaker deployments and AWS automation. The skill selects a supported Python version, installs current AWS packages, and checks versions so installation and execution use the same environment.

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• Prepare a Python environment for SageMaker deployment
• Separate AWS automation dependencies from system Python
• Check installed AWS package versions

Analyze AWS IAM access policies with boto3 and Cloudsplaining-style checks to find permissions that could grant greater access. Produces a JSON report linking risky permissions to users or roles, with severity ratings and remediation guidance.

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• Audit AWS IAM policies for excessive permissions
• Find permission combinations that enable privilege escalation
• Trace risky policies to affected users and roles

Analyze AWS CloudTrail logs against normal activity patterns to spot unusual API calls, IP addresses, errors, and sensitive changes. Produce a JSON report with anomaly scores, suspicious users, and suggested investigation steps.

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• Investigating possible misuse of AWS credentials
• Finding first-time API calls and unusual IP activity
• Reviewing IAM, KMS, and S3 policy changes

AWS SDK for Python (boto3/botocore) development patterns. You MUST use this skill when writing Python code that uses AWS services via boto3 or botocore. This includes creating service clients or resources, configuring sessions and credentials, handling errors with ClientError, using paginators and waiters, S3 file transfers and presigned URLs, DynamoDB table operations, and any boto3/botocore client configuration. Use this skill whenever Python code imports boto3 or botocore, or when the user asks about AWS operations in Python.

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Analyze access logs from AWS S3, GCS, and Azure Blob Storage to spot unusual downloads, unfamiliar IP addresses, and request spikes. Compare activity with normal patterns and prioritize findings for suspected data theft investigations.

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• Investigating suspected data theft from cloud storage
• Finding bulk downloads outside normal working hours
• Checking unfamiliar IP addresses and sudden request spikes

Detect AWS IAM privilege escalation paths using boto3 and Cloudsplaining policy analysis to identify overly permissive policies, dangerous permission combinations, and least-privilege violations

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Detect unusual API call patterns in AWS CloudTrail logs using boto3, statistical baselining, and behavioral analysis to identify credential compromise, privilege escalation, and unauthorized resource access.

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Set up an isolated Python environment for SageMaker / AWS work, with the right Python version and current boto3. Use this skill whenever Python code will be executed for a SageMaker deployment, training job, or any AWS automation — including when about to run `pip install`, when about to invoke `boto3`, when creating or activating a virtualenv, or when the user asks to "set up the environment". Never use system Python and never `pip install` into it. Always isolate. This skill prevents the most common failure modes: wrong Python version, dependency conflicts, and stale SDKs.

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tespit etme
unusual API call patterns in AWS CloudTrail logs using boto3, statistical baselining, and behavioral analysis to identify credential compromise, privilege escalation, and unauthorized resource access.

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tespit etme
AWS IAM privilege escalation paths using boto3 and Cloudsplaining policy analysis to identify overly permissive policies, dangerous permission combinations, and least-privilege violations

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Detect AWS IAM privilege escalation paths using boto3 and Cloudsplaining policy analysis to identify overly permissive policies, dangerous permission combinations, and least-privilege violations

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Detect unusual API call patterns in AWS CloudTrail logs using boto3, statistical baselining, and behavioral analysis to identify credential compromise, privilege escalation, and unauthorized resource access.

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Detect unusual API call patterns in AWS CloudTrail logs using boto3, statistical baselining, and behavioral analysis to identify credential compromise, privilege escalation, and unauthorized resource access.

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Detect AWS IAM privilege escalation paths using boto3 and Cloudsplaining policy analysis to identify overly permissive policies, dangerous permission combinations, and least-privilege violations

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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.

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• Inventory unused resources and estimate savings
• Prepare cleanup scripts for EBS volumes and Elastic IPs
• Review S3 versions and storage retention rules

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.

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• Move hardcoded credentials into AWS Secrets Manager
• Configure automatic database credential rotation
• Provide stored secrets to ECS and EKS workloads

Support authorized penetration testing of AWS environments by examining IAM permissions and resources such as S3, EC2, and Lambda. It guides identifying privilege escalation paths, where access can be expanded, and documenting security audit findings.

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• Review IAM permissions for privilege escalation paths
• Assess access to S3 buckets and Lambda functions
• Validate credential exposure risks through EC2 metadata

Assess serverless functions, primarily AWS Lambda, for unsafe execution of external input and unauthorized changes to code or added libraries. Combine source inspection, activity logs, and permission reviews to identify injection risks and recommend detection and prevention measures.

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• Reviewing Lambda event handlers for injection vulnerabilities
• Investigating unauthorized function or layer changes
• Checking privilege escalation through Lambda execution roles

Audit AWS S3 buckets for public exposure, excessive permissions, and gaps in encryption or versioning settings. Use AWS CLI, Prowler, and access analysis to compile findings, prioritize issues, and plan or apply configuration fixes.

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• Reviewing S3 settings in a newly onboarded AWS account
• Investigating alerts about publicly exposed buckets
• Preparing periodic access control audit reports

Use MLflow to record machine learning experiments, compare results, and manage model versions. The skill covers logging training settings, metrics, and files, organizing a model registry, and preparing models for local or cloud deployment.

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• Comparing training settings and model performance
• Recording experiments for later reproduction
• Managing model versions and release stages

Amazon Redshift is NOT PostgreSQL; corrects LLM mistakes; covers SQL, metadata, connectivity/auth, storage, query performance, and operations. Applies ONLY to Redshift (provisioned or Serverless). Pushes back on: CREATE INDEX, string_agg, pg_catalog, text type, SERIAL, stl_query. Triggers on: Redshift SQL, Redshift CREATE TABLE, Redshift COPY/UNLOAD, slow or suddenly slow Redshift query, EXPLAIN plan, queue wait, stale statistics/ANALYZE, result cache, Redshift permission denied, Redshift disk full, storage growth, snapshot/backup cost, ghost rows/bloat, VACUUM, spill to disk, distribution skew, Redshift system views, QUALIFY, PIVOT, MERGE, materialized views, Redshift Data API (Python/boto3), Redshift workload management (WLM), concurrency scaling, Redshift resize, Spectrum, awsdatacatalog, connection failures (SSL/TLS, ODBC/JDBC, connection limit), IAM/SAML federation, IAM Identity Center, datashares. Does NOT apply to: S3 policies, Athena, Glue/Iceberg outside Redshift, Aurora, RDS, DynamoDB.

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Create an isolated Python environment with a compatible Python version and boto3 for SageMaker or AWS automation. Use before executing AWS Python code; do not use for general Python environment setup.

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Amazon SES (Simple Email Service) sends and receives email through AWS: transactional and marketing mail, templates, bounce and complaint tracking, and inbound mail processing. Use when a user asks to send email with SES, verify a domain or set up DKIM, move an SES account out of the sandbox, create or bulk-send email templates, add an attachment or unsubscribe header, handle bounces and complaints with SNS, manage the suppression list, or store incoming mail in S3 with receipt rules. Covers the SES v2 API (aws sesv2, boto3 sesv2).

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