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Aws Cli Beast

Community
giuseppe-trisciuoglio
aws-cli-beast

Provides advanced AWS CLI patterns for managing EC2, Lambda, S3, DynamoDB, RDS, VPC, IAM, and CloudWatch. Generates bulk operation scripts, automates cross-service workflows, validates security configurations, and executes JMESPath queries for complex filtering. Triggers on "aws cli help", "aws command line", "aws scripting", "aws automation", "aws batch operations", "aws bulk operations", "aws cli pagination", "aws multi-region", "aws profiles", "aws cli troubleshooting".

Overview

Publishergiuseppe-trisciuoglio
Repositorydeveloper-kit
Skill nameaws-cli-beast
Stars
345
Forks
41
Bundled files
6
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by giuseppe-trisciuoglio on GitHub. Read the source before you install it.

Installation

Install the Aws Cli Beast AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/giuseppe-trisciuoglio/developer-kit.git /tmp/developer-kit
mkdir -p .claude/skills
cp -r /tmp/developer-kit/plugins/developer-kit-aws/skills/aws/aws-cli-beast .claude/skills/aws-cli-beast
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Aws Cli Beast in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Aws Cli Beast on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Aws Cli Beast is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

AWS CLI Beast Mode

Overview

Advanced AWS CLI patterns for speed, precision, and security-first automation. Covers JMESPath queries, bulk operations, waiters, cross-account access, and destructive operation safety.

When to Use

  • Bulk operations across thousands of AWS resources
  • Advanced JMESPath filtering and output transformation
  • Automated scripts for AWS routines
  • Multi-profile and multi-region management
  • Security auditing and compliance checks
  • CLI-driven infrastructure-as-code workflows

Instructions

Step 1: Categorize the Request

CategoryServicesCommands
ComputeEC2, Lambdadescribe-instances, invoke, publish-version
StorageS3sync, cp, mb, rb, presign
DatabaseDynamoDB, RDSquery, scan, batch-write-item
NetworkingVPC, Route53describe-vpcs, describe-security-groups
SecurityIAMsimulate-principal-policy, get-policy-version
ObservabilityCloudWatchget-metric-statistics, filter-log-events

Step 2: Apply Beast Mode Principles

  1. Dry-run first: Always validate with --dryrun or --dry-run
  2. Query server-side: Use --query with JMESPath to filter before transfer
  3. Batch intelligently: Paginate with --max-results and parallelize with xargs
  4. Wait properly: Use built-in waiters or exponential backoff polling
  5. Switch contexts: Use --profile and --region for multi-account operations

Step 3: Validate Destructive Operations

MANDATORY for any destructive operation:

bash
# S3 sync with delete - MUST dry-run first
aws s3 sync s3://source/ s3://dest/ --delete --dryrun
# Review output, then remove --dryrun only if satisfied

# Bulk EC2 stop - validate targets first
aws ec2 describe-instances \
  --filters "Name=tag:Environment,Values=development" \
  --query 'Reservations[].Instances[?State.Name==`running`].InstanceId' \
  --output text
# Confirm list, then pipe to stop command

# IAM policy attachment - simulate first
aws iam simulate-principal-policy \
  --policy-source-arn arn:aws:iam::123456789012:user/myuser \
  --action-names s3:DeleteObject \
  --resource-arns arn:aws:s3:::my-bucket/*

Step 4: Reference Detailed Guides

  • compute-mastery.md - EC2, Lambda, Spot Fleets, ASG
  • data-ops-beast.md - S3 multipart, DynamoDB batch, RDS snapshots
  • networking-security-hardened.md - VPC Flow Logs, IAM policies, security groups
  • automation-patterns.md - Shell aliases, JMESPath templates, CI/CD integration

Examples

Example 1: Bulk EC2 Stop

"Stop all development instances"

bash
# 1. Dry-run: identify targets
aws ec2 describe-instances \
  --filters "Name=tag:Environment,Values=development" \
           "Name=instance-state-name,Values=running" \
  --query 'Reservations[].Instances[].InstanceId' \
  --output text

# 2. Confirm IDs, then execute
aws ec2 describe-instances \
  --filters "Name=tag:Environment,Values=development" \
           "Name=instance-state-name,Values=running" \
  --query 'Reservations[].Instances[].InstanceId' \
  --output text | xargs aws ec2 stop-instances --instance-ids

Example 2: S3 Migration with Encryption

"Migrate data between buckets with SSE"

bash
# 1. Dry-run migration
aws s3 sync s3://source-bucket/ s3://dest-bucket/ \
  --sse AES256 \
  --storage-class GLACIER \
  --exclude "*.tmp" \
  --dryrun

# 2. Enable versioning on destination
aws s3api put-bucket-versioning \
  --bucket dest-bucket \
  --versioning-configuration Status=Enabled

# 3. Execute after review
aws s3 sync s3://source-bucket/ s3://dest-bucket/ \
  --sse AES256 \
  --storage-class GLACIER \
  --exclude "*.tmp"

Example 3: IAM Security Audit

"Find overprivileged IAM users"

bash
aws iam list-users --query 'Users[].UserName' --output text | \
while read user; do
  echo "Checking $user..."
  aws iam simulate-principal-policy \
    --policy-source-arn "arn:aws:iam::123456789012:user/$user" \
    --action-names DeleteItem,DeleteTable,DeleteFunction \
    --resource-arns "*" \
    --query 'EvaluationResults[?EvalDecision==`allowed`]'
done

Example 4: Multi-Region Lambda Deployment

"Deploy Lambda to all regions"

bash
for region in us-east-1 us-west-2 eu-west-1; do
  echo "Deploying to $region..."
  aws lambda update-function-code \
    --function-name my-function \
    --zip-file fileb://function.zip \
    --region $region \
    --publish
  aws lambda wait function-active \
    --function-name my-function \
    --region $region
done

Example 5: JMESPath Advanced Filtering

"Get running instances with specific tags as table"

bash
aws ec2 describe-instances \
  --query 'Reservations[].Instances[?State.Name==`running`].[InstanceId,Tags[?Key==`Name`].Value[0]|[0],PrivateIpAddress]' \
  --output table

Best Practices

  1. Use --output json for programmatic processing
  2. Filter with JMESPath server-side before transfer
  3. Implement retry logic with exponential backoff
  4. Use waiters instead of manual polling loops
  5. Tag all resources for cost allocation and automation
  6. Separate dev/staging/prod with AWS profiles
  7. Enable CloudTrail for audit compliance
  8. Validate IAM policies with simulate-principal-policy before attachment
  9. Use --dry-run on every state-modifying operation
  10. Enable MFA for security-sensitive operations

Constraints and Warnings

Rate Limiting

  • AWS API throttling applies; use --max-throttle and exponential backoff
  • Check aws service-quotas for current limits

Pagination

  • Default page size is variable; use --max-results for consistency
  • Use --no-paginate with jq for full dataset processing

Destructive Operations

  • S3 sync --delete: Irreversibly removes files not in source
  • EC2 terminate-instances: Cannot be undone; validate instance IDs first
  • IAM detach/policy: May break existing access; simulate before applying
  • RDS delete-db-instance: Snapshots do not protect all scenarios; verify retention

Security

  • Never commit AWS credentials; use aws configure or environment variables
  • Rotate access keys regularly with aws iam create-access-key
  • Use least-privilege: simulate before granting permissions

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Aws Cli Beast AI skill do?

Provides advanced AWS CLI patterns for managing EC2, Lambda, S3, DynamoDB, RDS, VPC, IAM, and CloudWatch. Generates bulk operation scripts, automates cross-service workflows, validates security configurations, and executes JMESPath queries for complex filtering. Triggers on "aws cli help", "aws command line", "aws scripting", "aws automation", "aws batch operations", "aws bulk operations", "aws cli pagination", "aws multi-region", "aws profiles", "aws cli troubleshooting".

Why use Aws Cli Beast on TypingMind?

Because you install it once and use it with any model. Aws Cli Beast is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Aws Cli Beast in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/giuseppe-trisciuoglio/developer-kit/tree/main/plugins/developer-kit-aws/skills/aws/aws-cli-beast. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Aws Cli Beast?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Aws Cli Beast?

As many as you like. As long as a model supports skills, you can use Aws Cli Beast with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Aws Cli Beast AI skill free?

Yes. It is published on GitHub by giuseppe-trisciuoglio under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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