Aws Batch logo

Aws Batch

Organization
skillmds
aws-batch

Use when working with Aws Batch — aWS Batch compute environment management, job queue analysis, scheduling policy review, and array job monitoring. Covers compute resource utilization, job status tracking, failure investigation, Fargate vs EC2 comparison, and queue priority analysis.

Overview

Publisherskillmds
Repositoryskillmd
Skill nameaws-batch
Stars
249
Forks
3
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by skillmds on GitHub. Read the source before you install it.

Installation

Install the Aws Batch 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.
    https://github.com/skillmds/skillmd/tree/main/plugins/cloud-devops/skills/aws-batch
  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/skillmds/skillmd.git /tmp/skillmd
mkdir -p .claude/skills
cp -r /tmp/skillmd/plugins/cloud-devops/skills/aws-batch .claude/skills/aws-batch
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Aws Batch 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 Batch 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 Batch 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 Batch Skill

Analyze AWS Batch compute environments and jobs with parallel execution and anti-hallucination guardrails.

Relationship to other AWS skills:

  • aws-batch/ → Batch-specific analysis (compute environments, job queues, jobs)
  • aws/ → "How to execute" (parallel patterns, throttling, output format)

CRITICAL: Parallel Execution Requirement

ALL independent operations MUST run in parallel using background jobs (&) and wait.

bash
#!/bin/bash
export AWS_PAGER=""

for ce in $compute_envs; do
  describe_compute_env "$ce" &
done
wait

Helper Functions

bash
#!/bin/bash
export AWS_PAGER=""

# List compute environments
list_compute_environments() {
  aws batch describe-compute-environments \
    --output text \
    --query 'computeEnvironments[].[computeEnvironmentName,state,status,type,computeResources.type,computeResources.minvCpus,computeResources.maxvCpus,computeResources.desiredvCpus]'
}

# List job queues
list_job_queues() {
  aws batch describe-job-queues \
    --output text \
    --query 'jobQueues[].[jobQueueName,state,status,priority,computeEnvironmentOrder[].computeEnvironment]'
}

# List jobs by status
list_jobs() {
  local queue=$1 status=$2
  aws batch list-jobs --job-queue "$queue" --job-status "$status" \
    --output text \
    --query 'jobSummaryList[].[jobId,jobName,status,createdAt,startedAt,stoppedAt,statusReason]' | head -20
}

# Describe jobs (batch)
describe_jobs() {
  local job_ids="$@"
  aws batch describe-jobs --jobs $job_ids \
    --output text \
    --query 'jobs[].[jobId,jobName,status,statusReason,container.exitCode,container.reason,startedAt,stoppedAt]'
}

# List job definitions
list_job_definitions() {
  aws batch describe-job-definitions --status ACTIVE \
    --output text \
    --query 'jobDefinitions[].[jobDefinitionName,revision,type,status,containerProperties.vcpus,containerProperties.memory]' | head -20
}

# List scheduling policies
list_scheduling_policies() {
  aws batch list-scheduling-policies \
    --output text \
    --query 'schedulingPolicies[].[arn,name]' 2>/dev/null
}

Common Operations

1. Compute Environment Overview

bash
#!/bin/bash
export AWS_PAGER=""
aws batch describe-compute-environments \
  --output text \
  --query 'computeEnvironments[].[computeEnvironmentName,state,status,type,computeResources.type,computeResources.minvCpus,computeResources.maxvCpus,computeResources.desiredvCpus]'

2. Job Queue Status with Active Jobs

bash
#!/bin/bash
export AWS_PAGER=""
QUEUES=$(aws batch describe-job-queues --output text --query 'jobQueues[].jobQueueName')
for queue in $QUEUES; do
  {
    info=$(aws batch describe-job-queues --job-queues "$queue" \
      --output text --query 'jobQueues[].[jobQueueName,state,priority]')
    running=$(aws batch list-jobs --job-queue "$queue" --job-status RUNNING \
      --output text --query 'length(jobSummaryList)')
    pending=$(aws batch list-jobs --job-queue "$queue" --job-status PENDING \
      --output text --query 'length(jobSummaryList)')
    submitted=$(aws batch list-jobs --job-queue "$queue" --job-status SUBMITTED \
      --output text --query 'length(jobSummaryList)')
    printf "%s\tRunning:%s\tPending:%s\tSubmitted:%s\n" "$info" "$running" "$pending" "$submitted"
  } &
done
wait

3. Failed Job Investigation

bash
#!/bin/bash
export AWS_PAGER=""
QUEUES=$(aws batch describe-job-queues --output text --query 'jobQueues[].jobQueueName')
for queue in $QUEUES; do
  FAILED=$(aws batch list-jobs --job-queue "$queue" --job-status FAILED \
    --output text --query 'jobSummaryList[:5].jobId')
  [ -z "$FAILED" ] && continue
  aws batch describe-jobs --jobs $FAILED \
    --output text \
    --query 'jobs[].[jobName,status,statusReason,container.exitCode,container.reason,stoppedAt]' &
done
wait

4. Array Job Progress

bash
#!/bin/bash
export AWS_PAGER=""
QUEUE=$1
ARRAY_JOBS=$(aws batch list-jobs --job-queue "$QUEUE" --job-status RUNNING \
  --output text --query 'jobSummaryList[?arrayProperties].jobId')
for job_id in $ARRAY_JOBS; do
  aws batch describe-jobs --jobs "$job_id" \
    --output text \
    --query 'jobs[].[jobName,arrayProperties.size,arrayProperties.statusSummary]' &
done
wait

5. Job Definition Audit

bash
#!/bin/bash
export AWS_PAGER=""
aws batch describe-job-definitions --status ACTIVE \
  --output text \
  --query 'jobDefinitions[].[jobDefinitionName,revision,type,platformCapabilities[0],containerProperties.image,containerProperties.vcpus,containerProperties.memory,retryStrategy.attempts]' | head -30

Anti-Hallucination Rules

  1. Compute environment types - MANAGED or UNMANAGED. Managed CEs auto-scale. Unmanaged CEs require manual instance management.
  2. Compute resource types - EC2, SPOT, FARGATE, or FARGATE_SPOT. Each has different pricing and availability characteristics.
  3. Job status flow - SUBMITTED -> PENDING -> RUNNABLE -> STARTING -> RUNNING -> SUCCEEDED/FAILED. Jobs do not skip states.
  4. vCPU units - minvCpus, maxvCpus, desiredvCpus are in vCPU units, not instance counts. The scheduler maps vCPUs to instance types.
  5. Array job indexing - Array job indices are 0-based. An array of size 100 has indices 0-99. Each index runs as a separate container.

Output Format

Present results as a structured report:

Aws Batch Report
════════════════
Resources discovered: [count]

Resource       Status    Key Metric    Issues
──────────────────────────────────────────────
[name]         [ok/warn] [value]       [findings]

Summary: [total] resources | [ok] healthy | [warn] warnings | [crit] critical
Action Items: [list of prioritized findings]

Target ≤50 lines of output. Use tables for multi-resource comparisons.

Counter-Rationalizations

ShortcutCounterWhy
"I'll skip discovery and check known resources"Always run Phase 1 discovery firstResource names change, new resources appear — assumed names cause errors
"The user only asked for a quick check"Follow the full discovery → analysis flowQuick checks miss critical issues; structured analysis catches silent failures
"Default configuration is probably fine"Audit configuration explicitlyDefaults often leave logging, security, and optimization features disabled
"Metrics aren't needed for this"Always check relevant metrics when availableAPI/CLI responses show current state; metrics reveal trends and intermittent issues
"I don't have access to that"Try the command and report the actual errorAssumed permission failures prevent useful investigation; actual errors are informative

Common Pitfalls

  • PENDING vs RUNNABLE: PENDING means the job is waiting for dependencies. RUNNABLE means it is ready but waiting for compute capacity. Extended RUNNABLE time indicates insufficient capacity.
  • Fargate limitations: Fargate Batch jobs have vCPU/memory limits (max 16 vCPU, 120 GB). EC2 CEs support larger instances.
  • Spot interruptions: SPOT compute environments may lose instances. Check statusReason for "Host EC2 (instance i-xxx) terminated" messages.
  • CloudWatch statistics syntax: Use spaces not commas: --statistics Average Maximum.
  • Job definition revisions: Each update creates a new revision. Jobs reference a specific revision. Use :N suffix for specific revision or omit for latest active.

Frequently asked questions

What does the Aws Batch AI skill do?

Use when working with Aws Batch — aWS Batch compute environment management, job queue analysis, scheduling policy review, and array job monitoring. Covers compute resource utilization, job status tracking, failure investigation, Fargate vs EC2 comparison, and queue priority analysis.

Why use Aws Batch on TypingMind?

Because you install it once and use it with any model. Aws Batch 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 Batch in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/skillmds/skillmd/tree/main/plugins/cloud-devops/skills/aws-batch. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Aws Batch?

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 Batch?

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

Is the Aws Batch AI skill free?

Yes. It is published on GitHub by skillmds 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇