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Cloudwatch

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itsmostafa
cloudwatch

AWS CloudWatch monitoring for logs, metrics, alarms, and dashboards. Use when setting up monitoring, creating alarms, querying logs with Insights, configuring metric filters, building dashboards, or troubleshooting application issues.

Overview

Publisheritsmostafa
Repositoryaws-agent-skills
Skill namecloudwatch
Stars
1.2K
Forks
445
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Cloudwatch 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/itsmostafa/aws-agent-skills/tree/main/skills/cloudwatch
  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/itsmostafa/aws-agent-skills.git /tmp/aws-agent-skills
mkdir -p .claude/skills
cp -r /tmp/aws-agent-skills/skills/cloudwatch .claude/skills/cloudwatch
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cloudwatch 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 Cloudwatch 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 Cloudwatch 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 CloudWatch

Amazon CloudWatch provides monitoring and observability for AWS resources and applications. It collects metrics, logs, and events, enabling you to monitor, troubleshoot, and optimize your AWS environment.

Table of Contents

Core Concepts

Metrics

Time-ordered data points published to CloudWatch. Key components:

  • Namespace: Container for metrics (e.g., AWS/Lambda)
  • Metric name: Name of the measurement (e.g., Invocations)
  • Dimensions: Name-value pairs for filtering (e.g., FunctionName=MyFunc)
  • Statistics: Aggregations (Sum, Average, Min, Max, SampleCount, pN)

Logs

Log data from AWS services and applications:

  • Log groups: Collections of log streams
  • Log streams: Sequences of log events from same source
  • Log events: Individual log entries with timestamp and message

Alarms

Automated actions based on metric thresholds:

  • States: OK, ALARM, INSUFFICIENT_DATA
  • Actions: SNS notifications, Lambda functions, Auto Scaling, EC2 actions, Systems Manager OpsItems
  • Types: metric (incl. metric math, anomaly detection, Metrics Insights), composite, log (scheduled Logs Insights query evaluated M-out-of-N on query results)
  • Mute rules: scheduled windows that mute actions while alarms keep evaluating

Common Patterns

Create a Metric Alarm

AWS CLI:

bash
# CPU utilization alarm for EC2
aws cloudwatch put-metric-alarm \
  --alarm-name "HighCPU-i-1234567890abcdef0" \
  --metric-name CPUUtilization \
  --namespace AWS/EC2 \
  --statistic Average \
  --period 300 \
  --threshold 80 \
  --comparison-operator GreaterThanThreshold \
  --evaluation-periods 2 \
  --dimensions Name=InstanceId,Value=i-1234567890abcdef0 \
  --alarm-actions arn:aws:sns:us-east-1:123456789012:alerts \
  --ok-actions arn:aws:sns:us-east-1:123456789012:alerts

boto3:

python
import boto3

cloudwatch = boto3.client('cloudwatch')

cloudwatch.put_metric_alarm(
    AlarmName='HighCPU-i-1234567890abcdef0',
    MetricName='CPUUtilization',
    Namespace='AWS/EC2',
    Statistic='Average',
    Period=300,
    Threshold=80.0,
    ComparisonOperator='GreaterThanThreshold',
    EvaluationPeriods=2,
    Dimensions=[
        {'Name': 'InstanceId', 'Value': 'i-1234567890abcdef0'}
    ],
    AlarmActions=['arn:aws:sns:us-east-1:123456789012:alerts'],
    OKActions=['arn:aws:sns:us-east-1:123456789012:alerts']
)

Lambda Error Rate Alarm

bash
aws cloudwatch put-metric-alarm \
  --alarm-name "LambdaErrorRate-MyFunction" \
  --metrics '[
    {
      "Id": "errors",
      "MetricStat": {
        "Metric": {
          "Namespace": "AWS/Lambda",
          "MetricName": "Errors",
          "Dimensions": [{"Name": "FunctionName", "Value": "MyFunction"}]
        },
        "Period": 60,
        "Stat": "Sum"
      },
      "ReturnData": false
    },
    {
      "Id": "invocations",
      "MetricStat": {
        "Metric": {
          "Namespace": "AWS/Lambda",
          "MetricName": "Invocations",
          "Dimensions": [{"Name": "FunctionName", "Value": "MyFunction"}]
        },
        "Period": 60,
        "Stat": "Sum"
      },
      "ReturnData": false
    },
    {
      "Id": "errorRate",
      "Expression": "errors/invocations*100",
      "Label": "Error Rate",
      "ReturnData": true
    }
  ]' \
  --threshold 5 \
  --comparison-operator GreaterThanThreshold \
  --evaluation-periods 3 \
  --alarm-actions arn:aws:sns:us-east-1:123456789012:alerts

Query Logs with Insights

bash
# Find errors in Lambda logs
aws logs start-query \
  --log-group-name /aws/lambda/MyFunction \
  --start-time $(date -d '1 hour ago' +%s) \
  --end-time $(date +%s) \
  --query-string '
    fields @timestamp, @message
    | filter @message like /ERROR/
    | sort @timestamp desc
    | limit 50
  '

# Get query results
aws logs get-query-results --query-id <query-id>

boto3:

python
import boto3
import time

logs = boto3.client('logs')

# Start query
response = logs.start_query(
    logGroupName='/aws/lambda/MyFunction',
    startTime=int(time.time()) - 3600,
    endTime=int(time.time()),
    queryString='''
        fields @timestamp, @message
        | filter @message like /ERROR/
        | sort @timestamp desc
        | limit 50
    '''
)

query_id = response['queryId']

# Wait for results
while True:
    result = logs.get_query_results(queryId=query_id)
    if result['status'] == 'Complete':
        break
    time.sleep(1)

for row in result['results']:
    print(row)

Create Metric Filter

Extract metrics from log patterns:

bash
# Create metric filter for error count
aws logs put-metric-filter \
  --log-group-name /aws/lambda/MyFunction \
  --filter-name ErrorCount \
  --filter-pattern "ERROR" \
  --metric-transformations \
    metricName=ErrorCount,metricNamespace=MyApp,metricValue=1,defaultValue=0

Create a Log Alarm

Alarm directly on a Logs Insights query (no metric filter needed). CloudWatch creates and manages the underlying scheduled query. The ScheduledQueryRoleARN role must trust logs.amazonaws.com and allow logs:StartQuery and logs:GetQueryResults on the log group ARNs, plus logs:StopQuery and logs:DescribeLogGroups on "Resource": "*" (these two support no resource-level permissions, so a log-group-scoped statement never matches them).

bash
# ALARM when >100 errors in 3 of the last 5 query runs
aws cloudwatch put-log-alarm \
  --alarm-name "HighErrorCount" \
  --comparison-operator GreaterThanThreshold \
  --threshold 100 \
  --query-results-to-evaluate 5 \
  --query-results-to-alarm 3 \
  --treat-missing-data notBreaching \
  --alarm-actions arn:aws:sns:us-east-1:123456789012:alerts \
  --scheduled-query-configuration '{
    "QueryString": "fields @timestamp, @message | filter @message like /ERROR/",
    "LogGroupIdentifiers": ["/aws/lambda/MyFunction"],
    "ScheduledQueryRoleARN": "arn:aws:iam::123456789012:role/ScheduledQueryRole",
    "AggregationExpression": "count(*)",
    "ScheduleConfiguration": {
      "ScheduleExpression": "rate(10 minutes)",
      "StartTimeOffset": 600
    }
  }'

# Log alarms are omitted from describe-alarms unless requested
aws cloudwatch describe-alarms --alarm-types LogAlarm

Publish Custom Metrics

python
import boto3

cloudwatch = boto3.client('cloudwatch')

cloudwatch.put_metric_data(
    Namespace='MyApp',
    MetricData=[
        {
            'MetricName': 'OrdersProcessed',
            'Value': 1,
            'Unit': 'Count',
            'Dimensions': [
                {'Name': 'Environment', 'Value': 'Production'},
                {'Name': 'OrderType', 'Value': 'Standard'}
            ]
        }
    ]
)

Create Dashboard

bash
cat > dashboard.json << 'EOF'
{
  "widgets": [
    {
      "type": "metric",
      "x": 0, "y": 0, "width": 12, "height": 6,
      "properties": {
        "title": "Lambda Invocations",
        "metrics": [
          ["AWS/Lambda", "Invocations", "FunctionName", "MyFunction"]
        ],
        "period": 60,
        "stat": "Sum",
        "region": "us-east-1"
      }
    },
    {
      "type": "log",
      "x": 12, "y": 0, "width": 12, "height": 6,
      "properties": {
        "title": "Recent Errors",
        "query": "SOURCE '/aws/lambda/MyFunction' | filter @message like /ERROR/ | limit 20",
        "region": "us-east-1"
      }
    }
  ]
}
EOF

aws cloudwatch put-dashboard \
  --dashboard-name MyAppDashboard \
  --dashboard-body file://dashboard.json

CLI Reference

Metrics Commands

CommandDescription
aws cloudwatch put-metric-dataPublish custom metrics
aws cloudwatch get-metric-dataRetrieve metric values
aws cloudwatch get-metric-statisticsGet aggregated statistics
aws cloudwatch list-metricsList available metrics

Alarms Commands

CommandDescription
aws cloudwatch put-metric-alarmCreate or update alarm
aws cloudwatch put-log-alarmCreate or update log query alarm
aws cloudwatch describe-alarmsList alarms (--alarm-types LogAlarm for log alarms)
aws cloudwatch describe-alarm-contributorsShow breaching contributors of a multi-contributor alarm
aws cloudwatch put-alarm-mute-ruleCreate or update scheduled mute window
aws cloudwatch list-alarm-mute-rulesList mute rules (--statuses SCHEDULED ACTIVE EXPIRED)
aws cloudwatch delete-alarm-mute-ruleDelete mute rule (unmutes immediately)
aws cloudwatch set-alarm-stateManually set alarm state
aws cloudwatch delete-alarmsDelete alarms

Logs Commands

CommandDescription
aws logs create-log-groupCreate log group
aws logs put-log-eventsWrite log events
aws logs filter-log-eventsSearch log events
aws logs start-queryStart Insights query
aws logs put-metric-filterCreate metric filter
aws logs put-retention-policySet log retention

Best Practices

Metrics

  • Use dimensions wisely — too many creates metric explosion
  • Aggregate before publishing — batch custom metrics
  • Use high-resolution metrics (1-second) only when needed
  • Set meaningful units for custom metrics

Alarms

  • Use composite alarms for complex conditions
  • Set appropriate evaluation periods to avoid flapping
  • Include OK actions to track recovery
  • Use anomaly detection for dynamic thresholds
  • Use log alarms instead of metric filter + metric alarm for query-based conditions; set --treat-missing-data notBreaching for sparse errors, breaching to detect logs that stop arriving
  • Stagger log alarm schedules — concurrent scheduled query executions per account are capped at 100
  • Set --warm-up-configuration on alarms created alongside new resources (1-2880 min) to avoid noise before metrics publish
  • Use --evaluation-window WallClockWindow={Timezone=...} for daily/weekly batch or backup alarms; keep the default sliding window for Auto Scaling
  • Use mute rules for maintenance, not disable-alarm-actions; enable-alarm-actions does not unmute an active mute rule

Logs

  • Set retention policies — don't keep logs forever
  • Use structured logging (JSON) for better querying
  • Create metric filters for key events
  • Use Contributor Insights for top-N analysis

Cost Optimization

  • Delete unused dashboards
  • Reduce log retention for non-critical logs
  • Avoid high-resolution metrics unless necessary
  • Use log subscription filters instead of polling

Troubleshooting

Missing Metrics

Causes:

  • Service not publishing yet (wait 1-5 minutes)
  • Wrong namespace/dimensions
  • Detailed monitoring not enabled (EC2)

Debug:

bash
# List metrics for a namespace
aws cloudwatch list-metrics \
  --namespace AWS/Lambda \
  --dimensions Name=FunctionName,Value=MyFunction

Alarm Stuck in INSUFFICIENT_DATA

Causes:

  • Metric not being published
  • Dimensions mismatch
  • Evaluation period too short
  • Warm-up period still active (ends early once data fills the window unless OnlyStartEvaluatingAfterWarmUpPeriodEnds=true)
  • Log alarm: just created/updated (query, schedule, or log group changes reset state), scheduled query role lacks permissions (EvaluationState = EVALUATION_ERROR, see StateReason), or ingestion lag (shift window back with EndTimeOffset)

Debug:

bash
# Check if metric has data
aws cloudwatch get-metric-statistics \
  --namespace AWS/Lambda \
  --metric-name Invocations \
  --dimensions Name=FunctionName,Value=MyFunction \
  --start-time $(date -d '1 hour ago' -u +%Y-%m-%dT%H:%M:%SZ) \
  --end-time $(date -u +%Y-%m-%dT%H:%M:%SZ) \
  --period 60 \
  --statistics Sum

# Log alarm: check evaluation state and reason
aws cloudwatch describe-alarms --alarm-types LogAlarm --alarm-names HighErrorCount \
  --query 'LogAlarms[].[StateValue,EvaluationState,StateReason]'

Log Events Not Appearing

Causes:

  • IAM permissions missing
  • CloudWatch Logs agent not running
  • Log group doesn't exist

Debug:

bash
# Check log streams
aws logs describe-log-streams \
  --log-group-name /aws/lambda/MyFunction \
  --order-by LastEventTime \
  --descending \
  --limit 5

High CloudWatch Costs

Check usage:

bash
# Get PutLogEvents usage
aws cloudwatch get-metric-statistics \
  --namespace AWS/Logs \
  --metric-name IncomingBytes \
  --dimensions Name=LogGroupName,Value=/aws/lambda/MyFunction \
  --start-time $(date -d '7 days ago' -u +%Y-%m-%dT%H:%M:%SZ) \
  --end-time $(date -u +%Y-%m-%dT%H:%M:%SZ) \
  --period 86400 \
  --statistics Sum

References

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 Cloudwatch AI skill do?

AWS CloudWatch monitoring for logs, metrics, alarms, and dashboards. Use when setting up monitoring, creating alarms, querying logs with Insights, configuring metric filters, building dashboards, or troubleshooting application issues.

Why use Cloudwatch on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/itsmostafa/aws-agent-skills/tree/main/skills/cloudwatch. 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 Cloudwatch?

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 Cloudwatch?

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

Is the Cloudwatch AI skill free?

Yes. It is published on GitHub by itsmostafa 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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