Terravision Cloud Diagrams
patrickchugh/terravision
Draw cloud architecture diagrams for AWS, Azure or GCP with the official provider icon sets, using TerraVision.
Analyze AWS resource health, diagnose issues from CloudWatch logs and metrics, and create a remediation plan for identified problems.
Install AWS Resource Health Diagnose
$ npx skills add github/awesome-copilot --skill aws-resource-health-diagnose -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install github/awesome-copilot aws-resource-health-diagnose --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/github/awesome-copilot.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/aws-resource-health-diagnose .claude/skills/aws-resource-health-diagnose && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "aws-resource-health-diagnose" agent skill from https://github.com/github/awesome-copilot/tree/main/skills/aws-resource-health-diagnose into .claude/skills/aws-resource-health-diagnose/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aws-resource-health-diagnose", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/github/awesome-copilot/tree/main/skills/aws-resource-health-diagnoseType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add github/awesome-copilot --skill aws-resource-health-diagnose -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install github/awesome-copilot aws-resource-health-diagnose --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/github/awesome-copilot.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/aws-resource-health-diagnose .agents/skills/aws-resource-health-diagnose && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "aws-resource-health-diagnose" agent skill from https://github.com/github/awesome-copilot/tree/main/skills/aws-resource-health-diagnose into .agents/skills/aws-resource-health-diagnose/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aws-resource-health-diagnose", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add github/awesome-copilot --skill aws-resource-health-diagnose -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install github/awesome-copilot aws-resource-health-diagnose --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/github/awesome-copilot.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/aws-resource-health-diagnose .cursor/skills/aws-resource-health-diagnose && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "aws-resource-health-diagnose" agent skill from https://github.com/github/awesome-copilot/tree/main/skills/aws-resource-health-diagnose into .cursor/skills/aws-resource-health-diagnose/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aws-resource-health-diagnose", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/github/awesome-copilot.git --path skills/aws-resource-health-diagnose--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add github/awesome-copilot --skill aws-resource-health-diagnose -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install github/awesome-copilot aws-resource-health-diagnose --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/github/awesome-copilot.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/aws-resource-health-diagnose .gemini/skills/aws-resource-health-diagnose && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "aws-resource-health-diagnose" agent skill from https://github.com/github/awesome-copilot/tree/main/skills/aws-resource-health-diagnose into .gemini/skills/aws-resource-health-diagnose/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aws-resource-health-diagnose", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install github/awesome-copilot aws-resource-health-diagnoseInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add github/awesome-copilot --skill aws-resource-health-diagnose -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/github/awesome-copilot.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/aws-resource-health-diagnose .github/skills/aws-resource-health-diagnose && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "aws-resource-health-diagnose" agent skill from https://github.com/github/awesome-copilot/tree/main/skills/aws-resource-health-diagnose into .github/skills/aws-resource-health-diagnose/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aws-resource-health-diagnose", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add github/awesome-copilot --skill aws-resource-health-diagnose -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install github/awesome-copilot aws-resource-health-diagnose --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/github/awesome-copilot.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/aws-resource-health-diagnose .opencode/skills/aws-resource-health-diagnose && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "aws-resource-health-diagnose" agent skill from https://github.com/github/awesome-copilot/tree/main/skills/aws-resource-health-diagnose into .opencode/skills/aws-resource-health-diagnose/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aws-resource-health-diagnose", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
aws-resource-health-diagnose- GitHub stars
- 40k
- Token cost
- ~1.7k tokens
- SKILL.md length
- 430 words
- Files
- 1
- Skills in repo
- 417
- Repo updated
- First seen
- Licence
- MIT
At a glance
Analyze AWS resource health, diagnose issues from CloudWatch logs and metrics, and create a remediation plan for identified problems.
- Works in 7 steps: Get AWS Diagnostic Best Practices → Resource Discovery & Identification → Health Status Assessment → …
- DevOps & Cloud work in your project
- SKILL.md covers Prerequisites, Workflow Steps, Error Handling and Success Criteria
- Calls aws; reaches docs.aws.amazon.com
What it does
AWS Resource Health Diagnose is an agent skill from github/awesome-copilot, published by the product's own GitHub organization. Analyze AWS resource health, diagnose issues from CloudWatch logs and metrics, and create a remediation plan for identified problems.
Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in DevOps & Cloud. It works with Amazon Web Services. The repository describes itself as: Community-contributed instructions, agents, skills, and configurations to help you make the most of GitHub Copilot. The licence is MIT.
When your agent uses it
- DevOps & Cloud work in your project
Example prompts
- “/aws-resource-health-diagnose”
Workflow steps
7 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 7cce7cf. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
aws
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
docs.aws.amazon.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
AWS Resource Health Diagnose loads about 1.7k tokens when it runs. Until then it costs about 41 tokens; SKILL.md has 430 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from github/awesome-copilot at commit 7cce7cf, republished under its MIT licence (© github). 430 words, ~1,743 tokens.
Download SKILL.mdSave it as
.claude/skills/aws-resource-health-diagnose/SKILL.md (or your agent's skills folder).- name
- aws-resource-health-diagnose
- description
- Analyze AWS resource health, diagnose issues from CloudWatch logs and metrics, and create a remediation plan for identified problems.
AWS Resource Health & Issue Diagnosis
This workflow analyzes a specific AWS resource to assess its health status, diagnose potential issues using CloudWatch logs and metrics, and develop a comprehensive remediation plan for any problems discovered.
Prerequisites
- AWS CLI configured and authenticated
- Target AWS resource identified (name, type, and optionally region/account)
- CloudWatch logging and metrics enabled on the target resource
Workflow Steps
Step 1: Get AWS Diagnostic Best Practices
Fetch https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/ for monitoring and troubleshooting guidance to inform the diagnostic approach.
Step 2: Resource Discovery & Identification
Locate the target resource using the appropriate AWS CLI command for its type:
# EC2
aws ec2 describe-instances --filters "Name=tag:Name,Values=<name>"
# Lambda
aws lambda get-function --function-name <name>
# RDS
aws rds describe-db-instances --db-instance-identifier <name>
# ECS
aws ecs describe-services --cluster <cluster> --services <name>
# ALB
aws elbv2 describe-load-balancers --names <name>
# DynamoDB
aws dynamodb describe-table --table-name <name>
# SQS
aws sqs get-queue-attributes --queue-url <url> --attribute-names All
# API Gateway
aws apigatewayv2 get-apisIf multiple matches are found, prompt the user to specify region/account.
Step 3: Health Status Assessment
Run service-specific health checks:
# EC2
aws ec2 describe-instance-status --instance-ids <id>
# RDS
aws rds describe-db-instances --db-instance-identifier <name> \
--query 'DBInstances[0].DBInstanceStatus'
# Lambda - error rate over 24h
aws cloudwatch get-metric-statistics --namespace AWS/Lambda \
--metric-name Errors --dimensions Name=FunctionName,Value=<name> \
--start-time $(date -u -d '24 hours ago' +%Y-%m-%dT%H:%M:%SZ) \
--end-time $(date -u +%Y-%m-%dT%H:%M:%SZ) \
--period 3600 --statistics Sum
# ECS
aws ecs describe-services --cluster <cluster> --services <name> \
--query 'services[0].[status,runningCount,desiredCount,pendingCount]'Key health indicators by service type:
- Lambda: Error rate, throttle rate, duration P99, concurrent executions
- RDS: CPU utilization, FreeStorageSpace, DatabaseConnections, ReadLatency/WriteLatency
- ECS: Running vs desired task count, task stop reason
- ALB: TargetResponseTime, HTTPCode_ELB_5XX_Count, UnHealthyHostCount
- SQS: ApproximateNumberOfMessagesNotVisible, ApproximateAgeOfOldestMessage
- DynamoDB: ConsumedReadCapacityUnits, ThrottledRequests, SuccessfulRequestLatency
Step 4: Log & Metrics Analysis
Find log groups and run CloudWatch Logs Insights queries:
# Find log groups
aws logs describe-log-groups --log-group-name-prefix /aws/<service>/<name>
# Start a query (last 24h errors)
aws logs start-query \
--log-group-name /aws/lambda/<name> \
--start-time $(date -u -d '24 hours ago' +%s) \
--end-time $(date -u +%s) \
--query-string 'filter @message like /ERROR/ | stats count(*) as errorCount by bin(1h)'
# Get results
aws logs get-query-results --query-id <id>
# Lambda cold starts
aws logs start-query \
--log-group-name /aws/lambda/<name> \
--start-time $(date -u -d '24 hours ago' +%s) \
--end-time $(date -u +%s) \
--query-string 'filter @type = "REPORT" | filter @initDuration > 0 | stats count() as coldStarts by bin(1h)'
# RDS Performance Insights (if enabled)
aws pi get-resource-metrics \
--service-type RDS --identifier db:<identifier> \
--metric-queries '[{"Metric":"db.load.avg"}]' \
--start-time $(date -u -d '24 hours ago' +%Y-%m-%dT%H:%M:%SZ) \
--end-time $(date -u +%Y-%m-%dT%H:%M:%SZ) \
--period-in-seconds 3600Identify: recurring error patterns, correlation with deployments (CloudTrail), performance trends, dependency failures.
Step 5: Issue Classification & Root Cause Analysis
Severity:
- Critical: Service unavailable, data loss, security incidents
- High: Performance degradation, error rates >5%, intermittent failures
- Medium: Warnings, suboptimal configuration, minor performance issues
- Low: Informational alerts, optimization opportunities
Root Cause Categories:
- Configuration Issues: wrong settings, missing env vars, IAM permission denials
- Resource Constraints: CPU/memory/disk limits, Lambda throttling, RDS connection exhaustion
- Network Issues: security group rules, VPC routing, DNS, NACLs
- Application Issues: code bugs, memory leaks, unhandled exceptions, slow queries
- Dependency Issues: downstream timeouts, SQS/SNS failures, external API limits
- Security Issues: KMS key issues, certificate expiration
Show full SKILL.md (153 more words)Show less
Step 6: Generate Remediation Plan
Immediate Actions (Critical):
# Lambda throttling — increase reserved concurrency
aws lambda put-reserved-concurrency \
--function-name <name> --reserved-concurrent-executions 100
# RDS connection exhaustion — reboot to reset connections
aws rds reboot-db-instance --db-instance-identifier <name>Short-term Fixes (High/Medium): Configuration adjustments, right-sizing, CloudWatch alarm improvements, IAM corrections.
Long-term Improvements: Architectural changes for resilience, preventive monitoring, enable AWS Health Dashboard notifications via EventBridge.
Step 7: Report & User Confirmation
Present findings:
🏥 AWS Resource Health Assessment
📊 Resource Overview:
• Resource: [Name] ([Type])
• Status: [Healthy/Warning/Critical]
• Region: [Region] | Account: [Account ID]
🚨 Issues Identified:
• Critical: X | High: Y | Medium: Z | Low: N
🔍 Top Issues:
1. [Issue]: [Description] — Impact: [High/Medium/Low]
2. [Issue]: [Description] — Impact: [High/Medium/Low]
🛠️ Remediation: X immediate, Y short-term, Z long-term actions
❓ Proceed with detailed remediation plan? (y/n)Then generate a full markdown report covering: health metrics, issues with root cause analysis, phased remediation steps with AWS CLI commands, CloudWatch alarm recommendations, and validation checklist.
Error Handling
- Resource Not Found: Ask user to clarify name/region
- Authentication Issues: Guide through
aws configure - Insufficient Permissions: List required IAM actions (
logs:*,cloudwatch:*,pi:*) - No Logs Available: Suggest enabling CloudWatch logging for the resource type
- Query Timeouts: Use shorter time windows
Success Criteria
- ✅ Resource health accurately assessed across all key metrics
- ✅ All significant issues identified and classified by severity
- ✅ Root cause analysis completed for major problems
- ✅ Actionable remediation plan with AWS CLI commands
- ✅ CloudWatch monitoring recommendations included
- ✅ Implementation steps include validation and rollback procedures
© github, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in skills/aws-resource-health-diagnose of github/awesome-copilot.
Open the folder on GitHubat commit 7cce7cf
Compare with similar skills
AWS Resource Health Diagnose next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| AWS Resource Health Diagnose this skillgithub/awesome-copilot | 40k | — | ~1.7k | Automated safety check: Pass | MIT | |
| Terravision Cloud Diagramspatrickchugh/terravision | 1.6k | — | ~5.6k | Automated safety check: Notes | AGPL-3.0-only | |
| Provider Bug Reviewmondoohq/mql | 411 | — | ~2.9k | Automated safety check: Pass | Custom licence | |
| Update Provider Depsmondoohq/mql | 411 | — | ~4.5k | Automated safety check: Pass | Custom licence | |
| AWS Architecture Diagramvidanov/aws-architecture-diagram-skill | 159 | — | ~4.9k | Automated safety check: Pass | MIT | |
| Nx Plugin For AWSawslabs/nx-plugin-for-aws | 151 | — | ~3.6k | Automated safety check: Pass | Apache-2.0 |
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Related
Works with
Categories
Questions about AWS Resource Health Diagnose
What does AWS Resource Health Diagnose do?
Analyze AWS resource health, diagnose issues from CloudWatch logs and metrics, and create a remediation plan for identified problems. AWS Resource Health Diagnose is an agent skill from github/awesome-copilot, published by the product's own GitHub organization. Analyze AWS resource health, diagnose issues from CloudWatch logs and metrics, and create a remediation plan for identified problems.
When should I use AWS Resource Health Diagnose?
AWS Resource Health Diagnose fits situations like: devOps & Cloud work in your project.
How do I install AWS Resource Health Diagnose in Claude Code?
Run `npx skills add github/awesome-copilot --skill aws-resource-health-diagnose -a claude-code`. Or copy the skill folder (skills/aws-resource-health-diagnose in github/awesome-copilot) into .claude/skills/aws-resource-health-diagnose in your project. Claude Code loads it when a task matches its description.
How do I install AWS Resource Health Diagnose in Codex?
Run `npx skills add github/awesome-copilot --skill aws-resource-health-diagnose -a codex`. Or copy the skill folder (skills/aws-resource-health-diagnose in github/awesome-copilot) into .agents/skills/aws-resource-health-diagnose in your project. Codex loads it when a task matches its description.
Can I use AWS Resource Health Diagnose in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add github/awesome-copilot --skill aws-resource-health-diagnose -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/aws-resource-health-diagnose, .gemini/skills/aws-resource-health-diagnose, .github/skills/aws-resource-health-diagnose and .opencode/skills/aws-resource-health-diagnose in your project.
What does AWS Resource Health Diagnose need to run?
Going by SKILL.md and its folder, AWS Resource Health Diagnose needs the command-line tools its instructions call (aws).
Does AWS Resource Health Diagnose access the network?
SKILL.md names 1 domain. In commands or code: docs.aws.amazon.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.
Is AWS Resource Health Diagnose safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does AWS Resource Health Diagnose use?
AWS Resource Health Diagnose is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does AWS Resource Health Diagnose use?
About 1.7k tokens (SKILL.md is roughly 7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to AWS Resource Health Diagnose?
Skills that share tags, products or a category with AWS Resource Health Diagnose: Terravision Cloud Diagrams (patrickchugh/terravision, 1.6k stars), Provider Bug Review (mondoohq/mql, 411 stars), Update Provider Deps (mondoohq/mql, 411 stars) and AWS Architecture Diagram (vidanov/aws-architecture-diagram-skill, 159 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains AWS Resource Health Diagnose?
github (a GitHub organization, an official publisher) maintains it in github/awesome-copilot, which has 39,792 GitHub stars. The repository holds 417 skills in this directory. The repository was last updated on October 8, 2026.
Source: github/awesome-copilot on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.