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resilience-hub-failure-mode-assessment

aws/agent-toolkit-for-aws

Run and interpret AWS Resilience Hub v2 failure mode assessments to identify and remediate architectural weaknesses.

What is resilience-hub-failure-mode-assessment?

This skill enables you to execute Resilience Hub v2 failure mode assessments, understand findings by severity and category, triage by achievability, and drive remediation. Use it when you need to run an assessment, review findings, understand failure modes, or resolve specific findings in your AWS architecture.

  • Start and run Resilience Hub v2 failure mode assessments
  • Interpret findings with severity levels, categories, and recommendations
  • Triage findings by achievability and priority
  • Manage AI-generated service functions and resource assignments
  • Resolve and remediate identified failure modes
  • Generate and secure assessment reports

How to install resilience-hub-failure-mode-assessment

npx skills add https://github.com/aws/agent-toolkit-for-aws --skill resilience-hub-failure-mode-assessment
Prerequisites
  • AWS Resilience Hub v2 service enabled in your AWS account
  • IAM permissions to describe resources in configured regions (least-privilege invoker role recommended)
  • AWS CLI or AWS MCP server for executing API calls
  • S3 bucket configured for assessment report output (with encryption and public-access blocking recommended)
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How to use resilience-hub-failure-mode-assessment

  1. 1.Follow the assessment workflow in references/assessment-workflow.md to set up your service and input sources
  2. 2.Start a failure mode assessment using the Resilience Hub API or CLI
  3. 3.Retrieve and review findings, sorted by severity (HIGH, MEDIUM, LOW)
  4. 4.Check achievability for each finding's policy component to determine if architecture changes are needed
  5. 5.Triage findings using the priority matrix: NOT_ACHIEVABLE findings require architecture changes; ACHIEVABLE findings can be validated with FIS experiments
  6. 6.Update service functions or resource assignments if AI-generated definitions are incorrect
  7. 7.Plan and execute remediation, then re-run assessments to validate fixes

Use cases

Good for
  • Run a failure mode assessment on a multi-region application and prioritize HIGH-severity findings for immediate remediation
  • Review assessment findings, filter by achievability, and plan sprint work for MEDIUM-severity issues
  • Update AI-generated service functions to correct resource assignments and re-run assessments
  • Validate architectural fixes using FIS experiments after addressing NOT_ACHIEVABLE findings
  • Generate encrypted assessment reports for compliance and architecture review
Who it's for
  • AWS Solutions Architects
  • DevOps and SRE engineers
  • Application resilience leads
  • Cloud infrastructure teams
  • AWS Well-Architected reviewers

resilience-hub-failure-mode-assessment FAQ

What's the difference between this skill and resilience-hub-getting-started?

resilience-hub-getting-started covers initial Resilience Hub setup and configuration. This skill applies when you're running assessments, reviewing findings, or remediating specific failure modes.

What does INVALID_PERMISSIONS error mean?

The invoker role or cross-account roles lack access to resources in the configured regions. Verify IAM permissions to describe resources in all regions where your service is deployed.

How should I prioritize findings when there are too many?

Start with HIGH-severity findings first. For each, check achievability: NOT_ACHIEVABLE means the architecture must change; ACHIEVABLE means validate the fix with an FIS experiment. Plan MEDIUM findings this sprint; track LOW findings but don't block on them.

Can I update AI-generated service functions?

Yes. Use `aws resiliencehubv2 update-service-function` to rename or change criticality, and `create-service-function-resources` to reassign resources. There is no service-function 'type' parameter.

How should I secure assessment reports?

Store reports in S3 buckets with server-side encryption (SSE-S3 or SSE-KMS) and block public access. If granting Resilience Hub service principal write access, scope the bucket policy with aws:SourceArn and aws:SourceAccount conditions to prevent confused-deputy writes.

Full instructions (SKILL.md)

Source of truth, from aws/agent-toolkit-for-aws.


name: resilience-hub-failure-mode-assessment description: > Runs and interprets AWS Resilience Hub v2 failure mode assessments. Covers starting assessments, understanding findings (severity, categories, recommendations), triaging by achievability, working with AI-generated service functions, and resolving findings. Applies when the user wants to run an assessment, review findings, or understand failure modes, or has a specific finding and asks how to resolve, remediate, or fix it. Does not apply to initial setup (use resilience-hub-getting-started) or FIS experiments. version: 1

Failure Mode Assessment

Overview

Domain expertise for running Resilience Hub v2 failure mode assessments, interpreting findings, triaging by severity and achievability, and driving remediation.

The AWS MCP server is recommended for executing this skill's AWS API calls, but it is not required — all operations also work with the AWS CLI directly.

Guardrail — where this skill's own files live (MCP vs local install)

Before reading a reference file, determine how this skill was loaded:

  • Loaded via the AWS MCP retrieve_skill tool: the skill's reference files are not on the local filesystem. Fetch each one through retrieve_skill with the file parameter (e.g. file="references/assessment-workflow.md") — do NOT file_read these paths locally or search the filesystem for them.
  • Installed locally (e.g. .kiro/skills/resilience-hub-failure-mode-assessment/ or ~/.claude/skills/resilience-hub-failure-mode-assessment/): read reference files from the local skill directory using the relative paths shown here.

This applies only to the skill's own reference files; always read and write user or session data in the working directory, never through retrieve_skill.

Run and interpret assessments

To run assessments and triage findings, follow the procedure exactly. See references/assessment-workflow.md.

Troubleshooting

Assessment fails with INVALID_PERMISSIONS

The service's permission model (invokerRoleName / crossAccountRoles) doesn't have access to the resources. Verify the invoker role (and any cross-account roles) can describe resources in all configured regions.

Too many findings — where to start?

Prioritize by finding severity, highest first (HIGH, then MEDIUM, then LOW). For HIGH-severity findings, check the service's achievability for the relevant policy component (from get-service / list-failure-mode-assessments): NOT_ACHIEVABLE means the architecture must change before testing; ACHIEVABLE means validate the fix with an FIS experiment. MEDIUM findings: plan remediation this sprint; LOW findings: track but don't block (see the priority matrix in references/assessment-workflow.md Step 5).

AI-generated service functions are wrong

Update them: aws resiliencehubv2 update-service-function to rename or change criticality (there is no service-function "type" parameter). Reassign resources by calling create-service-function-resources with the desired resource set (see references/assessment-workflow.md for the service-function operations).

Security Considerations

  • Least privilege: the invoker role should be scoped to read-only discovery of only the resource types in the service's input sources; avoid granting access beyond what assessment needs.
  • Encryption & access control: recommend that S3 buckets used for report output have server-side encryption (SSE-S3 or SSE-KMS) and block public access — assessment reports can contain sensitive architectural detail. If a bucket policy grants the Resilience Hub service principal write access, scope it with aws:SourceArn / aws:SourceAccount condition keys to prevent confused-deputy writes.
  • Further reading: see Security in AWS Resilience Hub and the AWS Well-Architected Security Pillar for securing assessment outputs and IAM configurations.