Hyperpod Issue Report Agent Skill
Generate comprehensive issue reports from HyperPod clusters (EKS and Slurm) by collecting diagnostic logs and configurations for troubleshooting and AWS Support cases. Use when users need to collect diagnostics from HyperPod cluster nodes, generate issue reports for AWS Support, investigate node failures or performance problems, document cluster state, or create diagnostic snapshots. Triggers on requests involving issue reports, diagnostic collection, support case preparation, or cluster troubleshooting that requires gathering logs and system information from multiple nodes.
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/awslabs/agent-plugins --skill hyperpod-issue-reportThe instruction itself
8 sections, as written by the authorHyperPod Issue Report
Collect diagnostic logs from HyperPod cluster nodes via SSM, store results in S3. Supports both EKS and Slurm clusters with auto-detection. Uses the bundled scripts/hyperpod_issue_report.py for reliable parallel collection.
Prerequisites
- AWS CLI configured with permissions:
sagemaker:DescribeCluster,sagemaker:ListClusterNodes,ssm:StartSession,s3:PutObject,s3:GetObject,eks:DescribeCluster - Python 3.8+ and uv (see uv installation docs for install options)
- SSM Agent running on target nodes; node IAM roles need
s3:GetObject/s3:PutObjecton the report bucket - For EKS clusters: kubectl installed and configured (see Workflow step 2)
Workflow
1. Gather Information
Collect from the user:
- Cluster identifier (required): accepts cluster name or full cluster ARN (e.g.,
arn:aws:sagemaker:us-west-2:123456789012:cluster/abc123) - AWS region (required unless extractable from ARN)
- S3 path for report storage (required, e.g.
s3://bucket/prefix). If the user doesn't have a bucket, create one (e.g.,s3://hyperpod-diagnostics-<account-id>-<region>) - Issue description (optional)
- Target scope: all nodes, specific instance groups, or specific node IDs (optional)
- Additional commands to run on nodes (optional)
2. Verify Environment
aws sts get-caller-identity
aws sagemaker describe-cluster --cluster-name <name-or-arn> --region <region>
If the S3 bucket doesn't exist, create it:
aws s3 mb s3://<bucket-name> --region <region>
For EKS clusters (check Orchestrator.Eks in describe-cluster output):
- Ensure kubectl is installed (
which kubectl). If missing, install it for the current platform. - Configure kubeconfig using the EKS cluster name from the describe-cluster response:
aws eks update-kubeconfig --name <eks-cluster-name> --region <region>
3. Run the Collection Script
uv run scripts/hyperpod_issue_report.py \
--cluster <cluster-name-or-arn> \
--region <region> \
--s3-path s3://<bucket>[/prefix]
Use --help for all options including --instance-groups, --nodes, --command, --max-workers, and --debug. Note: --instance-groups and --nodes are mutually exclusive. Node identifiers accept instance IDs (i-*), EKS names (hyperpod-i-*), or Slurm names (ip-*).
4. Present Results
After collection, the script shows statistics and offers interactive download. Report the S3 location and offer to:
- Download the report locally
- Help analyze collected diagnostics (see references/collection-details.md for what's in each file)
- Prepare a summary for AWS Support
Troubleshooting
See references/troubleshooting.md for error handling, large cluster tuning, and known limitations.
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How to use it
Take awslabs/hyperpod-issue-report from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.