How do I install the Hyperpod Issue Report skill?

Run npx skillmds@latest add gabrielmoreira/hyperpod-issue-report in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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Is Hyperpod Issue Report free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Hyperpod Issue Report?

gabrielmoreira (@gabrielmoreira) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Hyperpod Issue Report by gabrielmoreira

Name: Hyperpod Issue Report
Rating: 4.5 (4 reviews)
Author: gabrielmoreira

Hyperpod Issue Report

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.

gabrielmoreira Updated Sep 9, 2026 17 repo stars

File contents

HyperPod 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:PutObject on 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)

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, --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.

---name: hyperpod-issue-reportdescription: 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.--- # HyperPod 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](https://docs.astral.sh/uv/) (see [uv installation docs](https://docs.astral.sh/uv/getting-started/installation/) for install options) - SSM Agent running on target nodes; node IAM roles need `s3:GetObject`/`s3:PutObject` on 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) ### 2. Verify Environment ```bash 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: ```bash aws s3 mb s3://<bucket-name> --region <region> ``` **For EKS clusters** (check `Orchestrator.Eks` in describe-cluster output): 1. Ensure kubectl is installed (`which kubectl`). If missing, install it for the current platform. 2. Configure kubeconfig using the EKS cluster name from the describe-cluster response: ```bash aws eks update-kubeconfig --name <eks-cluster-name> --region <region> ``` ### 3. Run the Collection Script ```bash 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`, `--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](references/collection-details.md) for what's in each file) - Prepare a summary for AWS Support ## Troubleshooting See [references/troubleshooting.md](references/troubleshooting.md) for error handling, large cluster tuning, and known limitations.

Frequently asked questions