A professional-grade skill for executing prepared AWS Fault Injection Service experiments with integrated health monitoring and log analysis.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install aws-fis-experiment-execute
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install aws-fis-experiment-execute using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is AWS FIS Experiment Execute?
The AWS FIS Experiment Execute skill is a specialized automation tool designed for reliability engineers and DevOps professionals. It streamlines the execution of chaos engineering experiments by bridging the gap between infrastructure deployment and performance analysis. This skill ensures that your AWS environment is in a healthy baseline state before injecting faults, preventing misleading results from already-degraded infrastructure.
By leveraging Openclaw Skills, developers can automate the tedious aspects of chaos testing, including EKS application discovery, real-time log monitoring via kubectl, and the generation of detailed markdown reports. It acts as a safety-first execution engine that handles the complex lifecycle of an FIS experiment—from initial validation to post-experiment baseline collection.
AWS FIS Experiment Execute Use Cases
- Running pre-configured AWS FIS experiment templates against production-like environments.
- Automated health verification of AWS resources (RDS, EKS, MSK, etc.) before fault injection.
- Real-time application log monitoring and error detection during active chaos experiments.
- Generating standardized post-mortem reports to document system resilience and recovery times.
- Executing chaos experiments in CI/CD pipelines with non-interactive polling and safety aborts.
How AWS FIS Experiment Execute Works
- The skill resolves the experiment directory using a provided path or an FIS Template ID.
- It validates the environment by reading the experiment's README and checking the CloudFormation stack status.
- A pre-experiment health check is performed on all target resources to ensure they are in a healthy baseline state.
- It initializes the app-service-log-analysis sub-skill to discover EKS applications and begin real-time log collection.
- Upon explicit user confirmation, the AWS FIS experiment is triggered.
- The skill monitors the experiment progress, polling status every 30-60 seconds and capturing log insights.
- After completion, it waits for a 3-minute recovery baseline before stopping log collection.
- A comprehensive Markdown results report is generated, detailing timelines, service recovery, and log analysis.
AWS FIS Experiment Execute Setup
To use this Openclaw Skills component, ensure you have the AWS CLI and kubectl installed and configured. This skill requires the app-service-log-analysis sub-skill for full functionality.
# Install the required sub-skill
openclaw install app-service-log-analysis
# Ensure your AWS credentials have permissions for FIS, CFN, and CloudWatch
aws sts get-caller-identity
# Navigate to your experiment directory and execute
openclaw run aws-fis-experiment-execute
AWS FIS Experiment Execute Data Schema & Taxonomy
The skill organizes experiment data within the local experiment directory using a structured taxonomy:
| File/Directory | Purpose |
|---|---|
cfn-template.yaml |
The CloudFormation template used for the experiment infrastructure. |
README.md |
Contains experiment metadata, region info, and target resource lists. |
results-report.md |
The final generated report including timelines and log analysis. |
logs/ |
(Temporary) Directory containing raw background logs collected during the run. |
expected-behavior.md |
Reference file used to determine which services to monitor. |
AWS FIS Experiment Execute Advanced Features
- Multi-cluster EKS discovery with isolated kubeconfig management to prevent global config pollution.
- Automated 10-minute polling window for resource recovery in non-interactive/CI environments.
- Deep-scan dependency mapping to identify secondary applications affected by infrastructure faults.
- Real-time error and warning detection across distributed application logs during experiment execution.
- Post-experiment baseline window (3 minutes) to capture critical recovery and reconciliation log data.
SKILL.md
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