Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add adriannoes/awesome-agentic-ai --skill performing-cloud-forensics-investigationgit clone --depth 1 https://github.com/adriannoes/awesome-agentic-aiWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/performing-cloud-forensics-investigation)<a href="https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/performing-cloud-forensics-investigation"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/performing-cloud-forensics-investigation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/performing-cloud-forensics-investigation"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/performing-cloud-forensics-investigation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00034 | $0.03479 |
| Opus 5 | $0.00017 | $0.01740 |
| Sonnet 5 | $0.00007 | $0.00696 |
| Haiku 4.5 | $0.00003 | $0.00348 |
Grade D, and why
performing-cloud-forensics-investigation scanned grade D with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
sudo mount -o ro /dev/xvdf1 /mnt/evidence Cloud metadata endpointhighServer-side request forgery
One request to 169.254.169.254 can return temporary IAM credentials.
Collect Kubernetes audit logs and cloud provider logs, analyze pod creation events for privilege escalation attempts, examine node-level logs for container escape evidence, check for unauthorized access to cloud metadata How it starts
The opening of the file, as written. The whole thing — 348 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performing Cloud Forensics Investigation
When to Use
- When investigating a security breach in AWS, Azure, or GCP cloud environments
- For collecting volatile and non-volatile evidence from cloud infrastructure
- When tracing unauthorized access through cloud service API logs
- During incident response requiring preservation of cloud-based evidence
- For analyzing compromised virtual machines, containers, or serverless functions
Prerequisites
- Administrative access to the cloud account under investigation
- AWS CLI, Azure CLI, or gcloud CLI configured with appropriate permissions
- Understanding of cloud-native logging (CloudTrail, Activity Log, Audit Log)
- Forensic workstation with cloud SDKs installed
- Knowledge of IAM, networking, and compute services in target cloud
- Evidence preservation procedures for cloud environments
Workflow
Step 1: Preserve Cloud Evidence and Establish Scope
# === AWS Evidence Preservation ===
# Snapshot compromised EC2 instance volumes
INSTANCE_ID="i-0abc123def456789"
VOLUME_IDS=$(aws ec2 describe-instances --instance-ids $INSTANCE_ID \
--query 'Reservations[].Instances[].BlockDeviceMappings[].Ebs.VolumeId' --output text)
for vol in $VOLUME_IDS; do
aws ec2 create-snapshot --volume-id $vol \
--description "Forensic snapshot - Case 2024-001 - $(date -u)" \
--tag-specifications "ResourceType=snapshot,Tags=[{Key=Case,Value=2024-001},{Key=Evidence,Value=true}]"
done
# Capture instance metadata
aws ec2 describe-instances --instance-ids $INSTANCE_ID \
> /cases/case-2024-001/cloud/instance_metadata.json
# Capture security group rules
aws ec2 describe-security-groups --group-ids $(aws ec2 describe-instances \
--instance-ids $INSTANCE_ID --query 'Reservations[].Instances[].SecurityGroups[].GroupId' --output text) \
> /cases/case-2024-001/cloud/security_groups.json
# Capture network interfaces
aws ec2 describe-network-interfaces --filters "Name=attachment.instance-id,Values=$INSTANCE_ID" \
> /cases/case-2024-001/cloud/network_interfaces.json
# Isolate the instance (replace security group with forensic isolation SG)
aws ec2 modify-instance-attribute --instance-id $INSTANCE_ID \
--groups sg-forensic-isolation
# === Azure Evidence Preservation ===
# Snapshot a compromised VM disk
az snapshot create --resource-group forensics-rg \
--name "case-2024-001-osdisk-snapshot" \
--source "/subscriptions/SUB_ID/resourceGroups/RG/providers/Microsoft.Compute/disks/vm-osdisk"
# === GCP Evidence Preservation ===
gcloud compute disks snapshot compromised-disk \
--snapshot-names="case-2024-001-forensic" \
--zone=us-central1-a
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 348 lines · 34 tokens per session scan D dfb35d2544c7
performing-cloud-forensics-investigation is a skill published in the GitHub repository adriannoes/awesome-agentic-ai (57 stars, last pushed 13d ago), licensed MIT. It adds 34 tokens to every session and 3,479 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it D with 2 findings (asks for root, cloud metadata endpoint). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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