Agent skill

Performing Cloud Forensics Investigation

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Collect and analyze cloud forensic evidence using AWS CLI, Azure CLI, or gcloud to snapshot volumes, capture instance metadata and security group configurations, and preserve cloud-native logs…

Apache-2.0Auto-check: notesBackend & APIs

Install Performing Cloud Forensics Investigation

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-cloud-forensics-investigation -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills performing-cloud-forensics-investigation --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/performing-cloud-forensics-investigation .claude/skills/performing-cloud-forensics-investigation && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
performing-cloud-forensics-investigation
GitHub stars
34k
Token cost
~3.4k tokens
SKILL.md length
486 words
Files
4 (incl. scripts, references)
Skills in repo
644
Repo updated
First seen
Licence
Apache-2.0

At a glance

Collect and analyze cloud forensic evidence using AWS CLI, Azure CLI, or gcloud to snapshot volumes, capture instance metadata and security group configurations, and preserve cloud-native logs…

  • Works in 5 steps: Preserve Cloud Evidence and Establish… → Collect Cloud API and Access Logs → Analyze IAM and Access Patterns → …
  • Investigating a suspected breach in AWS
  • SKILL.md covers When to Use, Prerequisites, Workflow and Key Concepts, plus 3 more sections
  • Runs Python scripts from its folder; calls aws, az and gcloud

What it does

Performing Cloud Forensics Investigation is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Collect and analyze cloud forensic evidence using AWS CLI, Azure CLI, or gcloud to snapshot volumes, capture instance metadata and security group configurations, and preserve cloud-native logs (CloudTrail, Activity Log, Audit Log). Use when investigating a suspected breach in AWS, Azure, or GCP, tracing unauthorized access through API logs, or analyzing a compromised VM, container, or serverless function.

Its SKILL.md is about 3.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `references/api-reference.md` and `scripts/agent.py`).

It sits in Backend & APIs, covering Serverless and Digital forensics. It works with Amazon Web Services, Google Cloud and Microsoft Azure. The repository describes itself as: 817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io…. The licence is Apache-2.0.

When your agent uses it

  • Investigating a suspected breach in AWS
  • Tracing unauthorized access through API logs
  • Analyzing a compromised VM
  • Serverless function

Example prompts

  • “/performing-cloud-forensics-investigation”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Preserve Cloud Evidence and Establish Scope
  2. Collect Cloud API and Access Logs
  3. Analyze IAM and Access Patterns
  4. Acquire and Analyze VM Disk Image
  5. Generate Cloud Forensics Report

What it can do on your machine

Read from SKILL.md and the folder at commit 54a7988. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • aws
    • az
    • gcloud
    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use aws, az and gcloud, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Performing Cloud Forensics Investigation loads about 3.4k tokens when it runs, and up to ~4.1k if it reads all its reference files. Until then it costs about 112 tokens; SKILL.md has 486 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~112
When it runs · the whole SKILL.md, loaded when a task matches
~3.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteRuns commands with sudoSKILL.md:214
    sudo mount -o ro /dev/xvdf1 /mnt/evidence

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from mukul975/Anthropic-Cybersecurity-Skills at commit 54a7988, republished under its Apache-2.0 licence (© mukul975). 486 words, ~3,438 tokens.

Download SKILL.mdSave it as .claude/skills/performing-cloud-forensics-investigation/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
performing-cloud-forensics-investigation
description
Collect and analyze cloud forensic evidence using AWS CLI, Azure CLI, or gcloud to snapshot volumes, capture instance metadata and security group configurations, and preserve cloud-native logs (CloudTrail, Activity Log, Audit Log). Use when investigating a suspected breach in AWS, Azure, or GCP, tracing unauthorized access through API logs, or analyzing a compromised VM, container, or serverless function.
domain
cybersecurity
subdomain
digital-forensics
tags
forensics, cloud-forensics, aws, azure, gcp, incident-response, log-analysis
version
1.0
author
mahipal
license
Apache-2.0
nist_csf
RS.AN-03, DE.AE-02, RS.MA-01
mitre_attack
T1005, T1074, T1119, T1070, T1078.004

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
bash
# === 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
Step 2: Collect Cloud API and Access Logs
bash
# === AWS CloudTrail Logs ===
# Download CloudTrail events for the investigation period
aws cloudtrail lookup-events \
   --start-time "2024-01-15T00:00:00Z" \
   --end-time "2024-01-20T23:59:59Z" \
   --max-results 1000 \
   > /cases/case-2024-001/cloud/cloudtrail_events.json

# Filter for specific user activity
aws cloudtrail lookup-events \
   --lookup-attributes AttributeKey=Username,AttributeValue=compromised-user \
   --start-time "2024-01-15T00:00:00Z" \
   > /cases/case-2024-001/cloud/user_activity.json

# Download S3 access logs
aws s3 sync s3://my-cloudtrail-bucket/AWSLogs/ /cases/case-2024-001/cloud/cloudtrail_s3/

# Query CloudTrail with Athena for large-scale analysis
aws athena start-query-execution \
   --query-string "SELECT eventTime, eventName, userIdentity.arn, sourceIPAddress, errorCode
                   FROM cloudtrail_logs
                   WHERE eventTime BETWEEN '2024-01-15' AND '2024-01-20'
                   AND sourceIPAddress NOT IN ('10.0.0.0/8')
                   ORDER BY eventTime" \
   --result-configuration OutputLocation=s3://forensics-bucket/athena-results/

# === AWS VPC Flow Logs ===
aws logs filter-log-events \
   --log-group-name "vpc-flow-logs" \
   --start-time $(date -d "2024-01-15" +%s000) \
   --end-time $(date -d "2024-01-20" +%s000) \
   --filter-pattern "ACCEPT" \
   > /cases/case-2024-001/cloud/vpc_flow_logs.json

# === Azure Activity Log ===
az monitor activity-log list \
   --start-time "2024-01-15T00:00:00Z" \
   --end-time "2024-01-20T23:59:59Z" \
   --output json > /cases/case-2024-001/cloud/azure_activity.json

# === GCP Audit Logs ===
gcloud logging read 'logName="projects/PROJECT_ID/logs/cloudaudit.googleapis.com%2Factivity"
   AND timestamp>="2024-01-15T00:00:00Z"
   AND timestamp<="2024-01-20T23:59:59Z"' \
   --format=json > /cases/case-2024-001/cloud/gcp_audit.json
Step 3: Analyze IAM and Access Patterns
bash
# Analyze compromised credentials usage
python3 << 'PYEOF'
import json
from collections import defaultdict

with open('/cases/case-2024-001/cloud/cloudtrail_events.json') as f:
    data = json.load(f)

# Analyze by source IP
ip_events = defaultdict(list)
error_events = []
critical_actions = []

for event in data.get('Events', []):
    ct = json.loads(event.get('CloudTrailEvent', '{}'))
    source_ip = ct.get('sourceIPAddress', 'Unknown')
    event_name = ct.get('eventName', 'Unknown')
    user_arn = ct.get('userIdentity', {}).get('arn', 'Unknown')
    error = ct.get('errorCode')
    timestamp = ct.get('eventTime', '')

    ip_events[source_ip].append(event_name)

    if error:
        error_events.append({'time': timestamp, 'action': event_name, 'error': error, 'ip': source_ip})

    # Flag critical actions
    critical = ['CreateUser', 'CreateAccessKey', 'AttachUserPolicy', 'CreateRole',
                'PutBucketPolicy', 'StopLogging', 'DeleteTrail', 'CreateKeyPair',
                'RunInstances', 'AuthorizeSecurityGroupIngress']
    if event_name in critical:
        critical_actions.append({'time': timestamp, 'action': event_name, 'user': user_arn, 'ip': source_ip})

print("=== SOURCE IP ANALYSIS ===")
for ip, events in sorted(ip_events.items(), key=lambda x: len(x[1]), reverse=True):
    print(f"  {ip}: {len(events)} events ({len(set(events))} unique actions)")

print(f"\n=== ACCESS ERRORS ({len(error_events)} total) ===")
for e in error_events[:10]:
    print(f"  [{e['time']}] {e['action']} -> {e['error']} from {e['ip']}")

print(f"\n=== CRITICAL ACTIONS ({len(critical_actions)} total) ===")
for a in critical_actions:
    print(f"  [{a['time']}] {a['action']} by {a['user']} from {a['ip']}")
PYEOF
Step 4: Acquire and Analyze VM Disk Image
bash
# Create a forensic analysis instance from the snapshot
SNAPSHOT_ID="snap-0abc123def456789"

# Create volume from snapshot in isolated forensic VPC
FORENSIC_VOL=$(aws ec2 create-volume --snapshot-id $SNAPSHOT_ID \
   --availability-zone us-east-1a \
   --tag-specifications "ResourceType=volume,Tags=[{Key=Case,Value=2024-001}]" \
   --query 'VolumeId' --output text)

# Attach to forensic analysis instance (read-only mount)
aws ec2 attach-volume --volume-id $FORENSIC_VOL \
   --instance-id i-forensic-workstation \
   --device /dev/xvdf

# On the forensic instance, mount read-only
sudo mount -o ro /dev/xvdf1 /mnt/evidence

# Perform standard disk forensics on the mounted volume
# Extract logs, analyze file system, check for persistence
ls /mnt/evidence/var/log/
cp -r /mnt/evidence/var/log/ /cases/case-2024-001/cloud/vm_logs/
cp -r /mnt/evidence/etc/crontab /cases/case-2024-001/cloud/persistence/
cp -r /mnt/evidence/home/*/.ssh/ /cases/case-2024-001/cloud/ssh_keys/
cp -r /mnt/evidence/home/*/.bash_history /cases/case-2024-001/cloud/bash_history/
Step 5: Generate Cloud Forensics Report
bash
# Compile findings into structured report
python3 << 'PYEOF'
report = """
CLOUD FORENSICS INVESTIGATION REPORT
======================================
Case: 2024-001
Cloud Provider: AWS (Account: 123456789012)
Region: us-east-1
Investigation Period: 2024-01-15 to 2024-01-20

EVIDENCE PRESERVED:
- EC2 Instance Snapshot: snap-0abc123def456789 (i-0abc123def456789)
- CloudTrail Logs: 2024-01-15 to 2024-01-20
- VPC Flow Logs: 2024-01-15 to 2024-01-20
- Instance Metadata: captured and hashed
- Security Group Configuration: captured at time of isolation

FINDINGS:
1. Initial Access:
   - Compromised IAM access key AKIA... used from IP 203.0.113.45
   - First unauthorized API call: 2024-01-15 14:32:00 UTC
   - IP geolocation: Foreign jurisdiction (not company IP range)

2. Persistence:
   - New IAM user 'backup-admin' created with AdministratorAccess
   - New access key pair generated for backup-admin
   - SSH key added to EC2 instance authorized_keys

3. Lateral Movement:
   - S3 bucket policies modified to allow public access
   - Security group rules modified to allow SSH from 0.0.0.0/0
   - 3 additional EC2 instances launched for crypto-mining

4. Data Exfiltration:
   - S3 bucket 'company-confidential' accessed 234 times
   - 12 GB of data downloaded via GetObject API calls
   - Data transferred to external IP 185.x.x.x

5. Anti-Forensics:
   - CloudTrail logging disabled at 2024-01-18 03:00 UTC
   - CloudWatch log groups deleted

RECOMMENDATIONS:
- Rotate all IAM credentials immediately
- Enable MFA on all accounts
- Restore CloudTrail logging
- Review and restrict S3 bucket policies
- Implement GuardDuty for continuous monitoring
"""

with open('/cases/case-2024-001/cloud/cloud_forensics_report.txt', 'w') as f:
    f.write(report)
print(report)
PYEOF

Key Concepts

ConceptDescription
Cloud API loggingService logs recording all API calls (CloudTrail, Activity Log, Audit Log)
Volume snapshotsPoint-in-time copies of cloud disk volumes for forensic preservation
VPC Flow LogsNetwork traffic metadata logs showing source, destination, and action
IAM credential compromiseUnauthorized use of access keys, tokens, or assumed roles
Instance metadataEC2/VM configuration data including network, storage, and security settings
Shared responsibilityCloud provider secures infrastructure; customer secures data and access
Evidence volatilityCloud resources can be terminated; evidence must be preserved quickly
Multi-region artifactsAttacks may span regions requiring cross-region log collection

Tools & Systems

ToolPurpose
AWS CLICommand-line interface for AWS service interaction and log collection
CloudTrailAWS API call logging service for investigation and auditing
Azure MonitorAzure logging and diagnostics platform
GCP Cloud LoggingGoogle Cloud audit and access logging service
AthenaAWS serverless SQL query service for analyzing CloudTrail logs at scale
ProwlerOpen-source AWS security assessment and forensic collection tool
ScoutSuiteMulti-cloud security auditing tool
CADO ResponseCloud-native digital forensics and incident response platform
Show full SKILL.md (169 more words)Show less

Common Scenarios

Scenario 1: Compromised IAM Access Keys Identify the compromised key in CloudTrail, trace all API calls made with the key, determine the source IPs and actions taken, check for persistence mechanisms (new users, roles, keys), revoke the compromised credentials, assess data access scope.

Scenario 2: Cryptojacking on EC2 Instances Detect unauthorized instance launches in CloudTrail, snapshot the mining instances for analysis, examine security group changes that allowed C2 communication, identify the initial access vector (stolen keys, SSRF), calculate resource costs incurred.

Scenario 3: S3 Data Breach Analyze S3 access logs and CloudTrail for GetObject/PutBucketPolicy events, identify who modified bucket policies to allow public access, determine the scope of data exposure, check for data downloads from unauthorized IPs, assess regulatory reporting requirements.

Scenario 4: Container Escape in EKS/AKS/GKE 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 service (169.254.169.254), trace lateral movement to cloud APIs.

Output Format

Cloud Forensics Summary:
  Cloud: AWS (us-east-1) Account: 123456789012
  Investigation: 2024-01-15 to 2024-01-20
  Incident Type: IAM Credential Compromise + Data Exfiltration

  Evidence Collected:
    EBS Snapshots:    3 volumes preserved
    CloudTrail Events: 12,456 (1,234 from attacker IP)
    VPC Flow Logs:    45,678 records
    S3 Access Logs:   2,345 entries

  Attack Timeline:
    2024-01-15 14:32 - Compromised access key first used from 203.0.113.45
    2024-01-15 14:45 - New IAM user created with admin privileges
    2024-01-16 02:00 - S3 bucket policy modified (public access enabled)
    2024-01-16 03:00 - 12 GB downloaded from company-confidential bucket
    2024-01-18 03:00 - CloudTrail logging disabled

  Impact Assessment:
    Data Exposed: 12 GB from 3 S3 buckets
    Resources Created: 3 EC2 instances (crypto mining)
    Estimated Cost: $4,500 in unauthorized compute

© mukul975, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (scripts, references) in skills/performing-cloud-forensics-investigation of mukul975/Anthropic-Cybersecurity-Skills.

  • SKILL.md
  • LICENSE
  • references/api-reference.md
  • scripts/agent.py

Open the folder on GitHubat commit 54a7988

Compare with similar skills

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Investigating GCP Incidentstrilwu/secskills157—~2.2kAutomated safety check: PassMIT
Cloud AuditCommonHuman-Lab/nyxstrike157—~1.1kAutomated safety check: PassCustom licence
Cloud Auditbriiirussell/cybersecurity-skills413—~1.3kAutomated safety check: NotesMIT
Provider API Call Dedupmondoohq/mql412—~7.7kAutomated safety check: PassCustom licence

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Questions about Performing Cloud Forensics Investigation

What does Performing Cloud Forensics Investigation do?

Collect and analyze cloud forensic evidence using AWS CLI, Azure CLI, or gcloud to snapshot volumes, capture instance metadata and security group configurations, and preserve cloud-native logs…. Performing Cloud Forensics Investigation is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Collect and analyze cloud forensic evidence using AWS CLI, Azure CLI, or gcloud to snapshot volumes, capture instance metadata and security group configurations, and preserve cloud-native logs (CloudTrail, Activity Log, Audit Log).

When should I use Performing Cloud Forensics Investigation?

Performing Cloud Forensics Investigation fits situations like: investigating a suspected breach in AWS; tracing unauthorized access through API logs; analyzing a compromised VM; serverless function.

How do I install Performing Cloud Forensics Investigation in Claude Code?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-cloud-forensics-investigation -a claude-code`. Or copy the skill folder (skills/performing-cloud-forensics-investigation in mukul975/Anthropic-Cybersecurity-Skills) into .claude/skills/performing-cloud-forensics-investigation in your project. Claude Code loads it when a task matches its description.

How do I install Performing Cloud Forensics Investigation in Codex?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-cloud-forensics-investigation -a codex`. Or copy the skill folder (skills/performing-cloud-forensics-investigation in mukul975/Anthropic-Cybersecurity-Skills) into .agents/skills/performing-cloud-forensics-investigation in your project. Codex loads it when a task matches its description.

Can I use Performing Cloud Forensics Investigation in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-cloud-forensics-investigation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/performing-cloud-forensics-investigation, .gemini/skills/performing-cloud-forensics-investigation, .github/skills/performing-cloud-forensics-investigation and .opencode/skills/performing-cloud-forensics-investigation in your project.

What does Performing Cloud Forensics Investigation need to run?

Going by SKILL.md and its folder, Performing Cloud Forensics Investigation needs Python for the scripts in its folder and the command-line tools its instructions call (aws, az, gcloud and python3). Our summary lists: Python 3.

Does Performing Cloud Forensics Investigation access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Performing Cloud Forensics Investigation safe to install?

Our automated static check of SKILL.md found notes only (runs commands with sudo), nothing it rates as a warning. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Performing Cloud Forensics Investigation use?

Performing Cloud Forensics Investigation is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Performing Cloud Forensics Investigation use?

About 3.4k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 621 tokens, read only when the agent opens those files.

What are the alternatives to Performing Cloud Forensics Investigation?

Skills that share tags, products or a category with Performing Cloud Forensics Investigation: Ak Cloud Deploy (yaalalabs/agent-kernel, 192 stars), Investigating GCP Incidents (trilwu/secskills, 157 stars), Cloud Audit (CommonHuman-Lab/nyxstrike, 157 stars) and Cloud Audit (briiirussell/cybersecurity-skills, 413 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Performing Cloud Forensics Investigation?

mukul975 (a GitHub user) maintains it in mukul975/Anthropic-Cybersecurity-Skills, which has 33,993 GitHub stars. The repository holds 644 skills in this directory. The repository was last updated on August 31, 2026.

Source: mukul975/Anthropic-Cybersecurity-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.