Agent skill

Detecting Compromised Cloud Credentials

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Detect compromised cloud credentials across AWS, Azure, and GCP by analyzing anomalous API activity, impossible-travel patterns, and credential-stuffing indicators using GuardDuty, Microsoft…

Apache-2.0Auto-check passedDevOps & Cloud

Install Detecting Compromised Cloud Credentials

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-compromised-cloud-credentials -a claude-code

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

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills detecting-compromised-cloud-credentials --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/detecting-compromised-cloud-credentials .claude/skills/detecting-compromised-cloud-credentials && 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
detecting-compromised-cloud-credentials
GitHub stars
34k
Token cost
~3.9k tokens
SKILL.md length
628 words
Files
4 (incl. scripts, references)
Skills in repo
637
Repo updated
First seen
Licence
Apache-2.0

At a glance

Detect compromised cloud credentials across AWS, Azure, and GCP by analyzing anomalous API activity, impossible-travel patterns, and credential-stuffing indicators using GuardDuty, Microsoft…

  • Works in 5 steps: Detect Credential Compromise Indicators… → Detect Credential Abuse in Azure → Detect Credential Abuse in GCP → …
  • Investigating alerts about cloud API activity from unfamiliar locations
  • 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; reaches graph.microsoft.com

What it does

Detecting Compromised Cloud Credentials is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Detect compromised cloud credentials across AWS, Azure, and GCP by analyzing anomalous API activity, impossible-travel patterns, and credential-stuffing indicators using GuardDuty, Microsoft Defender for Identity, and Google SCC Event Threat Detection. Use when investigating alerts about cloud API activity from unfamiliar locations, responding to an exposed-credential notification, or scoping a credential compromise.

Its SKILL.md is about 3.9k 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 DevOps & Cloud, covering Secrets management. It works with Google Cloud, Microsoft Azure, Amazon Web Services and Microsoft Defender. 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 alerts about cloud API activity from unfamiliar locations
  • Responding to an exposed-credential notification
  • Scoping a credential compromise

Example prompts

  • “/detecting-compromised-cloud-credentials”

Requirements

  • Python 3

Workflow steps

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

  1. Detect Credential Compromise Indicators in AWS
  2. Detect Credential Abuse in Azure
  3. Detect Credential Abuse in GCP
  4. Build Cross-Cloud Correlation Rules
  5. Respond to Confirmed Credential Compromise

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

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

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • graph.microsoft.com

    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

Detecting Compromised Cloud Credentials loads about 3.9k tokens when it runs, and up to ~4.5k if it reads all its reference files. Until then it costs about 115 tokens; SKILL.md has 628 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~115
When it runs · the whole SKILL.md, loaded when a task matches
~3.9k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4.5k

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 passed

The automated check found no risky patterns in SKILL.md.

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). 628 words, ~3,888 tokens.

Download SKILL.mdSave it as .claude/skills/detecting-compromised-cloud-credentials/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
detecting-compromised-cloud-credentials
description
Detect compromised cloud credentials across AWS, Azure, and GCP by analyzing anomalous API activity, impossible-travel patterns, and credential-stuffing indicators using GuardDuty, Microsoft Defender for Identity, and Google SCC Event Threat Detection. Use when investigating alerts about cloud API activity from unfamiliar locations, responding to an exposed-credential notification, or scoping a credential compromise.
domain
cybersecurity
subdomain
cloud-security
tags
cloud-security, credential-compromise, threat-detection, guardduty, incident-response, anomaly-detection
version
1.0
author
mahipal
license
Apache-2.0
nist_csf
PR.IR-01, ID.AM-08, GV.SC-06, DE.CM-01
mitre_attack
T1078.004, T1530, T1537, T1580, T1003
mitre_f3.version
1.1
mitre_f3.tactics
initial-access, positioning, defense-impairment

Detecting Compromised Cloud Credentials

When to Use

  • When investigating alerts about unusual cloud API activity from unfamiliar locations
  • When building detection rules for credential theft and abuse across cloud environments
  • When responding to notifications from cloud providers about exposed credentials
  • When monitoring for credential stuffing or brute force attacks against cloud identities
  • When assessing the scope of a credential compromise after initial detection

Do not use for preventing credential compromise (use MFA, credential rotation, and secrets management), for detecting application-level credential theft (use application security monitoring), or for endpoint credential harvesting detection (use EDR tools).

Prerequisites

  • AWS GuardDuty enabled across all accounts and regions
  • Azure Defender for Identity and Entra ID Protection configured
  • GCP Security Command Center with Event Threat Detection enabled
  • CloudTrail, Azure Activity Log, and GCP Audit Log centralized for analysis
  • SIEM integration for cross-cloud correlation of credential abuse indicators
  • Threat intelligence feeds for known malicious IP ranges

Workflow

Step 1: Detect Credential Compromise Indicators in AWS

Monitor GuardDuty findings and CloudTrail anomalies that indicate credential abuse.

bash
# List GuardDuty credential-related findings
aws guardduty list-findings \
  --detector-id $(aws guardduty list-detectors --query 'DetectorIds[0]' --output text) \
  --finding-criteria '{
    "Criterion": {
      "type": {
        "Eq": [
          "UnauthorizedAccess:IAMUser/InstanceCredentialExfiltration.OutsideAWS",
          "UnauthorizedAccess:IAMUser/MaliciousIPCaller",
          "UnauthorizedAccess:IAMUser/MaliciousIPCaller.Custom",
          "UnauthorizedAccess:IAMUser/TorIPCaller",
          "UnauthorizedAccess:IAMUser/ConsoleLoginSuccess.B",
          "Recon:IAMUser/MaliciousIPCaller",
          "Recon:IAMUser/MaliciousIPCaller.Custom",
          "InitialAccess:IAMUser/AnomalousBehavior",
          "CredentialAccess:IAMUser/AnomalousBehavior",
          "Persistence:IAMUser/AnomalousBehavior"
        ]
      },
      "service.archived": {"Eq": ["false"]}
    }
  }' --output json

# Check for console logins from new locations
aws logs start-query \
  --log-group-name cloudtrail-logs \
  --start-time $(date -d "7 days ago" +%s) \
  --end-time $(date +%s) \
  --query-string '
    fields @timestamp, userIdentity.userName, sourceIPAddress, responseElements.ConsoleLogin
    | filter eventName = "ConsoleLogin"
    | filter responseElements.ConsoleLogin = "Success"
    | stats count() by userIdentity.userName, sourceIPAddress
    | sort count desc
  '

# Detect impossible travel (same user from geographically distant IPs within short time)
aws logs start-query \
  --log-group-name cloudtrail-logs \
  --start-time $(date -d "24 hours ago" +%s) \
  --end-time $(date +%s) \
  --query-string '
    fields @timestamp, userIdentity.arn, sourceIPAddress, eventName
    | filter userIdentity.type = "IAMUser"
    | stats earliest(@timestamp) as first_seen, latest(@timestamp) as last_seen,
            count_distinct(sourceIPAddress) as unique_ips by userIdentity.arn
    | filter unique_ips > 3
  '
Step 2: Detect Credential Abuse in Azure

Monitor Entra ID sign-in logs and Defender for Identity alerts for compromised credentials.

bash
# Check for risky sign-ins
az rest --method GET \
  --url "https://graph.microsoft.com/v1.0/auditLogs/signIns?\$filter=riskLevelDuringSignIn ne 'none' and createdDateTime ge 2026-02-16T00:00:00Z&\$top=50" \
  --query "value[*].{User:userPrincipalName,Risk:riskLevelDuringSignIn,IP:ipAddress,Location:location.city,App:appDisplayName,Status:status.errorCode}" \
  -o table

# Check for sign-ins from anonymous or Tor IPs
az rest --method GET \
  --url "https://graph.microsoft.com/v1.0/auditLogs/signIns?\$filter=riskEventTypes_v2/any(r:r eq 'anonymizedIPAddress') and createdDateTime ge 2026-02-22T00:00:00Z" \
  --query "value[*].{User:userPrincipalName,IP:ipAddress,Location:location.city}" \
  -o table

# List users flagged as compromised by Identity Protection
az rest --method GET \
  --url "https://graph.microsoft.com/v1.0/identityProtection/riskyUsers?\$filter=riskLevel eq 'high'" \
  --query "value[*].{User:userPrincipalName,RiskLevel:riskLevel,RiskState:riskState,LastDetected:riskLastUpdatedDateTime}" \
  -o table

# Check for suspicious application consent grants
az rest --method GET \
  --url "https://graph.microsoft.com/v1.0/auditLogs/directoryAudits?\$filter=activityDisplayName eq 'Consent to application' and activityDateTime ge 2026-02-16T00:00:00Z" \
  --query "value[*].{Activity:activityDisplayName,User:initiatedBy.user.userPrincipalName,App:targetResources[0].displayName}" \
  -o table
Step 3: Detect Credential Abuse in GCP

Query GCP audit logs and SCC findings for credential compromise indicators.

bash
# Check SCC Event Threat Detection findings
gcloud scc findings list ORG_ID \
  --filter="state=\"ACTIVE\" AND (category=\"ANOMALOUS_CALLER_LOCATION\" OR category=\"SUSPICIOUS_LOGIN\" OR category=\"CREDENTIAL_ACCESS\")" \
  --format="table(finding.category, finding.severity, finding.resourceName, finding.eventTime)"

# Query audit logs for service account key usage from unusual IPs
gcloud logging read '
  protoPayload.authenticationInfo.principalEmail:*@*.iam.gserviceaccount.com
  AND protoPayload.requestMetadata.callerIp!=("10." OR "172." OR "192.168.")
  AND timestamp>="2026-02-22T00:00:00Z"
' --limit=100 --format="table(timestamp, protoPayload.authenticationInfo.principalEmail, protoPayload.requestMetadata.callerIp, protoPayload.methodName)"

# Detect API calls from Tor exit nodes
gcloud logging read '
  protoPayload.requestMetadata.callerIp:("185." OR "198." OR "45.")
  AND protoPayload.authenticationInfo.principalEmail:*@company.com
  AND timestamp>="2026-02-22T00:00:00Z"
' --limit=50 --format=json

# Check for new service account keys created (persistence indicator)
gcloud logging read '
  protoPayload.methodName="google.iam.admin.v1.CreateServiceAccountKey"
  AND timestamp>="2026-02-16T00:00:00Z"
' --format="table(timestamp, protoPayload.authenticationInfo.principalEmail, protoPayload.request.name)"
Step 4: Build Cross-Cloud Correlation Rules

Create SIEM rules that correlate credential abuse indicators across cloud providers.

python
# siem_correlation.py - Cross-cloud credential abuse detection
import json
from datetime import datetime, timedelta

def detect_impossible_travel(events):
    """Detect same identity used from distant locations in short timeframe."""
    user_events = {}
    for event in events:
        user = event.get('principal', '')
        ip = event.get('source_ip', '')
        ts = event.get('timestamp', '')
        cloud = event.get('cloud_provider', '')

        key = f"{user}_{cloud}"
        if key not in user_events:
            user_events[key] = []
        user_events[key].append({'ip': ip, 'timestamp': ts, 'cloud': cloud})

    alerts = []
    for user_key, accesses in user_events.items():
        accesses.sort(key=lambda x: x['timestamp'])
        for i in range(1, len(accesses)):
            time_diff = (datetime.fromisoformat(accesses[i]['timestamp']) -
                        datetime.fromisoformat(accesses[i-1]['timestamp']))
            if time_diff < timedelta(hours=1) and accesses[i]['ip'] != accesses[i-1]['ip']:
                alerts.append({
                    'type': 'IMPOSSIBLE_TRAVEL',
                    'user': user_key,
                    'ip_1': accesses[i-1]['ip'],
                    'ip_2': accesses[i]['ip'],
                    'time_gap_minutes': time_diff.total_seconds() / 60,
                    'severity': 'HIGH'
                })
    return alerts

def detect_credential_stuffing(events, threshold=10):
    """Detect multiple failed logins followed by success."""
    user_attempts = {}
    for event in events:
        user = event.get('principal', '')
        success = event.get('success', False)
        key = user
        if key not in user_attempts:
            user_attempts[key] = {'failures': 0, 'success_after_failures': False}
        if not success:
            user_attempts[key]['failures'] += 1
        elif user_attempts[key]['failures'] >= threshold:
            user_attempts[key]['success_after_failures'] = True

    return [{'user': u, 'failures': d['failures'], 'severity': 'CRITICAL'}
            for u, d in user_attempts.items() if d['success_after_failures']]
Step 5: Respond to Confirmed Credential Compromise

Execute containment actions when credential compromise is confirmed.

bash
# AWS: Deactivate access key immediately
aws iam update-access-key --user-name COMPROMISED_USER \
  --access-key-id AKIA_COMPROMISED --status Inactive

# AWS: Invalidate temporary role credentials by updating role trust policy
aws iam update-assume-role-policy --role-name COMPROMISED_ROLE \
  --policy-document '{"Version":"2012-10-17","Statement":[{"Effect":"Deny","Principal":"*","Action":"sts:AssumeRole"}]}'

# AWS: Revoke all sessions for an IAM user
aws iam put-user-policy --user-name COMPROMISED_USER \
  --policy-name RevokeOldSessions \
  --policy-document '{
    "Version":"2012-10-17",
    "Statement":[{
      "Effect":"Deny",
      "Action":"*",
      "Resource":"*",
      "Condition":{"DateLessThan":{"aws:TokenIssueTime":"2026-02-23T10:00:00Z"}}
    }]
  }'

# Azure: Revoke all sign-in sessions
az rest --method POST \
  --url "https://graph.microsoft.com/v1.0/users/COMPROMISED_USER_ID/revokeSignInSessions"

# Azure: Force password reset
az ad user update --id COMPROMISED_USER_ID --force-change-password-next-sign-in true

# GCP: Disable service account
gcloud iam service-accounts disable COMPROMISED_SA_EMAIL

# GCP: Delete service account keys
gcloud iam service-accounts keys delete KEY_ID --iam-account=COMPROMISED_SA_EMAIL

Key Concepts

TermDefinition
Impossible TravelDetection of the same credential being used from geographically distant locations within a time period that makes physical travel impossible
Credential StuffingAttack using stolen username/password combinations from data breaches to attempt login across multiple cloud services
Instance Credential ExfiltrationGuardDuty finding indicating EC2 instance role credentials are being used from outside the expected AWS network
Anomalous BehaviorMachine learning-based detection of API call patterns that deviate significantly from the established baseline for a principal
Session RevocationInvalidating all active authentication sessions for a compromised principal to force re-authentication with new credentials
Persistence IndicatorAttacker actions designed to maintain access after initial compromise, such as creating new access keys or service account keys
Show full SKILL.md (270 more words)Show less

Tools & Systems

  • AWS GuardDuty: ML-based threat detection with specific finding types for credential compromise and unauthorized access
  • Microsoft Entra ID Protection: Identity risk detection for sign-in anomalies, compromised credentials, and risky user behavior
  • GCP Event Threat Detection: SCC component detecting anomalous API usage and credential abuse in GCP environments
  • CloudTrail / Activity Log / Audit Log: API audit logs providing the raw data for credential compromise investigation
  • SIEM (Splunk, Elastic, Sentinel): Centralized platform for cross-cloud correlation of credential abuse indicators

Common Scenarios

Scenario: Detecting an Access Key Compromised via Phishing

Context: A developer receives a phishing email that harvests their AWS console credentials. The attacker logs in from a foreign IP, creates a new access key, and begins enumerating the account.

Approach:

  1. GuardDuty triggers UnauthorizedAccess:IAMUser/ConsoleLoginSuccess.B for login from unusual country
  2. SOC reviews the finding and correlates with phishing reports from the email security team
  3. Query CloudTrail for all actions by the compromised user from the attacker's IP
  4. Discover the attacker created new access keys and ran IAM enumeration commands
  5. Immediately deactivate all access keys for the user and revoke active sessions
  6. Force password reset and re-enroll MFA
  7. Check for persistence: new IAM users, roles, Lambda functions, or EC2 instances created
  8. Remove any persistence artifacts and document the incident timeline

Pitfalls: Simply changing the password does not invalidate existing access keys or active sessions. All access keys must be rotated and temporary credentials revoked by adding a deny-all policy for tokens issued before the compromise was detected. Attackers may create new IAM users or roles for persistence before the initial credential is revoked.

Output Format

Cloud Credential Compromise Detection Report
===============================================
Detection Date: 2026-02-23
Scope: Multi-cloud (AWS, Azure, GCP)
Period: 2026-02-16 to 2026-02-23

ACTIVE COMPROMISE INDICATORS:
[CRED-001] AWS Console Login from Unusual Location
  User: developer@company.com
  Source IP: 185.x.x.x (Russia)
  Normal Location: US-East
  GuardDuty Finding: UnauthorizedAccess:IAMUser/ConsoleLoginSuccess.B
  Severity: HIGH
  Status: Credential deactivated

[CRED-002] Azure Impossible Travel Detection
  User: admin@company.onmicrosoft.com
  Location 1: New York, US (09:00 UTC)
  Location 2: Beijing, CN (09:15 UTC)
  Risk Level: HIGH
  Status: Sessions revoked, under investigation

DETECTION METRICS (Last 7 Days):
  Impossible travel detections:        5
  Anomalous API activity alerts:      12
  Failed login attempts > threshold:   3
  New credentials from unusual IPs:    2
  Total compromises confirmed:         2

CONTAINMENT ACTIONS TAKEN:
  AWS access keys deactivated:    3
  Azure sessions revoked:         2
  GCP service accounts disabled:  1
  Passwords force-reset:          4
  MFA re-enrolled:                4

© 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/detecting-compromised-cloud-credentials 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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Questions about Detecting Compromised Cloud Credentials

What does Detecting Compromised Cloud Credentials do?

Detect compromised cloud credentials across AWS, Azure, and GCP by analyzing anomalous API activity, impossible-travel patterns, and credential-stuffing indicators using GuardDuty, Microsoft…. Detecting Compromised Cloud Credentials is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Detect compromised cloud credentials across AWS, Azure, and GCP by analyzing anomalous API activity, impossible-travel patterns, and credential-stuffing indicators using GuardDuty, Microsoft Defender for Identity, and Google SCC Event Threat Detection.

When should I use Detecting Compromised Cloud Credentials?

Detecting Compromised Cloud Credentials fits situations like: investigating alerts about cloud API activity from unfamiliar locations; responding to an exposed-credential notification; scoping a credential compromise.

How do I install Detecting Compromised Cloud Credentials in Claude Code?

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

How do I install Detecting Compromised Cloud Credentials in Codex?

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

Can I use Detecting Compromised Cloud Credentials 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 detecting-compromised-cloud-credentials -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/detecting-compromised-cloud-credentials, .gemini/skills/detecting-compromised-cloud-credentials, .github/skills/detecting-compromised-cloud-credentials and .opencode/skills/detecting-compromised-cloud-credentials in your project.

What does Detecting Compromised Cloud Credentials need to run?

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

Does Detecting Compromised Cloud Credentials access the network?

SKILL.md names 1 domain. In commands or code: graph.microsoft.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Detecting Compromised Cloud Credentials safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. 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 Detecting Compromised Cloud Credentials use?

Detecting Compromised Cloud Credentials 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 Detecting Compromised Cloud Credentials use?

About 3.9k tokens (SKILL.md is roughly 16k 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 631 tokens, read only when the agent opens those files.

What are the alternatives to Detecting Compromised Cloud Credentials?

Skills that share tags, products or a category with Detecting Compromised Cloud Credentials: Defender For Cloud Hardening (vinayaklatthe/microsoft-security-skills, 175 stars), Azure Arc (vinayaklatthe/microsoft-security-skills, 175 stars), CD Pipeline Generator (ArabelaTso/Skills-4-SE, 253 stars) and Cloud Misconfig Auditor (criptogus/agent-evolve-network, 288 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Detecting Compromised Cloud Credentials?

mukul975 (a GitHub user) maintains it in mukul975/Anthropic-Cybersecurity-Skills, which has 33,922 GitHub stars. The repository holds 637 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.