xalgorix/xalgorix is a self-hosted AI penetration-testing tool in which an LLM agent performs reconnaissance, looks for vulnerabilities, and coordinates exploitation, while a separate verifier re-tests findings before reporting them. It is for security teams, ethical hackers, and bug bounty work using a private, bring-your-own-LLM setup. The catalogue skills extend the agent's pentesting workflow.
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 xalgorix/xalgorix --skill detecting-compromised-cloud-credentialsgit clone --depth 1 https://github.com/xalgorix/xalgorixWrote 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/xalgorix/xalgorix/detecting-compromised-cloud-credentials)<a href="https://agentmods.dev/skills/xalgorix/xalgorix/detecting-compromised-cloud-credentials"><img src="https://agentmods.dev/badge/skills/xalgorix/xalgorix/detecting-compromised-cloud-credentials/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/xalgorix/xalgorix/detecting-compromised-cloud-credentials"><img src="https://agentmods.dev/badge/skills/xalgorix/xalgorix/detecting-compromised-cloud-credentials.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00055 | $0.03759 |
| Opus 5.5 | $0.00022 | $0.01504 |
| Sonnet 5.5 | $0.00011 | $0.00752 |
| Haiku 4.5 | $0.00006 | $0.00376 |
Grade A, and why
detecting-compromised-cloud-credentials scanned grade A with 0 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 1mo 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 351 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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).
Detection Gaps & Validation
False-negatives that hide a live credential compromise:
- Instance-credential exfil has two variants.
UnauthorizedAccess:IAMUser/InstanceCredentialExfiltration.OutsideAWSflips to.InsideAWSwhen the attacker proxies through another AWS account — include both infinding-criteriaor you miss the AWS-internal pivot. - Federated/SSO console logins may not populate
responseElements.ConsoleLogin == "Success"; the signal lands inadditionalEventData/AssumeRoleWithSAMLinstead, so aConsoleLogin-only query misses SSO accounts. - Azure
riskLevelDuringSignInrequires Entra ID P2. Without P2,riskEventTypes_v2is empty and every risk query returns zero — a silent dead rule, not a clean tenant. - GCP service-account key usage needs Data Access audit logs (
DATA_READ/ADMIN_READ) explicitly enabled per service — off by default, soprincipalEmail:*.iam.gserviceaccount.comqueries see nothing. - The
unique_ips > 3impossible-travel heuristic misses low-and-slow single-residential-proxy abuse and false-positives on NAT/VPN.
Validate: confirm CloudTrail, SigninLogs, and GCP audit logs each return rows for the last 24h before trusting "no findings"; FP-tune impossible travel by allowlisting corporate egress and VPN CIDRs.
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.
- 1mo ago First seen · 351 lines · 55 tokens per session scan A b8470eb4cf69
detecting-compromised-cloud-credentials is a skill published in the GitHub repository xalgorix/xalgorix (1,230 stars, last pushed today), licensed Apache-2.0. It adds 55 tokens to every session and 3,759 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
auditing-aws-s3-bucket-permissions
Systematically audit AWS S3 bucket permissions to identify publicly accessible buckets, overly permissive ACLs, misconfigured bucket policies, and missing encryption settings using AWS CLI, S3audit, and Prowler to enforce least-privilege data access controls.
auditing-gcp-iam-permissions
Auditing Google Cloud Platform IAM permissions to identify overly permissive bindings, primitive role usage, service account key proliferation, and cross-project access risks using gcloud CLI, Policy Analyzer, and IAM Recommender.
analyzing-cloud-storage-access-patterns
Detect abnormal access patterns in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics. Identifies after-hours bulk downloads, access from new IP addresses, unusual API calls (GetObject spikes), and potential data exfiltration using statistical baselines…
analyzing-kubernetes-audit-logs
Parses Kubernetes API server audit logs (JSON lines) to detect exec-into-pod, secret access, RBAC modifications, privileged pod creation, and anonymous API access. Builds threat detection rules from audit event patterns. Use when investigating Kubernetes cluster compromise or building k8s-specific SIEM detection rules.
detecting-aws-cloudtrail-anomalies
Detect unusual API call patterns in AWS CloudTrail logs using boto3, statistical baselining, and behavioral analysis to identify credential compromise, privilege escalation, and unauthorized resource access.
auditing-terraform-infrastructure-for-security
Auditing Terraform infrastructure-as-code for security misconfigurations using Checkov, tfsec, Terrascan, and OPA/Rego policies to detect overly permissive IAM policies, public resource exposure, missing encryption, and insecure defaults before cloud deployment.