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Detecting data exfiltration attempts from AWS S3 buckets by analyzing CloudTrail S3 data events, VPC Flow Logs, GuardDuty findings, Amazon Macie alerts, and S3 access patterns to identify unauthorized bulk downloads and cross-account data transfers.

日本語の概要は準備中です。原文の説明を表示しています。

This skill teaches security teams how to detect and respond to unauthorized cryptocurrency mining operations in cloud environments. It covers identifying cryptomining indicators through compute usage anomalies, network traffic patterns to mining pools, GuardDuty CryptoCurrency findings, and runtime process monitoring on EC2, ECS, EKS, and Azure Automation workloads.

日本語の概要は準備中です。原文の説明を表示しています。

This skill teaches security teams how to deploy and operationalize Amazon GuardDuty for continuous threat detection across AWS accounts and workloads. It covers enabling protection plans for S3, EKS, EC2 runtime monitoring, and Lambda, interpreting finding severity levels, and building automated response workflows using EventBridge and Lambda.

日本語の概要は準備中です。原文の説明を表示しています。

This skill outlines methodologies for performing authorized penetration testing against AWS, Azure, and GCP cloud environments. It covers understanding the shared responsibility model for testing scope, leveraging cloud-specific attack tools like Pacu and ScoutSuite, exploiting IAM misconfigurations, testing for SSRF to cloud metadata services, and reporting findings aligned to MITRE ATT&CK Cloud matrix.

日本語の概要は準備中です。原文の説明を表示しています。

Find over-permissive RBAC roles and service-account token abuse paths in Kubernetes using kubectl auth can-i, rbac-police, kubectl-who-can, and rakkess during authorized cluster security reviews.

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Run CIS Kubernetes Benchmark checks and remediate findings with kube-bench.

日本語の概要は準備中です。原文の説明を表示しています。

Update npm/yarn/pnpm dependencies using manifest-first, lockfile-aware workflows. Prefer declared version bumps over resolution overrides. Use when updating, upgrading, or patching dependencies; fixing transitive dependency versions; addressing npm audit or CVE findings; or removing stale overrides.

日本語の概要は準備中です。原文の説明を表示しています。

Offensive/hostile QA auditor. Generalist reviewer specialized in the WordPress universe. Assumes everything is wrong until proven otherwise. Audit-tier only — produces severity-ranked findings, never implements.

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Standalone security audit on audit tier. Focused review of a feature or file set. Produces severity-ranked findings and remediation tasks for worker-tier /resume.

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Quality review of a change: is the logic right, is it safe, does it hold under real load, is risky code tested, is it fast enough, and is every line needed. Reads the connected code, not only the diff. Each finding is explained in plain English. Use for "review this", "code review", "review the last commit", "review my PR", "is this over-engineered", /ponytail-review.

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Correlate events across multiple log files from different sources, systems, or time periods. Build unified timelines, identify causal relationships between events, and highlight anomalies that span multiple data sources. Supports timezone normalization and gap detection. Use when analyzing logs from multiple systems (frontend, backend, database, infrastructure), investigating distributed system failures, or building event timelines across services. Triggers: "correlate these logs", "build timeline from multiple logs", "cross-system analysis", "find related events across logs", "multi-source log analysis", "distributed trace analysis"

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Review skills in any project using a dual-axis method: (1) deterministic code-based checks (structure, scripts, tests, execution safety) and (2) LLM deep review findings. Use when you need reproducible quality scoring for `skills/*/SKILL.md`, want to gate merges with a score threshold (for example 90+), or need concrete improvement items for low-scoring skills. Works across projects via --project-root.

日本語の概要は準備中です。原文の説明を表示しています。

Review audit-related documents (control design documents, bottleneck analyses, requirements definitions, etc.) for quality, scoring them 0-100 with a severity-rated findings list. Use when reviewing audit documents, checking control design quality, or verifying cross-document consistency. Supports documents governed by US GAAP, IFRS, or J-GAAP.

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Analyze email threads to extract current status, pending actions, timeline of events, and identify stale/outdated information. Use when asked to summarize email conversations, find outstanding action items in email chains, or detect when a thread summary is out of date.

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Android device offensive security, forensic extraction, and mobile app exploitation. Use this whenever the user needs to: break into or bypass the lock screen of an Android phone, extract data from a seized Android device for evidence, analyze or reverse-engineer an APK to find vulnerabilities, intercept communications on an Android device, exploit Android apps via deeplinks, steal crypto wallets or app data, set up rogue WiFi to target a phone, or plan any mobile device investigation or penetration test. Also trigger when the user mentions Android forensics, mobile pentesting, law enforcement phone extraction, device exploitation, WhatsApp or Telegram extraction from a phone, Android app reversing, CVE research for Android, or needing a chain of custody for mobile evidence. Even if the user describes the scenario casually or in another language, if they need to gain access to or pull data from an Android phone, use this skill.

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Static application security testing (SAST) for changed source files — Vulnetix's built-in rule set plus optional Semgrep augmentation when `.semgrep` config is present. Use when reviewing a PR for code-level vulnerabilities, scanning a feature branch before merge, gating CI on critical findings, or running rule-specific checks for a known weakness class.

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Package license analysis — detect copyleft conflicts against a permissive policy, surface AGPL / SSPL / GPL contaminants, output SPDX SBOM with per-package license findings. Use when auditing a release for license compliance, vetting a new dependency's license, building an OSS-attribution document, or producing a customer-facing license disclosure.

日本語の概要は準備中です。原文の説明を表示しています。

Dependency-conflict resolution when a `fix` version bump fails — diagnose the peer-dep tree, find a compatible safe version set, propose package-manager overrides (`overrides`/`resolutions`/`replace`/`[patch]`), fall back to safe-harbour inline patching. Use when an upgrade is blocked by transitive constraints, a peer-dep conflict surfaces, or you need to override a vulnerable transitive without bumping the parent.

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Create a look-alike audience from a seed audience and a candidate population dataset. Classifies Rosetta Stone attributes, generates the same materialized-view scoring pipeline Lookalike Studio emits (Naive-Bayes categorical weights + Gaussian continuous similarity), gates on approval, submits via `narrative_workflows_create`, and monitors the build to completion. Use when: "create a lookalike audience", "find more users like this segment", "expand my seed audience to 500k similar users", "score the population against my customers", "build a look-alike of dataset X". (narrative-audience)

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Weekly read-only repository audit. Grades one rotating rubric from .claude/audit-rubrics/, files between zero and five GitHub issues for what it finds, and changes no files. Use when the user asks to audit the repo, or when the audit-weekly workflow invokes it. It never edits, commits, branches, or opens pull requests.

日本語の概要は準備中です。原文の説明を表示しています。

Audit a dataset or access rule before it joins an identity-graph build (access rules behave like datasets in NQL here). Enumerates failure modes (hub identifiers, high-degree nodes, suspicious values, over-connected identifiers), tests hypotheses in parallel, quantifies damage by rows / edges / entities, and proposes minimal filters ranked by severity. When issues are found, returns a validated `CREATE MATERIALIZED VIEW` NQL the caller can run to produce a graph-ready clean source; if the data passes, says so and recommends it unchanged. Plans and authors the clean-view NQL; does not execute it. Use when: "audit this dataset before the graph build", "find bad edges in <source>", "check identity data quality", "recommend filters for the graph build", "quantify damage from <identifier_type>", "pre-graph DQ". (narrative-identity)

日本語の概要は準備中です。原文の説明を表示しています。

Work the review of a research run with rs_review — read the manuscript and record the panel's concerns with the evidence that grounds each one, keep the author's reply and the re-review's finding in their separate columns, run the devil's advocate with the concession threshold the tool enforces, and write the panel report and traceability matrix a response-to-reviewers letter is built from. Use when a document is ready for review, when a concern has to be answered or re-reviewed, or when an attack or a concession is being recorded.

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Work the prototyping lab itself — where runs live, what a run is, how to promote a run into the checked example library, and how to add a new runtime (Processing in Rust, Cinder) in one module. Use when asked to add a language or framework to the lab, to turn a prototype into a reusable example, to find or tidy what is in the lab, or when the lab's folder or catalog needs explaining.

日本語の概要は準備中です。原文の説明を表示しています。

Find top 10 new or improved free-AI-credit offers on X, Reddit, and the web, verify them officially, and open one review PR. Use for /offer-hunter or hunting new AI credits. Don't use for one known offer or catalog re-checks.

日本語の概要は準備中です。原文の説明を表示しています。