Results for “patterns”

Detect anomalies in DNP3 protocol communications used in SCADA systems by monitoring for unauthorized control commands, firmware update attempts, protocol violations, and deviations from baseline traffic patterns using deep packet inspection and machine learning approaches.

Detect WMI-based lateral movement by analyzing Windows Event ID 4688 process creation and Sysmon Event ID 1 for WmiPrvSE.exe child process patterns, remote process execution, and WMI event subscription persistence.

Detects insider data exfiltration by analyzing DLP policy violations, file access patterns, upload volume anomalies, and off-hours activity in endpoint and cloud logs using pandas for behavioral analytics and statistical baselines.

Detect process injection techniques (T1055) including classic DLL injection, process hollowing, and APC injection by analyzing Sysmon events for cross-process memory operations, remote thread creation, and anomalous DLL loading patterns.

Expert guidance on secure coding practices, focusing on preventing common security errors made by AI during code generation. Use for: auditing AI-generated code, implementing secure design patterns, and ensuring code follows OWASP Top 10 standards. Covers input validation, authentication, cryptography, file handling, secure configuration, and business logic security.

Detect data staging activity before exfiltration by monitoring for archive creation with 7-Zip/RAR, unusual temp folder access, large file consolidation, and staging directory patterns via EDR and process telemetry.

Analyzes DeFi protocol risk before deposit, decomposes headline APY into sustainable yield, emissions, and drag factors, and flags common rug-risk patterns in tokenomics, liquidity, and governance. Advisory only—no wallet access, no private key handling, no transaction signing, no on-chain execution.

Detect PowerShell Empire framework artifacts in Windows event logs by identifying Base64 encoded launcher patterns, default user agents, staging URL structures, stager IOCs, and known Empire module signatures in Script Block Logging events.

Designs and implements backend systems including REST APIs, microservices, database architectures, authentication flows, and security hardening. Use when the user asks to "design REST APIs", "optimize database queries", "implement authentication", "build microservices", "review backend code", "set up GraphQL", "handle database migrations", or "load test APIs". Covers Node.js/Express/Fastify development, PostgreSQL optimization, API security, and backend architecture patterns.

GDPR compliance implementation. Data subject rights (access, deletion, portability), consent management, data processing records, PII handling, and privacy by design patterns. USE WHEN: user mentions "GDPR", "data privacy", "right to be forgotten", "data deletion", "consent management", "PII", "data subject request", "privacy policy", "cookie consent" DO NOT USE FOR: authentication - use auth skills; encryption - use `cryptography`; audit logging - use `audit-logging`

Designer's eye QA: finds visual inconsistency, spacing issues, hierarchy problems, AI slop patterns, and slow interactions — then fixes them. Iteratively fixes issues in source code, committing each fix atomically and re-verifying with before/after screenshots. For plan-mode design review (before implementation), use /plan-design-review. Use when asked to "audit the design", "visual QA", "check if it looks good", or "design polish". Proactively suggest when the user mentions visual inconsistencies or wants to polish the look of a live site. (gstack)

中英双语学术降 AIGC / bilingual academic de-AIGC skill. Removes AI-generated writing signatures from empirical papers in economics, management, and the social sciences — in both English and Chinese. Covers Turnitin AI, GPTZero, Originality.ai on the English side and 知网 AMLC, 万方, 维普 on the Chinese side. Uses a six-step loop (intake → audit → claim-evidence check → differentiated rewrite → five-dimension self-score → cold-reader recheck) with two pattern libraries (22 English + 17 Chinese patterns), section-by-section strategies for empirical papers, and hard protections that keep every number, coefficient, and citation intact.

Personal finance review skill for individuals who want to understand their spending, budgets, and financial patterns. Use whenever the user wants to analyze expenses, categorize spending, review a budget, build a budget from scratch, analyze debt, audit subscriptions, understand their savings rate, or track progress toward a financial goal. Triggers: "help me review my spending", "here are my expenses", "can you look at my budget", "help me build a budget", "I want to understand where my money goes", "categorize these transactions", "help me pay off my debt", "audit my subscriptions", "am I saving enough", "am I on track for my goal", "analyze my finances", or any time the user shares financial data (even raw or messy) and wants insight. Does NOT provide investment, tax, or financial planning advice — helps users understand their own data. Trigger proactively when the user pastes transactions, expenses, or any money-related data, even without an explicit request for analysis.