social-engineering
Social engineering testing - phishing, pretexting, vishing, and physical security assessment techniques.
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Social engineering testing - phishing, pretexting, vishing, and physical security assessment techniques.
Generate publication-quality academic diagrams and plots from paper methodology text using optimize_input, plan_diagram, generate_image, and critique_image tools.
Search and analyze research papers, find related work, summarize key ideas. Use when user says "find papers", "related work", "literature review", "what does this paper say", or needs to understand academic papers.
AI paper reviewer. Use when the user says 'review my paper', 'help me review this paper', '审稿', 'give me feedback on my paper', 'check my manuscript', 'evaluate this paper for NeurIPS/ICLR/EuroSys'. Accepts PDF files and produces structured narrative reviews with venue-specific dimensional scores and Accept/Reject recommendation.
Detects CI/CD tools, containerization, and orchestration from public signals
Identifies CDNs (Cloudflare, Akamai, Fastly) and WAFs
Maps IP addresses to cloud providers, ASNs, and organizations via WHOIS
Discovers official company domain via web search, WHOIS, and common TLD patterns
Extracts technology signals from DNS records (MX, TXT, NS, CNAME, SRV)
Queries CT logs for certificates and extracts SANs for subdomain discovery
Parses HTML for meta tags, generator comments, and script URL patterns
Creates a new GitHub issue with structured user story format, acceptance criteria, and project integration. Use when the user wants to create a ticket, issue, or task for the tradingstrategy-ai/frontend repository.
Writes webnovel chapters (3000-5000 words) using v5.1 dual-agent architecture. Context Agent gathers context, writer produces pure text (no XML tags), review agents report issues, polish fixes problems, Data Agent extracts entities with AI.
Plans detailed volume outlines with chapter-by-chapter breakdown, cool-point distribution, and Strand Weave pacing. Activates when user requests outline planning or /webnovel-plan.
Analyzes a sequence of memory containing Commodore BASIC tokens, formats address/word data types, and constructs side comments representing the plain BASIC commands.
Apply cognitive constraints that reshape thinking. Use when user needs code generation (/code), advice (/interview), critique (/critic), debugging (/debug), brainstorming (/creative), simplification (/simplify), emotional support (/empathy), brevity (/concise), or structured planning (/planning). Auto-detects appropriate field from request type.
Generates comprehensive, LLM-optimized codebase context using PMAT (Pragmatic AI Labs MCP Agent Toolkit). Use this skill when: - Starting work on unfamiliar codebases - Onboarding to new projects or repositories - Need quick understanding of project architecture - Preparing for refactoring or feature implementation - Creating documentation or technical specifications Outputs highly compressed markdown (60-80% reduction) optimized for LLM consumption. Supports 25+ languages with architecture visualization, complexity heatmaps, and dependency graphs.
Provides automated refactoring suggestions and complexity reduction strategies using PMAT (Pragmatic AI Labs MCP Agent Toolkit). Use this skill when: - User requests code refactoring, optimization, or improvement - Complexity analysis reveals high-complexity functions (cyclomatic > 10) - Code review identifies maintainability issues - Technical debt needs to be addressed systematically - Preparing legacy code for modernization Supports 25+ languages with data-driven refactoring recommendations based on complexity metrics, mutation testing results, and industry best practices (Fowler's refactoring catalog).