Skill UI

This skill defines structured behavioral frameworks (e.g., Brainstorm, Implement, Debug, Review, Teach, Ship) to guide the AI's output and approach based on the specific task phase. It optimizes performance by ensuring the AI adopts the correct mindset and output format—from initial ideation to final deployment—guaranteeing systematic, high-quality results across the entire development lifecycle.

This tool provides a comprehensive framework for analyzing web server logs (Apache/Nginx) to detect various security threats. It uses advanced regex matching against OWASP attack signatures to identify SQL injection, Local File Inclusion, and Directory Traversal attempts, alongside behavioral analysis for brute-force attacks and web scanner fingerprints. The system also enriches data using GeoIP for source attribution, generating a prioritized report for SOC analysts.

This skill systematically probes an AI model's core behavioral patterns across six critical dimensions, including reasoning, refusal, formatting, and grounding. It generates a comprehensive, styled HTML report with radar charts and actionable insights, allowing users to understand a model's true capabilities, compare different models, or perform compliance and red-team assessments without needing an external API key.

This guide provides a comprehensive framework for debugging and analyzing AgentCore runtime sessions using CloudWatch Logs Insights. It details advanced techniques like OpenTelemetry (OTEL) span correlation, resolving session IDs to trace IDs, and structuring complex query queries. Use this when investigating agent execution flows, diagnosing tool call failures, analyzing latency, or building detailed behavioral timelines.

This tool reads your local GitHub Copilot CLI conversation history using the specialized `vardoger` CLI. It analyzes your behavioral patterns, coding conventions, and usage style to generate a personalized instructions block. This block is written directly to your local Copilot configuration file, allowing the assistant to adapt its guidance and suggestions specifically to your unique habits and preferences. The process runs entirely offline, ensuring maximum privacy.

An advanced system that analyzes coding sessions via hooks to extract atomic, confidence-scored behaviors called "instincts." It tracks patterns, allowing users to evolve these instincts into comprehensive skills, commands, or agents. V2.1 introduces project-scoped learning, preventing knowledge contamination between different codebases and improving development workflow efficiency.

Probe any AI model across six behavioral dimensions—refusal boundaries, hallucination tendencies, reasoning style, formatting defaults, persona, and grounding—and generate a styled HTML report. Requires bdistill MCP; no API key needed.

Defines distinct behavioral modes such as brainstorm, implement, debug, review, teach, and ship, helping AI adapt its approach, communication style, and priorities to different task types and multi-agent workflows.

Analyze suspected malware through static, dynamic, and behavioral techniques: IOC extraction, YARA/Sigma rule authoring, sandbox orchestration, and anti-analysis detection.

This skill detects persistent instruction injection vulnerabilities in AI agents. It verifies if the agent stores malicious instructions in long-term memory or context to execute later. The process involves attempting to write a persistent rule and verifying if the agent follows it in subsequent interactions. It focuses on behavioral persistence rather than data leakage.

Test and benchmark LLM agents using behavioral testing, capability assessment, reliability metrics, and production monitoring to catch regressions and ensure robust performance.

This skill enables AI to adapt its operational behavior based on specific task types such as brainstorming, implementation, debugging, reviewing, teaching, and deployment. It defines distinct modes like BRAINSTORM for ideation, IMPLEMENT for coding, and DEBUG for troubleshooting, optimizing communication and output style for each context. Users can trigger modes manually or rely on automatic detection to ensure high-quality, context-aware responses.