Skill UI

Helps founders and go-to-market teams select between PLG, hybrid, and sales-led motions, design value-before-purchase flows, and optimize time-to-first-value based on price, complexity, and buyer signals.

Guides solo founders through end-to-end GTM decisions, stacking affordable AI-supported tools, designing agent workflows, allocating their most precious time, and knowing when to hire so they can scale go-to-market without adding headcount.

A forensic tool for performing post-mortem analysis on AI-assisted coding sessions. It goes beyond simply stating what was done, determining *why* work deviated (e.g., spec ambiguity, repo friction, agent error), identifying scope creep, and classifying rework patterns. It provides evidence-backed recommendations to improve prompts, codebase health, and future development workflows.

This agent provides structured summarization for academic papers, reports, and articles. It goes beyond simple summarizing by extracting key findings, generating comparative matrices across multiple sources, and producing properly formatted citations (APA, IEEE, etc.). It is designed for analysts, product managers, and researchers who need actionable, deep insights from dense documentation.

This skill analyzes DNS query logs to detect sophisticated threats such as data exfiltration via DNS tunneling, Domain Generation Algorithm (DGA) usage, and covert Command and Control (C2) channels. It utilizes advanced techniques like entropy analysis, query volume anomaly detection, and subdomain length analysis within SIEM platforms, helping SOC teams identify threats that bypass traditional network security controls.

This skill provides a comprehensive workflow for reverse engineering Go-compiled malware using Ghidra. It focuses on overcoming Go-specific binary challenges, such as non-null-terminated strings, complex metadata structures (pclntab), and function name recovery in stripped binaries. Users can extract build information, reconstruct dependencies, and analyze malicious capabilities essential for threat intelligence and incident response.

A comprehensive guide for analyzing PCAP files captured during malware execution. It demonstrates advanced techniques using Wireshark, Zeek, Suricata, and Python (scapy) to identify Command and Control (C2) communications, data exfiltration channels, Domain Generation Algorithms (DGA), and periodic beaconing patterns. Essential for malware reverse engineering and network threat detection.

This skill analyzes the cryptographic algorithms, key management systems, and file encryption routines employed by various ransomware families. It covers hybrid schemes like AES-RSA and modern ciphers such as ChaCha20. Use this when assessing data recovery feasibility, reverse engineering ransomware binaries, identifying implementation weaknesses (e.g., IV reuse, key derivation flaws), or developing custom decryptor tools.

A comprehensive tool for interacting with the arXiv database. It allows users to search for academic papers by topic or ID, retrieve detailed metadata (abstracts, authors, categories), and download the PDF files directly. Ideal for researchers and students needing to efficiently find, save, and summarize scientific literature.

A comprehensive guide and script collection for auditing Google Cloud Platform IAM permissions. It helps identify overly permissive bindings, unused service accounts, primitive role usage, and cross-project access risks. It utilizes gcloud CLI, Policy Analyzer, and IAM Recommender to ensure compliance and minimize the blast radius of misconfigurations.

This skill facilitates autonomous, multi-round research review and refinement. It continuously submits work for assessment via external reviewers (Codex or manual), implements suggested fixes, and re-reviews until the designated positive threshold is met or the maximum number of rounds is reached. It manages state persistence and adjusts difficulty levels for rigorous, iterative quality assurance.

This advanced skill automates the rigorous, iterative review and refinement of complex research topics or papers. It runs multi-round cycles, using an external LLM (MiniMax API) to provide continuous, constructive feedback. The process guides the user through review, fixes, and re-review stages until a positive assessment is achieved or the maximum round limit is reached. It includes robust state persistence for long-running academic workflows.