Skill UI

This skill simulates a senior documentation engineer, generating highly structured, evidence-based technical documentation. It processes codebases to create comprehensive wiki pages, mandating deep structural analysis, citing specific code paths (file:line), and incorporating mandatory architectural diagrams (Mermaid). Ideal for creating deep-dive system manuals, component guides, and technical deep dives with verifiable sources.

This skill answers technical questions about a codebase by grounding all responses strictly within the provided source files. It is ideal for users who need to understand how a specific function works, locate definitions, or gain an overview of a component's implementation. All answers include inline citations to ensure accuracy and traceability.

This skill simulates an expert software engineer and systems analyst. It is designed for deep, evidence-based understanding of complex codebases. Users can ask "how does X work" or request architectural reviews, ensuring every claim is grounded in actual code paths, data flows, and dependency chains, following a rigorous multi-iteration process.

Detects missing zeroization of secrets and flags compiler-optimized removals with mandatory assembly/IR evidence, helping auditors verify C/C++/Rust cryptographic code through dataflow, CFG, and runtime validation.

Systematically verifies suspected security bugs to eliminate false positives and document evidence, guiding each finding through the standard or deep verification path and producing TRUE POSITIVE or FALSE POSITIVE verdicts.

Guides engineers through a disciplined four-phase debugging workflow so they investigate root causes, analyze patterns, test hypotheses, and implement fixes based on evidence before touching code.

Analyzes a code repository to generate a recommended, minimal set of read-only shell command permissions for Claude Code's settings.json. It automatically detects the project's tech stack (e.g., Python, Node.js, Go), build tools, and monorepo structure, ensuring a safe and evidence-based security baseline for new projects or auditing existing permissions.

A rigorous, security-focused code review process designed for Pull Requests (PRs), commits, and diffs. It moves beyond general reviews by focusing intensely on high-risk areas such as authentication, cryptography, external calls, and value transfer. The methodology is evidence-based, requiring detailed attack scenario modeling and generating comprehensive, auditable markdown reports to identify overlooked vulnerabilities and regressions.

Proof Of Vulnerability streamlines the creation and submission of evidence that a security flaw can be exploited, ensuring teams can quickly validate issues and coordinate fixes with a reproducible discovery narrative.

This skill orchestrates the entire coding lifecycle as a state machine, ensuring tasks proceed from issue intake through implementation, structured review, deployment, and final acceptance verification. It enforces a rigorous 'Definition of Done' (DoD) proof, moving beyond simple code changes. Ideal for complex feature development requiring structured handoffs and explicit verification evidence.

This skill enforces rigorous governance and discipline when AI modifies systems, debugs code, or makes root-cause claims. It mandates safety gates (e.g., backup, blast radius check), demands evidence for every diagnosis, and prevents common AI shortcuts like guessing or deflecting. Use this for critical code modifications, complex debugging, and high-stakes deployments to ensure thoroughness and safety.

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.