Skill UI

This skill manages the entire academic rebuttal cycle. It parses external reviews from major conferences (like ICML/OpenReview), drafts safe, text-only responses while strictly enforcing grounding (no fabrication) and full issue coverage. It supports multi-round follow-ups and adheres to venue-specific constraints, ensuring every claim is provable.

The Santa Method is a multi-agent adversarial verification framework designed to improve output quality by requiring two independent reviewers (Agent B and Agent C) to pass the content before publication. It eliminates single-agent bias and systematically improves outputs through iterative refinement loops until convergence or manual escalation.

This skill searches for published academic papers from major venues like IEEE, ACM, and Springer via the Semantic Scholar API. It is ideal for finding peer-reviewed journal articles and conference proceedings, providing crucial metadata such as citation counts, venue details, and TLDR summaries. Use it when you need verified, published literature beyond raw arXiv preprints.

This advanced code review skill forces genuine perspective shifts by employing three hostile reviewer personas: the Saboteur (production breaks), the New Hire (maintainability), and the Security Auditor (OWASP-informed). It ensures that all code reviews are critically rigorous, eliminating superficial "Looks Good To Me" approvals and catching blind spots missed by the original author or general AI models. Use it before merging a PR for maximum confidence.

Provides REST API access to PubMed for building advanced Boolean/MeSH queries, filtering by publication details, and programmatically fetching citations through E-utilities, making it ideal for systematic reviews and automated literature workflows.

Applies two-axis scoring, mandatory devil’s advocate reasoning, and persistent history to keep post-task self-reviews honest, resistant to inflation, and suitable for Claude Code sessions.

This skill provides a comprehensive, read-only audit of a development workspace, including active repositories, installed plugins, connected services, and environment configurations. It analyzes existing capabilities against best practices to identify functional gaps and recommends the most valuable ECC-native skills, hooks, and workflows needed to enhance the development experience. Ideal for setup planning and architectural reviews.

AgentFlow transforms your existing Kanban board (Asana, GitHub, Linear) into a self-managing, autonomous AI development pipeline. It orchestrates multiple AI agents through defined stages (Backlog to Done), enforcing deterministic quality gates, performing adversarial reviews, and tracking costs—all visible within your existing project management UI. It provides crash-proof execution and full observability for complex software development lifecycles.

A comprehensive collection of agent skills for rigorously auditing digital interfaces and user flows. It evaluates designs against 168 research-backed UX/UI principles, detects common design antipatterns, and provides specialized review for AI-powered interfaces, ensuring adherence to modern best practices.

This skill manages comprehensive software development life cycle operations—including issue triage, PR reviews, version releases, bug fixing, and marketing content generation. It operates using a parallel "agent army" of dynamic agents (10-20 per task), requiring the user to adopt an Engineering Manager (EM) mindset. Key principles involve rigorous citation checking (anti-hallucination) and applying multiple roles (Product Owner, Open Source Contributor, Architect) simultaneously to ensure robust, safe, and user-centric outcomes across all platforms.

Generates structured CodeTour `.tour` files in JSON format for detailed, step-by-step code walkthroughs. This tool is essential for onboarding new engineers, conducting architectural reviews, or documenting complex PR changes. It provides a guided narrative that directs users to specific files and line anchors, ensuring a structured and focused learning journey.

A comprehensive guide detailing security patterns and audit checklists for Automated Market Maker (AMM) smart contracts in DeFi. Covers critical vulnerabilities such as flash loans, slippage, reentrancy attacks, price manipulation, and integer overflow/underflow. Essential for reviewing major DeFi protocols like Uniswap.