Skill UI

This guide provides a systematic, safe workflow for refactoring rapidly developed or AI-generated fullstack applications. It focuses on identifying and removing dead code, fixing broken imports, and consolidating repetitive logic while strictly maintaining existing API contracts, routes, and core functionality. Use this before launch to ensure a stable, maintainable production baseline, following a 'surgery, not demolition' philosophy.

This meta-skill is designed for complex decision analysis when a user provides or references structured documents such as contracts, quotes, spreadsheets, or notices. Instead of simple summarization, it extracts key evidence, identifies risks (e.g., renewal risk, payment terms), and structures the output into decision-ready formats, such as 'sign/reject/negotiate' recommendations, evidence tables, or required next actions. It supports input from PDF, DOCX, and XLSX formats.

This meta-skill automates the complex process of modernizing legacy PDF documents (e.g., historical contracts, scanned manuals, legal documents). It follows a structured four-step pipeline: structural extraction, natural-language rewriting of problem pages, generating an audit summary, and finally re-merging the content into a single, refined PDF. This ensures content is modernized while maintaining fidelity to the original meaning, providing a human review summary before finalization.

The Atlas Contract is a goal-integrity governance protocol designed for complex, multi-stage tasks such as backend development, API integration, or data persistence. It prevents 'goal drift' by enforcing critical checkpoints (Goal Contracts, Phase Checks, Final Audits). It is essential for high-risk, multi-part requests where maintaining strict alignment with the user's original objective is paramount.

Atlas Ledger is a critical companion designed to provide lasting 'memory' to project contracts. It automatically processes detected deviations or 'drift' (e.g., from audits or reviews) and distills these instances into formal, actionable contract clauses (WHEN/DON'T/INSTEAD). These clauses are written to a persistent project memory file, Atlas.md. This mechanism ensures that lessons learned from failure are systematically captured, allowing future contract cycles to incorporate these rules, thereby strengthening overall project governance and reliability.

A comprehensive, pre-deployment, defense-in-depth security audit tool for Solidity smart contracts built with Foundry. It combines static analysis (Slither, Aderyn), symbolic execution (Mythril), property-based fuzz testing, and key hygiene checks to proactively detect critical vulnerabilities like reentrancy, access control flaws, and arithmetic bugs before deployment to any EVM chain.

A comprehensive framework for managing third-party risk (TPRM) throughout the entire vendor lifecycle. This tool guides users through inventory, risk tiering, running due diligence questionnaires (SIG, CAIQ), reviewing security evidence (SOC 2, ISO 27001), codifying requirements in contracts, and establishing continuous monitoring protocols. It is essential for maintaining compliance, managing supply chain risk, and securing data handled by external vendors.

This critical protocol is the ultimate measure for dealing with deeply corrupted, unmaintainable, or structurally rotten code modules. Instead of risky patching, it mandates a controlled, multi-phase process to completely delete the target and rebuild it from scratch. It ensures precise identification of all public interfaces, implicit contracts, and working behaviors (the Preservation List) before any removal occurs, guaranteeing the new implementation matches the original functionality while resolving deep architectural debt.

This discipline establishes a portable, framework-agnostic layer for ensuring type safety at the network edge of any React or React Native application. It mandates a single, typed API client to wrap all network fetching, guaranteeing that raw wire JSON is immediately parsed into trusted, domain-specific types or standardized error objects before entering the application state. This pattern drastically reduces runtime errors and enhances code robustness.

A robust protocol designed for multi-agent collaboration on long-running projects. It simulates a self-optimizing city where diverse AI agents (citizens) interact using decentralized markdown files (The Register, Contracts, Chronicle). This framework enables cross-vendor optimization, dynamic work routing, and constitutional evolution without requiring a centralized server or specific runtime environment.

This skill performs a comprehensive, read-only, multi-agent review of a git diff or file scope. It launches four specialized sub-agents—covering Regression, Security, Performance, and Contracts—in parallel. The main agent then aggregates, filters, and summarizes critical findings, ensuring the code change is robust, secure, and maintainable. Ideal for pre-commit or Pull Request review workflows.

This comprehensive guide outlines best practices for building reliable and robust AI agent harnesses. It covers optimizing action spaces, defining structured tools, managing observation formats, and implementing essential error recovery contracts. Use these principles to dramatically improve agent completion rates, manage context budgets effectively, and transition AI prototypes into stable, production-grade autonomous systems.