Skill UI

Analyzes options chain data by separating real calls and puts from lottery-like speculative contracts to produce more reliable put/call sentiment, anomaly detection, and expiry breakdowns for watchlists, sectors, and event-driven names.

A protocol enabling AI agents from different vendors to collaborate on long-running projects, route work to best-suited agents, and improve over time. Uses markdown files with no central server, featuring capability cards, contracts, chronicles, and self-amendment mechanisms for cross-vendor optimization and constitutional evolution.

This skill assists developers in creating Pydantic models following a multi-model pattern for clean API contracts. It supports defining Base, Create, Update, Response, and InDB models with camelCase aliases and optional fields. Ideal for Python backend development, ensuring type safety and seamless integration with TypeScript types.

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.

Create repository-based execution contracts for multi-session implementation work, with stable task IDs, evidence gates, checkpoints, and exact resume state for fresh agents.

A critical discipline for managing expenses when integrating paid AI or inference APIs. This guard treats dollar cost as a third complexity dimension, mandating the definition of cost contracts (max calls per run, max budget per run, max budget per day). It enforces best practices for bounding loops, retries, and fan-out, ensuring that code bugs cannot lead to unexpected overspending. Best practice requires configuring hard caps both in the code and at the provider dashboard for defense in depth.

This skill guides the mastery of smart contract security best practices for Solidity. It is essential for writing robust, secure, and efficient contracts, auditing existing code for common vulnerabilities (such as reentrancy, overflow, and access control issues), and implementing secure DeFi protocols. It focuses on preventing common attack vectors while optimizing gas usage.

This skill verifies code implementations strictly adhere to documentation specifications, especially for blockchain smart contracts. It compares code against whitepapers and design documents to identify gaps, undocumented behaviors, or compliance issues. Ideal for auditing protocol implementations, ensuring deterministic alignment between intended logic and actual code execution without relying on prior knowledge.

This skill validates Three.js graphics implementations using fixed-view visual contracts and diagnostics. It ensures reproducibility through seed sweeps, camera-scale tests, and temporal stability checks. It captures no-post baselines and GPU budgets to prevent regression in procedural scenes, evaluating the rendering mechanism rather than subjective screenshots.

This skill provides advanced guidelines for designing robust and effective tools specifically for autonomous AI agents. It shifts the focus from traditional developer APIs to contracts optimized for Language Models (LLMs). Key topics covered include treating tool descriptions as prompt engineering, implementing namespacing, applying the Consolidation Principle, and utilizing Architectural Reduction patterns (like file system access) to maximize agent reliability and workflow complexity.

Check PDFs and images for signs of tampering or forgery using the Stipple API. Returns a risk band, inspection quality, and per-signal evidence for payslips, invoices, bank statements, IDs, and contracts before you rely on them.

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.