Skill UI

This skill facilitates the systematic triage and closure of open promises or commitments within a project context. It guides the user to reliably close these commitments using verifiable, real-world outcomes via the acc_act(runtime="outcome") command. It emphasizes maintaining rigorous provenance discipline, ensuring that status updates are based only on external or runtime validated results, thereby maximizing data integrity and accountability.

This skill provides utilities to manage and drain the deliberation queue (brain_frames) of the ACC system. It allows users to list waiting frames, retrieve necessary context, perform deliberation on open tasks, and submit continuations using `acc_act(runtime="continue")`. It is essential for checkpointed work saved by headless runs, ensuring continuous task resolution and calibration against predicted outcomes in complex AI workflows.

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 comprehensive guide for developers building AI demos and GPU compute tasks on Hugging Face Spaces using ZeroGPU hardware. It covers best practices for writing and reviewing code that utilizes the `@spaces.GPU` decorator, managing runtime duration and quota, and understanding the specific CUDA availability model and process isolation rules required for reliable ML deployment.

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 functions as an optional agent companion for MDPR, a deterministic Markdown-to-presentation runtime. It helps review presentation workflows by providing semantic hints, visual evidence reports, and high-level layout intentions. Crucially, it keeps agent output evidence-based while letting MDPR maintain control over final slide geometry, rendering details, and layout coordinates.

This skill acts as a critical reality-check layer before code generation. It ensures that every function call, import, method, and variable the agent uses is provably available in the user's specific installed environment and version. It prevents runtime bugs caused by hallucinated or improperly assumed APIs, making the generated code robust and reliable.

Bun is a high-performance, all-in-one JavaScript runtime and toolchain. It functions as a runtime, package manager, bundler, and test runner, offering significant speed improvements over traditional Node.js setups. It is ideal for modern, fast-paced development, new projects, and deployment environments like Vercel.

Manages and coordinates complex workflows involving multiple AI agents and tasks. It efficiently distributes work across multiple workers, handles runtime errors, and intelligently merges disparate results into a unified output, making it ideal for large-scale, distributed AI computations.

This utility ensures the prevention of silent floating-point mismatch bugs across all EVM chains. It covers runtime querying of decimals, chain-aware caching, handling precision drift in bridged tokens, and safe normalization for bots, dashboards, and DeFi tools. It is crucial for accurate cross-chain balance comparison.

This skill provides access to the Anthropic Claude Managed Agent runtime, which is a cloud-native, persistent environment for running complex, long-running, or asynchronous AI tasks. Unlike local WASM agents, the managed runtime provides a full container, persistent filesystem, and robust state management across multiple turns. It supports the entire agent lifecycle: creation, prompting, status inspection, event logging, and mandatory termination.

This skill dispatches computationally intensive quantitative trading jobs (such as multi-year walk-forward analysis, large Monte-Carlo simulations, parameter sweeping, or deep model training) to a managed cloud agent runtime. It is designed for large-scale, production-grade backtesting that exceeds local machine capacity, ensuring robust execution and artifact management via Anthropic's cloud services.