Skill UI

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.

This pattern defines a robust, continuous loop structure for autonomous agents. It integrates crucial components like quality gates, evaluation harnesses, and explicit recovery controls. It guides complex workflows by defining decision paths (e.g., PR control, RFC decomposition) and includes strategies to mitigate common failure modes such as infinite loop churn or cost drift, ensuring measurable progress in autonomous systems.

Autonomous frontend builders follow a Stitch-driven baton loop to read prompts, generate and integrate pages, refresh documentation, and hand off the next task for continuous site evolution.

Autonomous experiment loop that optimizes any file by a measurable metric. The agent edits a target file, runs a fixed evaluation, keeps improvements (git commit), discards failures (git reset), and loops indefinitely. Ideal for optimizing code speed, reducing bundle size, improving test pass rates, enhancing prompts, or boosting content quality through iterative measured attempts.

This skill manages and schedules recurring, autonomous experiments. It allows users to set an experiment to run continuously at predefined intervals (e.g., every 10 minutes, daily, or weekly) using cron scheduling logic. It handles the full lifecycle, including execution, result review, strategy refinement, and state persistence.

This skill orchestrates end-to-end autonomous AI research projects using a sophisticated two-loop architecture. It manages the entire research lifecycle, from initial literature review and hypothesis formation to running constrained experiments and synthesizing findings into publishable papers. It acts as a project manager, autonomously driving the process while structuring the workspace and presenting progress to human users.

Autonomous multi-round research review loop. Runs a reviewer, implements fixes, and re-reviews until a policy-approved positive assessment or max rounds is reached. Supports Codex and Copilot CLI backends with optional human checkpoints, difficulty levels, and HTML rendering.

This skill automates the research refinement process by entering an autonomous, multi-round review loop. It leverages any OpenAI-compatible LLM to score, critique, and guide necessary fixes until the work meets a high, expert-defined standard. It manages state persistence across rounds, simulating the rigorous process of professional peer review.

This skill implements an autonomous, multi-round research review loop, simulating the rigorous peer-review process of top-tier conferences. It iteratively reviews, identifies weaknesses, suggests fixes, and re-reviews a research document using the MiniMax API. The system manages state persistence across multiple rounds, ensuring continuous improvement until a 'ready' assessment is achieved or the maximum round limit is reached. Ideal for academic writing, thesis refinement, or comprehensive project documentation.

Autonomous full research pipeline that takes a general research direction through idea discovery, experiments, review, and writing to a polished submission-ready paper PDF. Use for complete AI/ML research lifecycle workflows from ideation to submission, including resume support and cross-model quality review.

Register ICANN domains via a REST API with crypto payments so AI agents can autonomously check availability, pay in USDC/USDT/ETH/BTC, and retrieve DNS management credentials.

A professional-grade 'Living Pipeline' that integrates advanced AI reasoning into the SDLC. It provides autonomous guardrails for system security, cloud cost management (FinOps), and Kubernetes compliance. It audits Linux Kernel patches for memory vulnerabilities, detects cost drifts in Terraform plans, and generates hardened K8s manifests from natural language policies.