Skill UI

This skill manages complex coding tasks from initial implementation through testing, review incorporation, deployment, and final runtime verification. It ensures the task meets explicit Definition of Done (DoD) criteria iteratively, minimizing the need for manual prompting across multiple development stages.

This skill provides methods for reliably extracting typed, validated, and structured data from Large Language Models (LLMs), moving beyond simple text parsing. It covers best practices across major APIs (OpenAI's response_format, Anthropic's tool_use, Google's responseSchema) and local models. Key workflows include defining schemas (Pydantic/Zod), implementing constrained decoding, and building robust retry loops for production-grade data pipelines.

Guides founders and GTM teams on designing and deploying AI SDR systems, automating qualification, routing signals into actions, and orchestrating human-in-the-loop handoffs across inbound/outbound motions and CRM stacks.

Autoresearch Agent runs an autonomous optimization loop that edits a target file, invokes a provided evaluation command, keeps metric-improving commits, drops failures, and repeats indefinitely so engineers can optimize speed, bundle size, prompts, or other measurable goals overnight.

Starts a recurring autoresearch experiment loop at user-selected intervals (10 minutes to monthly), auto-schedules CronCreate jobs, records experiment metadata, and offers commands to monitor progress or stop the loop.

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.

This skill automates the iterative refinement of academic papers. It simulates a multi-round expert review process by running a cycle of Review $\rightarrow$ Fix $\rightarrow$ Recompile, utilizing advanced LLMs. It enhances structural consistency, polishes presentation, and addresses theoretical flaws, supporting optional style references and strict edit whitelisting for precise control.

This skill automates the multi-round research review process. It iteratively reviews provided work using advanced AI models (Codex MCP), identifying critical weaknesses, implementing fixes, and re-reviewing until the content achieves a positive assessment or the maximum round limit is reached. Ideal for refining complex academic papers, technical designs, or large codebases that require rigorous, iterative quality assurance.

This skill executes an autonomous, iterative research review loop, simulating a rigorous peer-review process. It repeatedly uses an external OpenAI-compatible LLM via API calls to score, critique, and identify weaknesses in a given research topic or document. The process continues to refine the content by implementing suggested fixes until the LLM provides a positive assessment or the maximum round limit is reached. Ideal for academic writing, technical documentation, and refining complex proposals.

This skill automates the iterative process of academic paper refinement, simulating the rigorous peer-review cycle of top-tier conferences (e.g., NeurIPS, ICML). It connects with an external LLM (MiniMax) to receive multi-round, critical feedback, tracks state persistence, and guides the user through necessary revisions until the research is deemed submission-ready. Ideal for maximizing the quality of technical writing.

Autonomously run programs, analyze outputs, and iteratively tune parameters for computer architecture and EDA workloads until objectives or timeouts are reached, making it easy to request sweeps, optimizations, or best configurations.

Structured verification loop for Laravel projects that runs environment checks, linting/static analysis, tests with coverage, security audits, migrations, and build/deployment readiness before PRs or deployments.