Skill UI

Explains what makes up a language model’s context state and how engineers should structure prompts, tools, retrieval, history, and outputs to guide agent behavior and manage token budgets.

Designs and runs structured evaluation frameworks for agent systems, balancing token budgets, tool usage, and LLM scoring to compare configurations, guard production, and track regressions across complex workflows.

Guides Lindy AI developers through API and agent action rate limit handling, covering request throttling, agent budgets, webhook controls, and error recovery to keep integrations stable.

Optimizes Mistral AI spending by selecting models per task, estimating token costs, enforcing daily/monthly budgets, monitoring usage, and tightening prompts—ideal for teams watching billing, cutting API spend, and triggering budget alerts.

Guides selecting and implementing Retell AI architecture blueprints from single webhook servers to event-driven voice pipelines, helping teams match scale, latency budgets, and state management when building or migrating voice agent integrations.

Transforms a refined research proposal or method idea into a concise, claim-backed experiment roadmap, covering ablations, evaluation protocols, compute budgets, and execution order so papers can defend the problem, novelty, and any LLM/VLM/Diffusion/RL contribution.

Adds x402 payment execution stack to AI agents, giving per-task/session budgets, allowed recipients, rate controls, and non-custodial wallets via MCP tools so agents can autonomously pay for APIs, services, or other agents with no custodial risk.