730 reviewed prompts.
Builds a checklist of documents and details to gather before filing an insurance claim, with common reasons for delay. Use when starting a claim and wanting a complete submission packet.
Summarizes a market or sector outlook report: its thesis, evidence, assumptions and risks, plus what to watch. Use when you need the substance of a long outlook quickly. Educational, not investment advice.
Turns a meeting transcript or notes into a short summary, decisions, action items with owners and open questions. Use right after a meeting to share what was agreed.
Compares your written design system documentation with what the code actually uses and lists undocumented, outdated and conflicting values with fixes. Use when docs and product have drifted apart.
Plans how to run an AI model on your own hardware: memory estimate, model size and compression level, runtime setup, expected speed, risks and a benchmark checklist. Use it when privacy or offline use rules out cloud services.
Turns market news and data you paste in into a one-page weekly briefing with drivers, anomalies and a watch list. Use it for an internal Monday note or a personal review of markets.
Explains how a codebase works in plain language for product managers, designers or new team members, with a guided tour, glossary and quiz questions. Use it for onboarding or before discussing changes with developers.
Reviews a list of dependencies and produces a prioritized plan to replace outdated or risky ones, with risks, testing and rollback. Use when planning dependency upgrades for a service.
Designs a pipeline strategy for building, testing and deploying multiple independent services from a single repository, with rollout and rollback rules. Use when starting or untangling a delivery pipeline.
Designs an internal cloud platform for your workloads: architecture, infrastructure-as-code module layout, reliability targets, cost model, security posture and runbook outline. Use it when setting up or reworking shared infrastructure.
Turns a business problem and the data you have into a practical production ML design: metrics, data risks, model options, rollout and monitoring. Use it before committing a team to an ML project.
Checks an invoice's line totals, subtotals, discounts, tax amounts, dates and missing details, and lists discrepancies. Use before sending an invoice or paying one you received.
