Browse All AI Prompts

Produces startup concepts for a field you name, each with a value proposition, target user, and rough model attached. Useful for widening the option set early on, when the goal is to find angles worth researching rather than to pick a winner.

Frames the model as a salesperson who pitches a product and works through buyer hesitation. Useful for rehearsing a call, drafting outreach that does not read as a template, or stress-testing how your offer sounds to a sceptical prospect.

Puts the model in a brand strategy role, working through positioning, personality, and messaging pillars for a business you describe. Useful early, when you need the words that everything else — site copy, ads, packaging — will later be built from.

Casts the model as a PM who turns a rough feature idea into a structured requirements document with goals, scope, and success measures. Useful for forcing clarity on what a feature is actually for before any design or engineering time is spent.

Drafts business emails at a tone and length you specify, covering the awkward ones such as chasing payment, declining a request, or following up after silence. Useful for getting a defensible first draft when the wording matters more than the speed.

Frames the model as an email marketer producing subject lines, body copy, and calls to action for a described audience and goal. Useful for drafting a campaign quickly, then editing for voice — the structure and the offer logic come out already in place.

Generates ad headlines that respect Google's character limits while keeping the keyword and the offer intact. Useful for filling out a responsive search ad where you need many interchangeable lines that still read naturally in any combination.

Sets the model up to advise on keyword targeting, on-page structure, and technical fixes for a page or site you describe. Best used to pressure-test a plan before you write, so the page targets a real query with clear intent rather than a vague topic.

Frames the model as a social manager who plans content, cadence, and engagement tactics for a named platform and audience. Useful when you need a coherent posting plan instead of one-off ideas, including what to measure once the posts are live.

Puts the model in the role of a campaign strategist that picks a target audience, key message, channels, and supporting activity for a product you name. Good for generating a first campaign skeleton you can argue with, rather than starting from an empty brief.

Frames the model as an experienced Ethereum developer writing Solidity contracts with the access rules and state handling spelled out. Useful for scaffolding a contract and understanding the trade-offs, though any output needs a real audit before deployment.

Converts a written description of a system or process into diagram source you can render. Useful for documenting an architecture, a request flow, or a state machine without opening a drawing tool, and the output stays diffable in version control.

Asks the model to produce SVG markup for a described graphic, returning code you can paste directly into a page. Handy for simple icons, diagrams, and placeholder artwork when you want scalable output with no binary asset or build step.

Sets a security-focused frame for reviewing designs and configurations, surfacing weak points and recommending mitigations. Aimed at defensive work such as hardening a deployment, planning data protection, or preparing for a security review.

Positions the model as a solution architect who maps requirements onto systems, integrations, and deployment topology. Use it when you need to reason about how a new component fits an existing estate before committing to a technical direction.

Frames the model as a patient React instructor who explains a concept and then demonstrates it in code. Good for closing gaps around hooks, rendering behaviour, and state management when documentation alone has not made the idea click.

Turns the model into a QA engineer that writes test cases and reproducible bug reports for a described feature. Useful for building a regression checklist, or for turning a vague complaint into a report a developer can actually act on.

Produces commit messages that follow the Conventional Commits specification, with the right type prefix and a scoped subject line. Keeps a shared history readable and makes automated changelog and semantic-version tooling work properly across a team.

Reviews markup or a described interface for accessibility defects: contrast, focus order, labelling, and semantic structure. Use it before shipping a page to catch the issues that automated scanners miss, especially keyboard traps and missing landmarks.

Puts the model in an operations mindset for CI/CD, containers, and infrastructure questions, focusing on reliability and rollback safety. Good for reviewing a deployment plan or working out why a pipeline stage keeps failing intermittently.

Casts the model as an ML engineer who explains approaches, data preparation, and evaluation in practical terms rather than academic notation. Useful when you need to choose between techniques or understand why a training run is behaving badly.

Sets up the model to design and implement a feature across database, API, and UI in one pass, keeping the layers consistent. Best for scaffolding a new vertical slice where you want the data model and the interface decided together rather than bolted on.

Frames the model as an experienced frontend engineer who answers with modern React patterns, state decisions, and file structure rather than toy examples. Useful when you need an opinionated second view on how to lay out a feature before writing it.

Generates unit tests for a function you paste in, covering the normal path plus boundary conditions. Pair it with your existing test framework conventions so output drops straight into the suite. A quick way to add a safety net before refactoring legacy code.