Every collection contains task-specific templates plus an expert protocol for evidence, assumptions, risks, next actions, and quality control.
01
11 prompts
SEO
These SEO prompts help you reason from real keywords, audiences, pages, and first-party evidence. They deliberately separate facts from assumptions, so the model supports your analysis instead of pretending it has live search-volume or ranking data.
Use these marketing prompts to turn customer evidence and business constraints into focused action. Every template asks for a specific audience, offer, channel, or decision so the output stays practical instead of becoming generic marketing language.
These templates support planning, drafting, editing, and repurposing while keeping the human author responsible for facts and judgment. They work best when you provide original notes, examples, interviews, and a clear point of view.
Productivity prompts should reduce ambiguity, not add more text. This set helps you extract actions, plan realistically, document a process, and communicate status while keeping owners, evidence, dependencies, and unanswered questions visible.
Research quality depends on source quality and careful interpretation. These prompts help structure inquiry and synthesis, but they explicitly prohibit invented citations and ask the model to distinguish direct evidence, inference, disagreement, and missing information.
These prompts support consultative selling without fake urgency or manufactured claims. Give the model accurate account notes and let it organize discovery, follow-up, demos, and proposals around the buyer’s stated priorities and agreed next steps.
Strategy prompts are most useful when they challenge a decision rather than decorate it. This collection creates consistent comparison criteria, explores second-order effects, and identifies signals that would change a recommendation.
Coding prompts work when the model receives the stack, expected behavior, constraints, and the smallest relevant code. These templates prioritize evidence, minimal fixes, tests, and explicit trade-offs instead of unexplained code generation.