Prompt catalog
Browse reusable AI prompts by topic, then open any prompt to copy and adapt it for your workflow.
375 prompts · page 24 of 42
Explains a pasted code block the way you'd brief a competent new hire who has zero context on this codebase: what it does, why it exists, and any non-obvious decisions or gotchas baked into it. The output is formatted as a comment block ready to paste directly above the function, making it useful as living documentation and not just a one-off explanation.
Reviews a pasted diff through the lens of a senior engineer who has owned this codebase for two years and has strong opinions about pragmatic architecture. Instead of hedged, generic notes, it calls out whether abstractions sit at the right level, flags trade-offs that will bite in six months, and says so directly rather than diplomatically. Swap in your real stack and values for a sharper persona.
Profiles a pasted code block for real performance issues, using its execution frequency, typical data size, and your current pain point to prioritize what actually matters. - Flags O(n²) loops, N+1 queries, missing indexes, and memory/caching gaps - Ranks each finding High/Medium/Low impact with a suggested fix and expected gain - Focuses on the 20% of changes likely to deliver 80% of the speedup - Confirm suggestions with real profiling before shipping
Takes review notes you've already worked out — the issues, not the raw diff — and turns them into 5 polished GitHub PR comments ready to post. Each one stays direct without being harsh, names the specific issue, explains its impact, and suggests a fix, capped at 80 words so reviewers actually read it. Useful when you already know what's wrong and just want the wording tightened before publishing.
A fixed, project-specific checklist for reviewing changes in a React/TypeScript plus FastAPI/Python codebase, not a generic "review this code" prompt. - Frontend: React Flow node patterns, Zustand store selectors, design tokens - Backend: Pydantic model layering, FastAPI auth dependencies, response models - Also covers security, performance, test coverage, docs, and commit hygiene - Reach for it before merging, to keep reviews consistent across the team
A reusable context block to paste at the start of any AI coding session, covering your stack, key files, conventions, and known constraints so replies stop being generic. - Fill in project name, stack, main/config files, and naming/testing conventions once - Lists known constraints like performance limits and technical debt to work around - Save it as a markdown file and refresh it weekly as the project changes - Re-paste key facts mid-session on long threads to keep the AI anchored
Turns a pasted directory tree into a guided tour of an unfamiliar codebase, aimed at whoever is starting fresh or inheriting legacy code. - Identifies the architecture pattern, entry points, and the 5 most important files - Traces data flow, external dependencies, and maintenance red flags - Points to exactly which files to open first for a stated task - A follow-up step explains any single pasted file in depth: role, dependents, gotchas
Built for the PR that looks trivial in the diff but quietly changes an assumption something downstream relies on. - Flags changed assumptions, edge cases, and null/empty handling - Checks off-by-one logic, state transitions, and API contract drift - For each issue: what changed, what it could break, and the smallest test that would have caught it
Reviews a pasted diff the way a security-focused application engineer would, prioritizing auth and authorization checks, input validation, secret handling, injection risks, unsafe defaults, and data exposure over general code quality. Every finding is labeled a confirmed vulnerability, a plausible risk, or something needing manual verification, and it won't speculate beyond what the code shows. Reach for it on changes touching permissions, secrets, or user input.