AI Prompt Library

A curated index of prompts and prompting techniques circulating in the AI community. Each entry credits its source where we have one, notes when we have adapted or rewritten it, and explains what it is for, when to use it, and what output to expect. Search and filtering run entirely in your browser.

What is in the library

24 prompts across 14 categories. Each entry gives the prompt itself plus what it is for, when to use it, and the output to expect. Search and category filtering happen in your browser — nothing is sent to a server.

Attribution: 20 of 24 entries name the source they were curated from; 4 have no creator recorded in our archive and say so. Sources are credited in plain text — we hold no verified public profile URLs, so we link none.

Coding Agents

  • Senior Engineer Codebase Audit — Have the model join an unfamiliar codebase like a senior engineer, reverse-engineer it, and surface architecture/quality problems without changing behavior. Curated from STICS
  • Production Debugging Engineer — Get root-cause debugging discipline: trace the real cause of a failure, enumerate edge cases, and propose the most robust fix rather than a guess. Curated from STICS
  • AI Technical Lead Mode — Make the model behave like a tech lead — ask clarifying questions and challenge decisions before writing code — instead of a code generator. Curated from STICS

Research Validation

  • Pressure-Test a Startup Idea Before You Build — Find every fatal flaw in a startup idea before building — core assumption, ranked failure modes, real-vs-nice-to-have, founder-market fit, and a blunt verdict. Rewritten from material curated from @evolving.ai
  • Validate the Real Problem (Must-Fix or Nice-to-Have) — Test whether an idea solves a real, paid-for problem or an invented one — with a specific early-adopter profile and non-leading discovery questions. Rewritten from material curated from @evolving.ai
  • Build Your MVP in 2 Weeks — Design the smallest product that tests the single riskiest assumption, cut everything else, and produce a day-by-day 2-week launch plan. Rewritten from material curated from @evolving.ai

Claude Code

  • "A Reviewer Will Check Your Output" (CLAUDE.md line) — Add a single line to CLAUDE.md telling the agent its work will be reviewed, so it self-scrutinizes for correctness, edge cases, and completeness before finishing. Adapted from material by @abhijitwt
  • Plan Before Coding — Stop the agent from jumping straight into a large implementation — make it brainstorm/plan and get approval first (no special mode needed). Adapted from material by Boris Cherny
  • Git-History Explainer — Use the model's git access to explain why code is shaped the way it is — when/why/by whom something was introduced and which issues it links to. Adapted from material by Boris Cherny

Context Management

  • Wrap-Up Skill (Session → NotebookLM / Second Brain) — End a Claude session by producing a NotebookLM-import-friendly summary so the next session can pick up without re-reading the chat. Source attribution not captured in our archive.
  • Compact Conversation Skill — Summarize a long chat into a paste-ready handoff so you can continue in a fresh session without losing context (and without paying to reload the whole history). Curated from @evolving.ai

Explanation

  • Fake Constraint (Force Creative Connections) — Break out of standard framing by forcing analogies from an unrelated domain. Curated from u/naculalex
  • Pretend Auditorium (Higher-Structure Explanations) — Get better structure, emphasis, anticipated Q&A, and pacing than 'explain clearly'. Curated from u/naculalex

Security Review

  • Red-Team Security Audit (All Layers) — Turn the model into an adversarial security auditor that sweeps every layer of a codebase and reports findings, attack chains, and fixes in a strict format. Adapted from material by The Wize AI (@thewizeai)
  • Pre-Launch Readiness Audit (Vibe Coders) — Check a fast-built app against the four launch basics — privacy policy, OWASP, secrets hygiene, and rate limits — with evidence and the smallest fix per failure. Source attribution not captured in our archive.

Workflow Automation

  • Self-Checking Loop (Any LLM) — Make any model iterate on its own work against strict success criteria until every criterion scores 8+/10, instead of a one-shot answer. Source attribution not captured in our archive.
  • Coding Loop Spec (Test-Gated) — Give a coding agent a strict loop with a real verifier — run tests, fix the highest-impact failure, re-check — and a hard stop condition. Source attribution not captured in our archive.

Advisor Mode

  • Critical Advisor Mode (Anti-Sycophancy Contract) — Reframe an LLM from polite assistant into smarter-than-you advisor. Rewritten from material curated from STICS

Iteration

  • Version 2.0 (Not Improve This) — Force the model to treat the input as v1 that needs a sequel, not incremental polish. Curated from u/naculalex

Token Savings

  • Caveman / Concise Mode — Cut conversational-reply output tokens by ~30–50% without changing answer quality. Curated from @evolving.ai

Preprocessing

  • Document Condenser — Compress an image-heavy PDF into ~20–30% of its length before uploading to the main LLM. Curated from @evolving.ai

Continuity

  • Assumed Shared Context (Continuity Bootstrap) — Bootstrap continuity on a fresh chat without paying full history reload. Curated from u/naculalex

Stress Test

  • Fake Audience Disagreement — Force the model to defend or concede rather than re-explain. Curated from u/naculalex

Seo Geo

  • SEO + GEO Auditor — Audit a site for both Google ranking and AI-search (GEO) visibility, prioritize quick wins, and output schema + llms.txt + content changes. Adapted from material by @vibha.in.progress

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