1254 hand-tested AI prompts across image, video, and music — each with full context, variables, and a sample output. Content is authored in Traditional Chinese; the UI here is English.
By default, helpful = agreeable = burying the real problem in paragraph four. Three one-line instructions reverse it: problem first, force a recommendation, explicitly forbid softening. The difference between reading and fixing.
Three patterns shared by the HackAPrompt 2.0 champion: frame the task as an audit, make the abstraction level explicit, and stage the context like production. They work on Claude, GPT, and Gemini alike.
Mitchell Hashimoto's "Agent = Model + Harness" framework. The reason 88% of enterprise AI agents never reach prod isn't the prompt — it's the missing deterministic orchestration layer. Four CS primitives you can't skip: state machine, idempotency, DAG, DLQ.
No conversation, no suggestions, no attempts — it just turns any situation into a structured execution plan. Fixed 9-block output: Reality / Objective / 4-phase Timeline / Tactics / Threats / Defenses / Contingency / Leverage / Confidence. Same input always yields the same output.
More than "list the CVEs." It finds how individual weaknesses chain into an attack and estimates how long an AI-augmented attacker would need to break in. A take on the OpenAI Daybreak concept, built for small teams. Feed in a stack description (Next.js + Supabase + Clerk, etc.) and get back critical paths plus a patch-priority order.
Most people fill Custom Instructions with a LinkedIn resume, "I'm a software engineer, I like bullet points," and it does nothing. Swap in a description of the conversational relationship you want, and three sentences will completely change how the model behaves.
Scores every claim on three axes (A: sources / B: counterevidence / C: completeness), plus a 4-tier source hierarchy, adversarial self-poking, and 4 output modes.
Written after Google's threat-intelligence report in May 2026. AI hacking has gone from nascent to industrial scale, with commercial AI models digging up zero-days humans had missed for decades. This prompt lays out every place you can be attacked.
Combines a resume auditor, market analyst, system auditor, and strategist. It won't flatter you, won't over-reassure you, and won't push you toward roles that don't fit. Includes a 10-step process and ghost-job detection.
More than a grammar fix. Purpose-built for persuasive and narrative writing: four priorities (clarity → tone → emotional resonance → structure), an explanation for every change, plus extra suggestions.
Stop picking a role for every decision (financial planner / career coach / relationship coach) — one universal prompt, four steps. The regret line is the core — it forces the model from "it depends" to "actually do this."
The pain of writing a CV isn't the layout — it's listing duties instead of achievements, then having AI pile on and turn you into a "dynamic team player." This ruleset forbids AI from drafting anything until it has your evidence and positioning, and walks you through one question at a time.
Before you log off, ramble into your phone mic for 2 minutes, hand it to Claude, and get back a sorted list: first thing tomorrow morning + ranked to-dos + waiting-on + can-defer. Auto-detects daily vs. weekly mode.
The author wrote this after 30% of their heated replies made them wince the next day — costing a few deals and a friendship. The trick: don't let AI rewrite it, let it show you what the calm version would look like. Once you see it, you usually can't go back to the original.
A method dreamed up by a teacher 19 essays into a 30-essay all-nighter. Where typical AI prompts "act as an English teacher" and devolve into a wall of rubric noise, this chain solves the real grading workflow: don't comment on everything.
Written after Cloudflare cut 20%, BILL cut 30%, and Upwork cut 24% in 2026. Your job title tells you nothing; breaking the work into tasks tells you everything. Get an AI vulnerability score (1-10) and timeline for each task, plus an overall risk rating.
Instead of telling AI to write code directly, have it translate a vague goal into an executable prompt structure first, then use that structure to drive code generation. Phase 1 Intent → Phase 2 Structuring → Phase 3 Tool-Level → Phase 4 Implementation.
Run this before you build a custom GPT or n8n agent. It forces you to define boundaries up front and prevents agent scope creep ("just handle my work for me" = disaster).
An Oxford study in Nature: the "warmer" an AI is trained to be, the more its accuracy drops, by 10-30 percentage points. This prompt reviews an AI response and flags every place warmth overrides accuracy.
Upload 10 PDFs and Gemini automatically maps the consensus, disagreements, and gaps, then drafts a literature review.
Upload a public company's annual or quarterly report PDF and auto-generate a management-quality + risk read plus an investment thesis
Upload the whole repo or its key files, and Gemini gives new hires a reading order to understand the codebase within an hour
Upload a 60-minute meeting recording and Gemini surfaces every action item, owner, and timestamp.
Paste 1-3 screenshots (browser / Figma / app) and Gemini pinpoints the visual problem + the fix