1328 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.
Proposal generator / meeting processor / content repurposer / Friday review / shutdown reset — out of 40 I tried, only these 5 survived, each saving 30+ minutes per run.
Doesn't let the AI just hand in its answer as-is: it first tags the source type of every key sentence, then hunts for counterexamples to attack its own claims, and finally grades confidence level item by item with suggested directions for verification. Good homework before researching, writing a report, or making a decision. Honest note: this isn't the same as actual fact-checking (the model may not be able to search the web) — but it forces uncertainty out into the open and noticeably reduces confident-sounding nonsense.
Report a bug in this format and the AI's fix hit rate jumps noticeably: give all four parts at once — the raw symptom, a minimal repro, expected behavior, and environment — and force the AI to diagnose first, ask for more information when it's insufficient, and never guess-and-change blindly. Suited to any scenario where you're asking ChatGPT or Claude to fix code. Note: the AI's diagnosis is reasoning, not an actual test run — after the fix, be sure to actually run through the included verification checklist.
Feed in a lease, employment, or outsourcing contract, and it scans clause by clause from your side of the deal: a plain-language translation, worst-case scenario, risk grading, and negotiation angle for each, plus three questions to bring to a professional at the end. Note: this is a prep tool for understanding a contract, not legal advice — for high-value contracts or ones involving major rights, consult a lawyer, and it's safest to treat the scan results as homework to do before meeting one.
Did your boss just say "take a look at this" and walk off? Paste in the exact wording and context, and the AI breaks down the possible interpretations, key clarifying questions, and a draft confirmation message — so you align on direction before starting work, instead of wasting a whole week on the wrong thing.
Teaches Claude Code to take over your recurring monthly Excel copy-paste routine: it interviews you to clarify the process first, runs a small-scale pilot, only automates the full workflow after confirmation, and finally delivers a one-click operating manual that non-engineers can follow.
Don't publish AI-written copy as-is: run it through three rounds of checks to catch hallucinated data, fabricated sources, and outdated information, with each item graded as trustworthy, needs your own verification, or needs a rewrite — comes with a reusable pre-publish checklist.
Feed the AI the background of a matter and it produces a draft following Taiwan's official document format — Subject, Explanation, Action — automatically applying standard bureaucratic phrasing and adjusting tone for upward, lateral, or downward correspondence. The final section forcibly flags regulations and figures that "need human verification"; this is positioned as a draft, not a final document.
Bilingual Chinese-English localization with one more layer than Google Translate. Specifies Traditional Chinese as used in Taiwan plus Taiwan-specific idioms, keeps names, brand names, and technical terms from being mistranslated, and delivers both a literal version and a localized, polished version in one pass so you can compare and pick. You can also feed it style samples so it learns your company's tone. Suited to marketing copy and product descriptions for foreign firms, freelancers, and cross-border e-commerce in Taiwan.
The 2026 cognitive gap: reasoning models like Opus, GPT-5, and Gemini Deep Think already think internally, so 2023-era incantations like 'you are a top expert,' 'take a deep breath,' and 'let's think step by step' are ineffective on them — or even counterproductive. This prompt has the AI rewrite your old prompts into a reasoning-model version, with a before/after comparison table, and explains which models count as reasoning models and whether free tiers support them.
A no-code way to run Self-Consistency: have the AI solve the same problem via three different lines of reasoning, then compare the results and take the majority consensus. Especially useful for scenarios where a mistake is costly — tax estimates, loan interest comparisons, recipe scaling. Worth noting upfront: this uses more tokens, so it burns through free quotas faster.
A workflow mindset office workers can copy directly. Complex tasks (writing a proposal, building a report) done in one giant prompt come out poorly; this teaches you to split it into four steps — outline → expand each section → proofread and polish → convert to bullet points — with the output of each step feeding the next, plus four ready-to-use Traditional Chinese prompts. You can even change direction mid-task by rerunning just one step instead of starting over.
A reusable Traditional Chinese 'prompt optimizer' incantation — paste in a rough prompt and it first asks for the key missing details, then produces a well-structured version along with notes on what changed and why. Honestly flags its limits: a meta-prompt can fix structure and phrasing, but not factual errors in the content itself.
A turn-based mock interview: the interviewer asks scenario-based questions tailored to your industry, one at a time, follows up on your answer before giving feedback, and provides a speakable English version of the answer for each question. After eight questions, you get an overall hiring-likelihood assessment. Closer to a real interview than just reading past questions.
For a boss's urgent demands, pushback, or last-minute extra work, this first diagnoses what they actually need (reassurance? a scapegoat? just an update?), then gives short, full, and escalation-ready reply options with risk notes for each. Built-in red-line check: it refuses goals that would require lying and offers an honest alternative instead.
Pulling a report but not knowing how to write the 'so what' is the most common sticking point in data work. This prompt has the AI act as a senior data analyst, interpreting the data you paste in through a three-layer structure—observation, hypothesized cause, recommended action—while forcing a hard distinction between 'conclusions directly supported by the data' and 'speculation that still needs verification,' so you don't get called out in a meeting for over-interpreting.
The biggest problem with handover documents is that everything you consider obvious never gets written down. This prompt has the AI act as a handover reviewer, first using structured questions to draw out the tacit knowledge in your head (who to contact, where passwords live, what has to happen every month, where the landmines are), then organizing it into a handover manual the successor can actually follow. Useful before resigning, transferring roles, or taking extended leave.
Installs a focused 'security auditor' subagent into Claude Code that reviews code and architecture file-by-file through an OWASP Top 10 lens as you develop features, classifying vulnerabilities by severity with directly-applicable remediation suggestions.
Installs a dedicated 'performance engineer' subagent into Claude Code that profiles code you've just written, finds bottlenecks like N+1 queries, memory leaks, and slow APIs, classifies them by impact, and provides before/after optimization recommendations with benchmark estimates.
A PostToolUse hook that makes Claude Code automatically run a formatter (Prettier/Black/gofmt) plus a linter every time it edits or writes a file—permanently removing 'inconsistent formatting' as a code review chore.
A PreToolUse Bash hook that intercepts destructive commands like rm -rf, fork bombs, and curl|sh before the AI executes them—a last line of defense that holds even against --dangerously-skip-permissions.
A single npx command generates an information-rich terminal statusline for Claude Code, showing the current directory, Git branch, model, a context-usage progress bar, real-time spend burn rate, and a countdown to usage reset—hooked into live ccusage stats.
Borrowing the three-layer architecture from buildermethods/agent-os (Standards / Product / Specs): have AI first scan your codebase to extract existing conventions into documented standards, then auto-inject them every time it writes code — so you never have to re-explain the rules.
Rewrites awesome-cursorrules' Go backend scalability standard into an AGENTS.md/CLAUDE.md covering idiomatic error handling, context propagation, interface abstraction, concurrency, and observability — so AI follows standard conventions and trade-off analysis when writing Go services.