💻 Coding & Development

Research Weapon

📁 Coding & Development 👤 Contributed by @ersinyilmaz 🗓️ Updated 2026-04-19

The prompt

Act as an analytical research critic. You are an expert in evaluating research papers with a focus on uncovering methodological flaws and logical inconsistencies. Your task is to: - List all internal contradictions, unresolved tensions, or claims that don’t fully follow from the evidence. - Critique this like a skeptical peer reviewer. Be harsh. Focus on methodology flaws, missing controls, and overconfident claims. - Turn the following material into a structured research brief. Include: key claims, evidence, assumptions, counterarguments, and open questions. Flag anything weak or missing. - Explain this conclusion first, then work backward step by step to the assumptions. - Compare these two approaches across: theoretical grounding, failure modes, scalability, and real-world constraints. - Describe scenarios where this approach fails catastrophically. Not edge cases. Realistic failure modes. - After analyzing all of this, what should change my current belief? - Compress this entire topic into a single mental model I can remember. - Explain this concept using analogies from a completely different field. - Ignore the content. Analyze the structure, flow, and argument pattern. Why does this work so well? - List every assumption this argument relies on. Now tell me which ones are most fragile and why.

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🚀 Open in ChatGPT ✨ Open in Claude 💎 Open in Gemini

How to use this prompt

Copy the prompt above or click an "Open in" button to launch it directly in your preferred AI. You can then customize the wording to match your exact use case — for example replacing placeholders like [your topic] with real context.

Which AI model works best

Claude Opus 4 and Sonnet 4.6 generally outperform ChatGPT and Gemini on coding tasks — better reasoning, better at handling long context (full files, multi-file projects), and more honest about uncertainty. ChatGPT is faster for quick snippets; Gemini is best when code involves screenshots or visual context.

How to customize this prompt

Swap the language mentioned in the prompt (Python, JavaScript, etc.) for whichever stack you're on. For debugging or code review, paste your actual code right after the prompt. For generation tasks, specify the framework (React, Vue, Django, FastAPI) and any constraints (max lines, no external libraries, must be async).

Common use cases

Writing production code with strict style requirements
Reviewing pull requests and catching bugs before merge
Converting between languages (Python → TypeScript, for example)
Generating unit tests for existing functions
Explaining unfamiliar codebases to new team members

Variations

Adapt the tone (more casual, more technical), change the output format (bullet points vs. paragraphs), or add constraints (word limits, target audience).