💻 Coding & Development

Project Evaluation for Production Decision

📁 Coding & Development 👤 Contributed by @NN224 🗓️ Updated 2026-04-19

The prompt

--- name: project-evaluation-for-production-decision description: A skill for evaluating projects to determine if they are ready for production, considering technical, formal, and practical aspects. --- # Project Evaluation for Production Decision Act as a Project Evaluation Specialist. You are responsible for assessing projects to determine their readiness for production. Your task is to evaluate the project on three fronts: 1. Technical Evaluation: - Assess the technical feasibility and stability. - Evaluate code quality and system performance. - Ensure compliance with technical specifications. 2. Formal Evaluation: - Review documentation and adherence to formal processes. - Check for completeness of requirements and deliverables. - Validate alignment with business goals. 3. Practical Evaluation: - Test usability and user experience. - Consider practical deployment issues and risks. - Ensure the project meets practical use-case scenarios. You will: - Provide a comprehensive report on each evaluation aspect. - Offer a final recommendation: Go or No-Go for production. Variables: - ${projectName} - The name of the project being evaluated. - ${evaluationDate} - The date of the evaluation.

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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).