Free, copy-paste AI prompt template. Works with ChatGPT, Claude, Gemini, Perplexity, and Microsoft Copilot. Fill in the placeholders and run.
Act as a comprehensive repository analysis and bug-fixing expert. You are tasked with conducting a thorough analysis of the entire repository to identify, prioritize, fix, and document ALL verifiable bugs, security vulnerabilities, and critical issues across any programming language, framework, or technology stack. Your task is to: - Perform a systematic and detailed analysis of the repository. - Identify and categorize bugs based on severity, impact, and complexity. - Develop a step-by-step process for fixing bugs and validating fixes. - Document all findings and fixes for future reference. Phase 1: Initial Repository Assessment 1. Map the complete project structure (e.g., src/, lib/, tests/, docs/, config/, scripts/). 2. Identify the technology stack and dependencies (e.g., package.json, requirements.txt). 3. Document main entry points, critical paths, and system boundaries. 4. Analyze build configurations and CI/CD pipelines. 5. Review existing documentation (e.g., README, API docs). Phase 2: Systematic Bug Discovery Identify bugs in these categories: 1. Critical Bugs: Security vulnerabilities, data corruption, crashes. 2. Functional Bugs: Logic errors, state management issues, incorrect API contracts. 3. Integration Bugs: Database query errors, API usage issues, network problems. 4. Edge Cases: Null handling, boundary conditions, timeout issues. 5. Code Quality Issues: Dead code, deprecated APIs, performance bottlenecks. Discovery Methods: Static code analysis, dependency vulnerability scanning, code path analysis for untested code, configuration validation. Phase 3: Bug Documentation & Prioritization For each bug, document: BUG-ID, Severity, Category, File(s), Component, description of current and expected behavior, root cause analysis, impact assessment (user/system/business), reproduction steps and verification methods. Prioritize by severity, user impact, and complexity. Phase 4: Fix Implementation 1. Create an isolated branch for each fix. 2. Write a failing test first (TDD). 3. Implement minimal fixes and verify tests pass. 4. Run regression tests and update documentation. Phase 5: Testing & Validation 1. Provide unit, integration, and regression tests for each fix. 2. Validate fixes using comprehensive test structures. 3. Run static analysis and verify performance benchmarks. Phase 6: Documentation & Reporting 1. Update inline code comments and API documentation. 2. Create an executive summary report with findings and fixes. 3. Deliver results in Markdown, JSON/YAML, and CSV formats. Phase 7: Continuous Improvement 1. Identify common bug patterns and recommend preventive measures. 2. Propose enhancements to tools, processes, and architecture. 3. Suggest monitoring and logging improvements. Constraints: Never compromise security for simplicity. Maintain an audit trail of changes. Follow semantic versioning for API changes. Document assumptions and respect rate limits.
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The Comprehensive Repository Analysis & Bug Fixing prompt sits in the Development category of the LAXIMA AI Prompt Library. It is designed for any task where you would otherwise spend 10–30 minutes drafting from scratch — instead, paste the template, replace the 0 placeholders, and ship.
Like every prompt in the library, this template is structured so the AI understands role, context, constraints, and expected output format. That structure is the difference between a generic AI response and one you can actually use without rewriting.
This is a free, copy-paste AI prompt template for "Comprehensive Repository Analysis & Bug Fixing" in the Development category. Paste it into ChatGPT, Claude, Gemini, Perplexity, or Microsoft Copilot, replace the bracketed placeholders with your specifics, and get a structured response in seconds. The prompt is curated by LAXIMA as part of a 70+ prompt library focused on real business and technical tasks.
This prompt works with any major AI assistant including ChatGPT (GPT-4o, GPT-4.1), Claude (Sonnet 4.6, Opus 4.7), Google Gemini (2.5 Pro, 2.5 Flash), Perplexity, and Microsoft Copilot. For development tasks specifically, frontier-tier models tend to produce the highest-quality outputs — use the LAXIMA LLM Picker if you are unsure which model to pick.
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A well-structured prompt sets role, context, constraints, and output format — which is where most ad-hoc prompts fall short. According to a 2024 Harvard Business School study, professionals using structured prompts were 40% more productive than those writing from scratch. Templates eliminate trial-and-error while still letting you customize.
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