Add reusable prompt components as MDX partials

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

Author: ofriw

website/shared-prompts/_ai-assisted-review.mdx

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+## AI-Assisted PR Review
+
+Systematic PR analysis using GitHub CLI for metadata, Chain of Draft (CoD) for efficient reasoning, and code research for architectural validation.
+
+### The Prompt
+
+````markdown
+You are $PROJECT_NAME's maintainer reviewing $PR_LINK. Ensure code quality, prevent technical debt, and maintain architectural consistency.
+
+Context from the PR author:
+$PASTE_AI_OPTIMIZED_DESCRIPTION
+
+# Review Process
+
+1. Use GitHub CLI to read the PR discussions, comments, and related issues
+2. Think step by step, but only keep a minimum draft for each thinking step, with 5 words at most. End the assessment with a separator ####.
+3. Never speculate about code you haven't read - investigate files before commenting
+
+# Critical Checks
+
+Before approving, verify:
+
+- Can existing code be extended instead of creating new?
+- Does this respect module boundaries and responsibilities?
+- Are there similar patterns elsewhere? Search the codebase.
+- Is this introducing duplication?
+
+# Output Format
+
+```markdown
+**Summary**: [One sentence verdict]
+**Strengths**: [2-3 items]
+**Issues**: [By severity: Critical/Major/Minor with file:line refs]
+**Reusability**: [Specific refactoring opportunities]
+**Decision**: [APPROVE/REQUEST CHANGES/REJECT]
+```
+
+Start by executing `gh pr view $PR_LINK --comments`, follow with the Code Research tool for codebase understanding.
+````
+
+**Replace:** `$PROJECT_NAME` (repo name), `$PR_LINK` (GitHub PR URL or number), `$PASTE_AI_OPTIMIZED_DESCRIPTION` ([from Dual-Optimized PR](/prompts/pull-requests/dual-optimized-pr)).

website/shared-prompts/_comprehensive-review.mdx

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+## Comprehensive Code Review
+
+Four-category review structure (Architecture, Code Quality, Maintainability, UX) ensuring comprehensive coverage while preventing confirmation bias through fresh-context execution.
+
+### The Prompt
+
+```markdown
+You are an expert code reviewer. Analyze the current changeset and provide a critical review.
+
+The changes in the working tree were meant to: $DESCRIBE_CHANGES
+
+Think step-by-step through each aspect below, focusing solely on the changes in the working tree.
+
+1. **Architecture & Design**
+   - Verify conformance to project architecture
+   - Check module responsibilities are respected
+   - Ensure changes align with the original intent
+
+2. **Code Quality**
+   - Code must be self-explanatory and readable
+   - Style must match surrounding code patterns
+   - Changes must be minimal - nothing unneeded
+   - Follow KISS principle
+
+3. **Maintainability**
+   - Optimize for future LLM agents working on the codebase
+   - Ensure intent is clear and unambiguous
+   - Verify comments and docs remain in sync with code
+
+4. **User Experience**
+   - Identify areas where extra effort would significantly improve UX
+   - Balance simplicity with meaningful enhancements
+
+Review the changes critically. Focus on issues that matter.
+Use ChunkHound's code research.
+DO NOT EDIT ANYTHING - only review.
+```

website/shared-prompts/_dual-optimized-pr.mdx

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+## Dual-Optimized PR Description Generation
+
+Generate coordinated PR descriptions for two audiences: human maintainers (concise summaries) and AI review assistants (comprehensive technical context).
+
+### The Prompt
+
+```markdown
+You are a contributor to $PROJECT_NAME creating a GitHub pull request for the current branch.
+Using the sub task tool to conserve context, explore the changes in the git history relative to main.
+Summarize and explain them like you would to a fellow co-worker:
+
+- Direct and concise
+- Professional but conversational
+- Assume competence and intelligence
+- Skip obvious explanations
+
+The intent of the changes are:
+$CHANGES_DESC
+
+Building upon this, draft two markdown files: one for a human reviewer/maintainer of the project
+and another complementary that's optimized for the reviewer's agent. Explain:
+
+- What was done and the reasoning behind it
+- Breaking changes, if any exist
+- What value the changes adds to the project
+
+Constraints:
+
+- The human optimized markdown file should be 1-3 paragraphs max
+- Agent optimized markdown should focus on explaining the changes efficiently
+
+Use ArguSeek, learn how to explain and optimize both for humans and LLMs.
+Use the code research to learn the overall architecture, module responsibilities and coding style.
+```
+
+**Replace:** `$PROJECT_NAME` (repo name), `$CHANGES_DESC` (1-2 sentence PR summary).

website/shared-prompts/_edge-case-discovery.mdx

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+## Edge Case Discovery
+
+Two-step pattern to discover what needs testing through systematic research, grounding test generation in actual code rather than generic advice.
+
+### The Prompt (Step 1: Discover Existing Edge Cases)
+
+```
+How does validateUser() work? What edge cases exist in the current implementation?
+What special handling exists for different auth providers?
+Search for related tests and analyze what they cover.
+```
+
+**Replace `validateUser()` with your target function/module.**
+
+### Follow-Up Prompt (Step 2: Identify Gaps)
+
+```
+Based on the implementation you found, what edge cases are NOT covered by tests?
+What happens with:
+- Null or undefined inputs
+- Users mid-registration (incomplete profile)
+- Concurrent validation requests
+```
+
+**Adapt bulleted questions to your domain.** Examples: payment processing (refunds, expired cards, rate limiting), data transformation (empty arrays, special characters, unicode), API endpoints (malformed JSON, missing headers, rate limits), auth (expired tokens, permission escalation), financial (rounding errors, overflow, currency conversion).

website/shared-prompts/_evidence-based-debug.mdx

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+## Evidence-Based Debugging
+
+Systematic debugging workflow combining Chain-of-Thought reasoning, grounding directives, and evidence requirements to prevent hallucination and force code retrieval.
+
+### The Prompt
+
+````markdown
+```
+$ERROR_DESCRIPTION
+```
+
+Use the code research to analyze the error above.
+
+INVESTIGATE:
+
+1. Read relevant source files and trace the code path
+2. Examine error messages, stack traces, and logs
+3. Identify the specific location of the failure
+4. Understand the surrounding architecture and data flow
+
+ANALYZE:
+
+5. Compare expected vs actual behavior
+6. Identify the root cause of the failure
+7. Determine if related issues exist elsewhere
+
+EXPLAIN:
+
+Provide your root cause analysis with evidence:
+- File paths and line numbers (`src/auth/jwt.ts:45-67`)
+- Actual values from code (`port: 8080`)
+- Specific identifiers (`validateJWT()`)
+- Exact error messages
+
+Then propose a fix.
+````

website/shared-prompts/_generate-agents-md.mdx

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+## Generate AGENTS.md (Project Context File)
+
+Systematic workflow for bootstrapping project context files using [ChunkHound](https://chunkhound.github.io/) codebase analysis and [ArguSeek](https://github.com/ArguSeek/arguseek) ecosystem research.
+
+### The Prompt
+
+```
+Generate AGENTS.md for this project.
+Use the code research tool to learn the project architecture, tech stack,
+how auth works, testing conventions, coding style, and deployment process.
+Use ArguSeek to fetch current best practices for the tech stack used and the
+latest security guidelines.
+
+Create a concise file (≤500 lines) with sections:
+- Tech Stack
+- Development Commands (modified for non-interactive execution)
+- Architecture (high-level structure)
+- Coding Conventions and Style
+- Critical Constraints
+- Common Pitfalls (if found).
+
+Do NOT duplicate information already in README or code comments—instead, focus
+exclusively on AI-specific operations: environment variables, non-obvious
+dependencies, and commands requiring modification for agents.
+```
+
+**Adapt sections** for your project: Security Guidelines (sensitive data), API Integration Patterns (microservices), Database Schema (data-intensive), Deployment Checklist (complex workflows).

website/shared-prompts/_test-failure-diagnosis.mdx

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+## Test Failure Diagnosis
+
+Systematic four-phase workflow for debugging test failures, combining Chain-of-Thought reasoning, grounding directives, and evidence requirements for objective analysis.
+
+### The Prompt
+
+````markdown
+```
+$FAILURE_DESCRIPTION
+```
+
+Use the code research to analyze the test failure above.
+
+DIAGNOSE:
+
+1. Examine the test code and its assertions.
+2. Understand and clearly explain the intention and reasoning of the test - what is it testing?
+3. Compare against the implementation code being tested
+4. Identify the root cause of failure
+
+DETERMINE:
+Is this a test that needs updating or a real bug in the implementation?
+
+Provide your conclusion with evidence.
+````