{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"cursor-rules","slug":"cursor-rules","name":"Cursor Rules","type":"repo","url":"https://cursor.directory","page_url":"https://unfragile.ai/cursor-rules","categories":["prompt-engineering","code-editors","app-builders"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"cursor-rules__cap_0","uri":"capability://memory.knowledge.project.context.injection.via.dotfile","name":"project-context-injection-via-dotfile","description":"Injects project-specific AI instructions into Cursor IDE by parsing and loading .cursorrules files from the repository root. The system reads plain-text rule files, interprets them as system prompts, and automatically prepends them to all AI interactions within that project context, enabling the AI assistant to understand framework conventions, coding standards, and project-specific patterns without manual context setup for each conversation.","intents":["I want my AI assistant to understand my project's coding conventions without repeating them in every prompt","I need the AI to follow framework-specific best practices (React hooks patterns, Django ORM conventions, etc.) automatically","I want to enforce team coding standards through AI guidance rather than just linting rules","I need the AI to understand my project structure and naming conventions to generate more contextually appropriate code"],"best_for":["teams standardizing on Cursor IDE with shared coding conventions","framework-specific projects (Next.js, Django, Rails) needing consistent AI-assisted development","organizations enforcing architectural patterns through AI guardrails","open-source maintainers guiding contributor code generation"],"limitations":[".cursorrules files are Cursor IDE-specific; no cross-IDE portability to VS Code, JetBrains, or other editors","Plain-text format lacks schema validation — malformed rules fail silently or produce unpredictable AI behavior","No versioning mechanism — rule changes apply immediately to all team members without migration path","File size limitations may apply (typical LLM context windows); very large rule files may be truncated","Rules are loaded once at project open; dynamic rule updates require IDE restart"],"requires":["Cursor IDE (any recent version with .cursorrules support)",".cursorrules file in repository root or project directory","Plain text editor to create/modify rules","No external dependencies or API keys required"],"input_types":["plain text (.cursorrules file)","markdown-formatted instructions","code examples and patterns"],"output_types":["system prompt injection","AI assistant behavior modification","contextual code generation guidance"],"categories":["memory-knowledge","prompt-engineering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cursor-rules__cap_1","uri":"capability://memory.knowledge.community.rule.discovery.and.curation","name":"community-rule-discovery-and-curation","description":"Provides a searchable, community-maintained repository of pre-written .cursorrules files organized by framework, language, and use case. The directory indexes rules contributed by developers, includes metadata (framework version, language, author), and enables users to browse, fork, and adapt existing rules rather than writing from scratch. Rules are stored as plain-text files in a Git repository with community voting/starring to surface high-quality examples.","intents":["I want to see example .cursorrules for my tech stack (React, Django, Rust, etc.) without writing from scratch","I need to understand what kinds of AI instructions are effective for my framework","I want to adapt a proven rule set from another team rather than inventing my own","I need to discover best practices for AI-assisted development in my language/framework"],"best_for":["developers new to Cursor IDE or AI-assisted development","teams adopting a new framework and seeking AI guidance patterns","open-source projects wanting to standardize contributor AI behavior","framework maintainers documenting recommended AI development practices"],"limitations":["Rules are community-contributed with no formal review or quality guarantee — some may be outdated or ineffective","No automated testing of rules; users must manually validate that rules produce desired AI behavior","Repository growth may make discovery harder over time without robust categorization/tagging","Rules may conflict with each other if users combine multiple .cursorrules from different sources","No built-in versioning or deprecation mechanism for outdated rules"],"requires":["Internet access to browse cursor.directory","Git or GitHub account to contribute rules (optional)","Cursor IDE to use downloaded rules","Basic understanding of how .cursorrules work"],"input_types":["framework/language search queries","user-contributed .cursorrules files","metadata (tags, descriptions, author info)"],"output_types":["searchable rule listings","downloadable .cursorrules files","rule metadata and examples","community ratings/stars"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cursor-rules__cap_10","uri":"capability://code.generation.editing.dependency.and.library.management.guidance","name":"dependency-and-library-management-guidance","description":"Encodes preferred libraries, dependency constraints, and version requirements into .cursorrules files, guiding AI to use approved libraries and avoid deprecated or incompatible dependencies. Rules can specify which libraries are preferred for common tasks, which versions are supported, and which dependencies should be avoided. The AI can then generate code that uses the correct libraries and respects version constraints.","intents":["I want the AI to use our approved libraries, not suggest random npm packages","I need the AI to respect our version constraints and compatibility requirements","I want the AI to avoid deprecated libraries and suggest modern alternatives","I need the AI to understand our dependency tree and avoid conflicts"],"best_for":["teams with strict dependency management policies","projects with complex dependency trees","organizations managing multiple projects with shared dependencies","teams maintaining legacy code with specific version constraints"],"limitations":["AI may not know about all available libraries or their versions","Rules can become outdated as libraries are updated or deprecated","AI may suggest libraries that technically work but are suboptimal","Dependency conflicts are complex and may not be fully expressible as rules","No programmatic enforcement; AI may still suggest unapproved libraries"],"requires":["Cursor IDE","Clear list of approved libraries and versions","Understanding of dependency constraints and compatibility","Ability to express dependency guidelines as text rules"],"input_types":["approved library lists","version constraint specifications","deprecated library warnings","dependency compatibility requirements"],"output_types":["library-aware code generation","approved dependency usage","version-compatible implementations",".cursorrules dependency section"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cursor-rules__cap_11","uri":"capability://code.generation.editing.documentation.and.comment.generation.guidance","name":"documentation-and-comment-generation-guidance","description":"Encodes documentation standards, comment conventions, and documentation requirements into .cursorrules files, guiding AI to generate code with appropriate documentation, comments, and docstrings. Rules can specify documentation format (JSDoc, Sphinx, etc.), comment style, and what should be documented. The AI can then generate code with documentation that follows team standards.","intents":["I want the AI to generate code with proper JSDoc/docstring comments","I need the AI to follow our documentation format and style","I want the AI to document complex logic and edge cases","I need the AI to generate README sections or API documentation"],"best_for":["teams with strong documentation cultures","open-source projects needing high-quality documentation","teams with API documentation requirements","projects with complex logic requiring detailed comments"],"limitations":["AI-generated documentation may be generic or miss important details","Rules cannot enforce documentation quality or accuracy","AI may generate comments that are obvious or unhelpful","Documentation requirements vary widely; rules may not cover all cases","Maintaining documentation rules as standards evolve requires ongoing effort"],"requires":["Cursor IDE","Clear documentation standards and format","Understanding of team's documentation expectations","Ability to express documentation guidelines as text rules"],"input_types":["documentation format specifications (JSDoc, Sphinx, etc.)","comment style guidelines","documentation requirement descriptions","example documentation snippets"],"output_types":["documented code generation","generated comments and docstrings","documentation-aware implementations",".cursorrules documentation section"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cursor-rules__cap_12","uri":"capability://code.generation.editing.error.handling.and.logging.patterns","name":"error-handling-and-logging-patterns","description":"Encodes error handling strategies, logging conventions, and exception patterns into .cursorrules files, guiding AI to generate code with appropriate error handling and logging. Rules can specify error handling patterns (try-catch, error boundaries, etc.), logging levels and formats, and what should be logged. The AI can then generate code that handles errors and logs appropriately.","intents":["I want the AI to generate code with proper error handling, not just happy paths","I need the AI to follow our logging conventions and log levels","I want the AI to understand our error types and custom exceptions","I need the AI to generate code that handles edge cases and failures gracefully"],"best_for":["teams with strict error handling requirements","production systems requiring robust error handling","projects with centralized logging and monitoring","teams with specific error handling patterns (e.g., error boundaries in React)"],"limitations":["AI may not anticipate all possible errors or edge cases","Rules cannot enforce error handling quality or completeness","Error handling patterns vary widely; rules may not cover all cases","AI may generate error handling that is overly verbose or insufficient","Maintaining error handling rules as patterns evolve requires ongoing effort"],"requires":["Cursor IDE","Clear error handling patterns and conventions","Understanding of logging standards and requirements","Ability to express error handling guidelines as text rules"],"input_types":["error handling pattern specifications","logging convention descriptions","custom exception type documentation","edge case handling examples"],"output_types":["error-aware code generation","generated error handling code","logging-aware implementations",".cursorrules error handling section"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cursor-rules__cap_2","uri":"capability://code.generation.editing.framework.specific.instruction.templating","name":"framework-specific-instruction-templating","description":"Provides pre-structured .cursorrules templates tailored to specific frameworks (Next.js, Django, Rails, Svelte, etc.) that encode framework-specific best practices, common patterns, and architectural conventions. Templates include sections for code style, testing patterns, performance considerations, and framework idioms, allowing developers to customize a proven baseline rather than writing rules from scratch. Rules are organized by framework version and include examples of good/bad patterns.","intents":["I want AI to generate Next.js code that follows app router conventions, not pages router","I need the AI to understand Django ORM best practices and avoid N+1 queries","I want AI-generated code to follow my framework's testing conventions (Jest, pytest, RSpec)","I need the AI to respect my framework's performance constraints (e.g., avoiding large bundle sizes in Next.js)"],"best_for":["teams standardizing on a single framework across projects","framework-specific teams (e.g., all Next.js, all Rails) wanting consistent AI behavior","junior developers learning framework conventions through AI-assisted code generation","framework maintainers documenting recommended development practices"],"limitations":["Templates are static snapshots; they don't auto-update when frameworks release new major versions","Rules may become outdated as frameworks evolve (e.g., Next.js app router vs pages router changes)","No built-in mechanism to detect framework version from package.json and auto-select appropriate rules","Templates assume common use cases; specialized or custom framework configurations may not be covered","Rules are text-based and cannot enforce constraints programmatically (e.g., no linting integration)"],"requires":["Cursor IDE","Project using the target framework (Next.js, Django, Rails, etc.)","Basic familiarity with the framework's conventions","Ability to customize template rules for project-specific needs"],"input_types":["framework name/version","customization parameters (coding style, testing framework, etc.)","project-specific overrides"],"output_types":["framework-specific .cursorrules file","structured instruction sections (style, patterns, testing, performance)","example code snippets demonstrating good patterns"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cursor-rules__cap_3","uri":"capability://code.generation.editing.team.coding.standard.enforcement.via.ai","name":"team-coding-standard-enforcement-via-ai","description":"Enables teams to encode coding standards, architectural patterns, and style guidelines into .cursorrules files that are version-controlled alongside the codebase. The rules act as a shared AI instruction set that guides all team members' code generation toward consistent patterns, reducing the need for code review cycles focused on style/convention violations. Rules can specify naming conventions, folder structures, import patterns, and architectural layers that the AI should respect.","intents":["I want all team members' AI-generated code to follow our naming conventions without manual correction","I need the AI to respect our folder structure and module organization automatically","I want to enforce architectural patterns (e.g., service layer, repository pattern) through AI guidance","I need the AI to follow our team's import/export conventions and avoid circular dependencies"],"best_for":["distributed teams needing consistent code style across time zones","organizations with strong architectural patterns to enforce","teams transitioning from manual code review to AI-assisted development","projects with complex folder structures or module organization"],"limitations":["Rules are advisory only — AI may still violate them if the instruction is ambiguous or conflicts with other guidance","No automated validation that generated code actually follows the rules; requires manual review or linting","Rules must be maintained as the codebase evolves; outdated rules may mislead AI","Difficult to enforce rules that require semantic understanding (e.g., 'avoid circular dependencies') vs syntactic rules","No built-in conflict resolution if team members disagree on standards"],"requires":["Cursor IDE for all team members",".cursorrules file committed to the repository","Team agreement on coding standards to encode","Version control system (Git) to track rule changes"],"input_types":["team coding standards documentation","architectural guidelines","style guide specifications","naming convention rules"],"output_types":["version-controlled .cursorrules file","AI-guided code generation aligned with team standards","reduced code review friction for style/convention issues"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cursor-rules__cap_4","uri":"capability://code.generation.editing.multi.language.and.polyglot.project.support","name":"multi-language-and-polyglot-project-support","description":"Supports .cursorrules files that provide language-specific and cross-language guidance for polyglot projects (e.g., frontend TypeScript + backend Python + infrastructure Terraform). Rules can specify different conventions for different file types, import patterns, and language-specific idioms, allowing a single .cursorrules file to guide AI behavior across multiple languages and frameworks within the same project. Rules can include conditional guidance based on file extension or directory context.","intents":["I want the AI to follow TypeScript conventions in /src but Python conventions in /backend","I need the AI to understand how my frontend and backend communicate (API contracts, data models)","I want language-specific testing patterns (Jest for TypeScript, pytest for Python) enforced automatically","I need the AI to respect language-specific performance constraints (e.g., bundle size for JS, memory for Python)"],"best_for":["full-stack teams with frontend and backend in different languages","microservices architectures with polyglot services","infrastructure-as-code projects mixing multiple languages (Terraform, Python, YAML)","monorepos with multiple language ecosystems"],"limitations":["Rules become complex and harder to maintain as more languages are added","No built-in mechanism to detect file language and apply language-specific rules automatically","Cross-language consistency (e.g., API contracts between frontend and backend) is difficult to enforce via text rules","Rules may conflict between languages (e.g., naming conventions that work in Python don't work in TypeScript)","Difficult to keep rules synchronized across language updates and framework changes"],"requires":["Cursor IDE","Project with multiple languages/frameworks","Understanding of conventions for each language in the project","Ability to write clear, language-specific guidance"],"input_types":["language-specific coding standards","cross-language API contracts","framework conventions for each language","file structure and organization rules"],"output_types":["polyglot .cursorrules file","language-aware AI code generation","cross-language consistency guidance"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cursor-rules__cap_5","uri":"capability://automation.workflow.rule.version.control.and.team.collaboration","name":"rule-version-control-and-team-collaboration","description":"Enables .cursorrules files to be version-controlled in Git alongside code, allowing teams to track rule changes, review rule modifications through pull requests, and maintain rule history. Rules can be updated collaboratively, with changes reviewed before deployment to the team. The Git history provides an audit trail of how AI guidance has evolved, and teams can revert to previous rule versions if needed. Rules are treated as code artifacts subject to the same review process.","intents":["I want to review rule changes before they affect the whole team's AI behavior","I need to track why and when our AI guidance changed (audit trail)","I want to revert to a previous rule version if a change causes problems","I need to see who changed the rules and why (Git blame, commit messages)"],"best_for":["teams with formal code review processes","organizations needing audit trails for AI behavior changes","projects with strict governance or compliance requirements","teams evolving their standards over time and wanting to track changes"],"limitations":["Requires Git/GitHub workflow; teams not using version control cannot leverage this capability","Rule changes apply to all team members once merged; no gradual rollout or A/B testing mechanism","No built-in conflict resolution if multiple team members edit rules simultaneously","Git history can become cluttered if rules are changed frequently without clear commit messages","No automated testing of rule changes before deployment to the team"],"requires":["Git repository for the project","GitHub or similar platform with pull request support",".cursorrules file committed to the repository","Team agreement on review process for rule changes"],"input_types":["rule modifications","commit messages explaining changes","pull request descriptions"],"output_types":["version-controlled .cursorrules file","Git history and audit trail","pull request reviews and discussions","change notifications to team"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cursor-rules__cap_6","uri":"capability://memory.knowledge.project.structure.and.architecture.documentation","name":"project-structure-and-architecture-documentation","description":"Allows .cursorrules files to document project structure, folder organization, module boundaries, and architectural patterns in a way that the AI can reference when generating code. Rules can describe the purpose of each directory, explain module dependencies, and provide examples of how different parts of the system interact. This documentation is embedded in the AI's context, enabling it to generate code that respects architectural boundaries and integrates correctly with existing modules.","intents":["I want the AI to understand my project structure and generate code in the right folders","I need the AI to respect module boundaries and not create circular dependencies","I want the AI to understand how my services/components interact and generate compatible code","I need the AI to know which files are entry points, which are utilities, and which are internal"],"best_for":["large projects with complex folder structures","teams with strict architectural patterns (layered, hexagonal, microservices)","projects with many interdependent modules","teams onboarding new developers who need to understand project structure"],"limitations":["Documentation can become outdated as project structure evolves","AI may not fully understand complex architectural relationships from text descriptions alone","No programmatic enforcement of architectural boundaries; AI may still generate code that violates them","Large projects with many modules may exceed AI context window if fully documented","Difficult to describe implicit architectural rules (e.g., 'this module should only be used by X')"],"requires":["Cursor IDE","Clear understanding of project structure and architecture","Ability to write clear descriptions of module purposes and relationships","Project with defined folder structure and module organization"],"input_types":["folder structure descriptions","module purpose and responsibility documentation","architectural pattern explanations","dependency relationship diagrams (as text)"],"output_types":["structured .cursorrules documentation","AI-aware project structure guidance","architecture-respecting code generation"],"categories":["memory-knowledge","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cursor-rules__cap_7","uri":"capability://code.generation.editing.testing.and.quality.assurance.guidance","name":"testing-and-quality-assurance-guidance","description":"Encodes testing conventions, quality standards, and testing frameworks into .cursorrules files, guiding AI to generate code with appropriate test coverage, follow testing best practices, and use the team's preferred testing tools. Rules can specify testing patterns (unit vs integration vs e2e), mocking conventions, assertion styles, and coverage expectations. The AI can then generate code with corresponding tests or suggest test cases for existing code.","intents":["I want the AI to generate code with tests, not just implementation","I need the AI to follow our testing framework (Jest, pytest, RSpec, etc.) and conventions","I want the AI to understand what should be unit tested vs integration tested","I need the AI to generate appropriate mocks and fixtures for our testing patterns"],"best_for":["teams with strong testing cultures and high coverage expectations","projects with specific testing frameworks and conventions","teams wanting to maintain test quality as code is generated","organizations with compliance or quality requirements"],"limitations":["AI-generated tests may not cover all edge cases or real-world scenarios","Rules cannot enforce test quality or coverage; generated tests may be superficial","Testing patterns vary widely; rules may not cover all testing scenarios","AI may generate tests that pass but don't actually validate behavior","Maintaining testing rules as testing frameworks evolve requires ongoing effort"],"requires":["Cursor IDE","Testing framework(s) in use (Jest, pytest, RSpec, etc.)","Clear testing conventions and patterns","Understanding of team's testing standards and expectations"],"input_types":["testing framework specifications","testing pattern examples","coverage expectations","mocking and fixture conventions"],"output_types":["test-aware code generation","generated test cases","testing pattern guidance",".cursorrules testing section"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cursor-rules__cap_8","uri":"capability://code.generation.editing.performance.and.optimization.constraints","name":"performance-and-optimization-constraints","description":"Encodes performance requirements, optimization constraints, and efficiency guidelines into .cursorrules files, guiding AI to generate code that respects performance budgets and avoids common performance pitfalls. Rules can specify constraints like bundle size limits, database query optimization patterns, memory usage expectations, and rendering performance targets. The AI can then generate code that considers these constraints and suggests optimizations.","intents":["I want the AI to avoid generating code that exceeds our bundle size budget","I need the AI to understand database query optimization and avoid N+1 queries","I want the AI to generate memory-efficient code for resource-constrained environments","I need the AI to respect rendering performance targets (e.g., Core Web Vitals)"],"best_for":["performance-critical applications (e.g., mobile, real-time systems)","teams with strict performance budgets or SLAs","projects targeting resource-constrained environments","applications with large user bases where performance impacts revenue"],"limitations":["AI cannot measure actual performance; it can only follow guidelines","Performance constraints are often context-dependent and hard to express as rules","AI may generate code that follows rules but still performs poorly in practice","Rules may conflict (e.g., code clarity vs performance optimization)","Performance requirements change as the codebase grows; rules may become outdated"],"requires":["Cursor IDE","Clear performance requirements and constraints","Understanding of performance bottlenecks in the project","Ability to express performance guidelines as text rules"],"input_types":["performance budget specifications","optimization patterns and anti-patterns","resource constraint descriptions","performance target metrics"],"output_types":["performance-aware code generation","optimization guidance","constraint-respecting implementations",".cursorrules performance section"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cursor-rules__cap_9","uri":"capability://code.generation.editing.security.and.compliance.guidance","name":"security-and-compliance-guidance","description":"Encodes security best practices, compliance requirements, and security patterns into .cursorrules files, guiding AI to generate code that follows security guidelines and avoids common vulnerabilities. Rules can specify secure coding patterns, authentication/authorization approaches, data handling requirements, and compliance constraints (e.g., GDPR, HIPAA). The AI can then generate code that respects these security and compliance requirements.","intents":["I want the AI to generate code that follows our security best practices","I need the AI to avoid generating code with common vulnerabilities (SQL injection, XSS, etc.)","I want the AI to understand our authentication and authorization patterns","I need the AI to respect data handling and privacy requirements (GDPR, HIPAA, etc.)"],"best_for":["security-sensitive applications (fintech, healthcare, government)","teams with strict compliance requirements","organizations with security-first development cultures","projects handling sensitive user data"],"limitations":["AI cannot guarantee security; rules are guidelines, not enforcement","Security vulnerabilities often require context-specific understanding","AI may generate code that follows rules but still has security issues","Security requirements change as threats evolve; rules may become outdated","Compliance requirements are complex and may not be fully expressible as text rules"],"requires":["Cursor IDE","Clear security policies and compliance requirements","Understanding of security best practices for the technology stack","Ability to express security guidelines as text rules"],"input_types":["security best practice guidelines","authentication/authorization patterns","data handling and privacy requirements","compliance constraint specifications"],"output_types":["security-aware code generation","vulnerability-avoiding patterns","compliance-respecting implementations",".cursorrules security section"],"categories":["code-generation-editing","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cursor-rules__headline","uri":"capability://tool.use.integration.project.specific.ai.instruction.repository.for.cursor.ide","name":"project-specific ai instruction repository for cursor ide","description":"A community-driven collection of .cursorrules files designed to provide AI assistants with project-specific instructions tailored to various frameworks, languages, and coding styles, enhancing their understanding of project context and conventions.","intents":["best AI instruction repository","AI rules for Cursor IDE","project-specific AI guidelines for coding","Cursor IDE customization for AI assistants","how to improve AI context understanding in projects"],"best_for":["developers using Cursor IDE","teams with specific coding conventions"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["Cursor IDE (any recent version with .cursorrules support)",".cursorrules file in repository root or project directory","Plain text editor to create/modify rules","No external dependencies or API keys required","Internet access to browse cursor.directory","Git or GitHub account to contribute rules (optional)","Cursor IDE to use downloaded rules","Basic understanding of how .cursorrules work","Cursor IDE","Clear list of approved libraries and versions"],"failure_modes":[".cursorrules files are Cursor IDE-specific; no cross-IDE portability to VS Code, JetBrains, or other editors","Plain-text format lacks schema validation — malformed rules fail silently or produce unpredictable AI behavior","No versioning mechanism — rule changes apply immediately to all team members without migration path","File size limitations may apply (typical LLM context windows); very large rule files may be truncated","Rules are loaded once at project open; dynamic rule updates require IDE restart","Rules are community-contributed with no formal review or quality guarantee — some may be outdated or ineffective","No automated testing of rules; users must manually validate that rules produce desired AI behavior","Repository growth may make discovery harder over time without robust categorization/tagging","Rules may conflict with each other if users combine multiple .cursorrules from different sources","No built-in versioning or deprecation mechanism for outdated rules","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.49999999999999994,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.548Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=cursor-rules","compare_url":"https://unfragile.ai/compare?artifact=cursor-rules"}},"signature":"4D2A1b7ngOOcnmdj1ZBibo/WZwYdD2IVUWkHGXq5DAxoSaRdgpThzdINlBI74gpMDMcj0wo8vuzQorC/qDXbBA==","signedAt":"2026-06-22T00:58:58.386Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/cursor-rules","artifact":"https://unfragile.ai/cursor-rules","verify":"https://unfragile.ai/api/v1/verify?slug=cursor-rules","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}