{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-khazp--vibe-coding-prompt-template","slug":"khazp--vibe-coding-prompt-template","name":"vibe-coding-prompt-template","type":"prompt","url":"https://vibeworkflow.app/","page_url":"https://unfragile.ai/khazp--vibe-coding-prompt-template","categories":["prompt-engineering","automation"],"tags":["ai-agents","ai-workflow","beginner-friendly","claude-code","claude-code-skills","claude-skills","dev-tools","development-workflows","llm-tools","llm-workflow","low-code","mvp-development","no-code","product-requirement-document","prompt-enginering","technical-design","vibe-coding","workflow-automation"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-khazp--vibe-coding-prompt-template__cap_0","uri":"capability://automation.workflow.five.stage.document.driven.pipeline.generation","name":"five-stage document-driven pipeline generation","description":"Implements a linear, sequential document generation pipeline that transforms application ideas into MVP code through five distinct stages (Research → PRD → Tech Design → Agent Config → Build). Each stage consumes outputs from previous stages and produces structured artifacts that feed into the next stage, with platform-agnostic AI provider selection at each step. The architecture separates documentation phases (Stages 1-4 using conversational AI) from implementation phases (Stage 5 using specialized coding agents), enabling iterative refinement and quality gates between stages.","intents":["I want to systematically transform a vague app idea into working code without jumping straight to coding","I need to generate PRDs, technical designs, and agent instructions in a structured sequence","I want to use different AI providers (Gemini, Claude, ChatGPT) at different pipeline stages based on their strengths","I need to ensure each artifact builds on previous outputs to maintain consistency across research, requirements, and design"],"best_for":["non-technical founders and product managers building MVPs without engineering teams","solo developers who want structured planning before coding","teams migrating from manual specification writing to AI-assisted documentation","builders using AI coding agents (Cursor, Claude Code) who need pre-structured context"],"limitations":["Linear pipeline design means backtracking to earlier stages requires re-running all downstream stages","Quality of Stage 5 output depends heavily on quality of Stage 1-4 artifacts; garbage-in-garbage-out risk if early stages are poorly prompted","No built-in feedback loops or validation gates between stages — relies on manual review to catch specification errors before implementation","Pipeline assumes conversational AI platforms have sufficient context window to consume all previous artifacts; very large projects may exceed token limits"],"requires":["API access to at least one conversational AI platform (Gemini 3 Pro, Claude Sonnet, or ChatGPT)","Access to an AI coding agent for Stage 5 (Cursor, Claude Code, or equivalent)","Ability to copy-paste prompts and outputs between platforms (or integration with automation tools)","Basic understanding of how to structure application ideas into research questions"],"input_types":["natural language application idea or concept","optional existing research notes or competitive analysis","optional PRD or technical specification fragments"],"output_types":["research-YourApp.txt (market analysis, competitive landscape, technical feasibility)","PRD-YourApp-MVP.md (product requirements, user stories, feature scope)","TechDesign-YourApp-MVP.md (architecture, technology stack, implementation approach)","AGENTS.md + agent_docs/ directory (machine-actionable agent instructions)","working application code (from Stage 5 implementation)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-khazp--vibe-coding-prompt-template__cap_1","uri":"capability://memory.knowledge.progressive.disclosure.context.management.for.ai.agents","name":"progressive disclosure context management for ai agents","description":"Implements a layered information architecture that decomposes comprehensive project documentation into progressively detailed files (.cursorrules, CLAUDE.md, agent_docs/ subdirectories) to manage AI context window limitations. The system uses a hierarchical disclosure pattern where tool config files serve as entry points with essential context, while detailed specifications are stored in separate files that agents can selectively load based on task requirements. This prevents context overflow while maintaining information accessibility for multi-file, multi-step implementation tasks.","intents":["I need to provide comprehensive project context to AI agents without exceeding their token limits","I want AI agents to access detailed specifications only when needed, keeping their working context lean","I need different AI tools (Cursor, Claude Code, VS Code extensions) to understand the same project with tool-specific configuration files","I want to avoid re-explaining the entire project architecture every time I ask an agent to implement a new feature"],"best_for":["developers using context-window-limited AI coding agents (Cursor, Claude Code with 100K-200K token limits)","teams building multi-file applications where full specification exceeds a single AI context window","builders who need tool-specific configurations (.cursorrules for Cursor, CLAUDE.md for Claude Code, etc.)","projects requiring selective context loading based on implementation task scope"],"limitations":["Requires manual file organization and naming conventions; no automatic context discovery or indexing","AI agents must be explicitly instructed which files to load; no built-in mechanism for agents to determine relevant context","Layered structure adds complexity to documentation maintenance — changes to core specifications must be propagated across multiple files","Progressive disclosure pattern assumes agents can handle file references and selective loading; older or simpler AI tools may not support this workflow"],"requires":["AI coding agent that supports file-based context loading (Cursor, Claude Code, or similar)","Ability to organize documentation into hierarchical directory structures (agent_docs/, tool configs)","Understanding of target AI agent's context window size and token counting","Tool-specific knowledge for creating configuration files (.cursorrules syntax, CLAUDE.md format, etc.)"],"input_types":["comprehensive project documentation (PRD, technical design, architecture notes)","tool-specific configuration requirements"],"output_types":[".cursorrules file (Cursor-specific configuration)","CLAUDE.md file (Claude Code configuration)","agent_docs/ directory structure with modular specification files","tool-specific config files (environment setup, dependency lists, etc.)"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-khazp--vibe-coding-prompt-template__cap_10","uri":"capability://safety.moderation.visual.verification.workflows.with.self.healing.tests","name":"visual verification workflows with self-healing tests","description":"Implements visual verification workflows where AI agents generate test cases and verification steps that can be manually executed or automated, with self-healing test patterns that automatically adapt to minor implementation changes. The system generates test specifications and visual verification steps (UI screenshots, API response validation, data model verification) that enable non-technical stakeholders to validate implementation without code review. Self-healing tests use pattern matching and semantic comparison rather than brittle exact matching, allowing tests to adapt to minor code changes.","intents":["I want to verify that AI-generated code actually works without doing code review","I need non-technical stakeholders to validate implementation through visual verification","I want tests that don't break every time I make minor code changes","I need to catch implementation bugs before they reach production"],"best_for":["teams with non-technical stakeholders who need to validate implementation","projects where visual verification is more important than code quality metrics","MVPs where quick validation is more important than comprehensive test coverage","teams using AI agents where test generation and validation are critical quality gates"],"limitations":["Visual verification workflows are manual process; requires human execution or automation infrastructure","Self-healing tests require careful pattern design; overly flexible patterns may miss real bugs","Test generation quality depends on specification clarity; vague requirements produce vague tests","Visual verification doesn't catch performance issues, security vulnerabilities, or architectural problems"],"requires":["Completion of Stage 5 (implementation) or working application code","Test specification generation (manual or AI-assisted)","Ability to execute visual verification steps (manual or automated)","Understanding of self-healing test patterns and when to apply them"],"input_types":["implementation code and running application","test specifications and verification steps"],"output_types":["test results and verification reports","visual verification evidence (screenshots, API responses, etc.)","self-healing test patterns and adaptations"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-khazp--vibe-coding-prompt-template__cap_11","uri":"capability://automation.workflow.per.prompt.execution.refinement.architecture.for.iterative.improvement","name":"per (prompt-execution-refinement) architecture for iterative improvement","description":"Implements a Prompt-Execution-Refinement (PER) architecture that enables iterative improvement of AI-generated artifacts through structured feedback loops. The system captures execution results (code output, specification clarity, implementation success) and uses them to refine prompts and instructions for subsequent iterations. This creates a feedback mechanism where each stage's output informs improvements to that stage's prompt template, enabling continuous optimization of the workflow without manual intervention.","intents":["I want to improve my workflow based on what actually works in practice","I need to capture lessons learned from each project and apply them to future projects","I want AI-generated artifacts to improve over time as I run more projects","I need to identify which prompt templates and instructions produce the best results"],"best_for":["teams running multiple projects using the workflow who want to optimize over time","organizations building internal AI-assisted development practices","builders who want to measure and improve workflow effectiveness","teams with dedicated resources to analyze and refine prompt templates"],"limitations":["PER architecture requires systematic data collection and analysis; adds overhead to each project","Feedback loops are manual process; no automatic prompt optimization","Improvements are incremental; significant workflow changes require deliberate redesign","Requires discipline to consistently capture and analyze execution results across projects"],"requires":["Systematic execution of workflow across multiple projects","Data collection infrastructure to capture results and feedback","Analysis capability to identify patterns and improvement opportunities","Discipline to test and validate prompt refinements before deploying to production"],"input_types":["execution results from completed projects","feedback on artifact quality and usefulness","implementation success metrics"],"output_types":["refined prompt templates based on execution feedback","improved instructions and guidelines","documented lessons learned and best practices"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-khazp--vibe-coding-prompt-template__cap_2","uri":"capability://tool.use.integration.multi.platform.ai.provider.selection.and.orchestration","name":"multi-platform ai provider selection and orchestration","description":"Enables users to select different AI providers (Gemini 3 Pro, Claude Sonnet, ChatGPT) at each pipeline stage based on provider strengths, cost, or availability, without modifying the underlying workflow structure. The system maintains platform-agnostic prompt templates that can be executed on any conversational AI platform, allowing Stage 1 to use Gemini for research, Stage 2-3 to use Claude for specification writing, and Stage 5 to use specialized coding agents. This decouples the workflow logic from specific AI provider implementations.","intents":["I want to use the best AI provider for each task (e.g., Gemini for research, Claude for design, Cursor for coding)","I need to optimize costs by using cheaper providers for some stages and premium providers for others","I want to avoid vendor lock-in by keeping my workflow portable across AI platforms","I need to switch providers mid-project if one becomes unavailable or performs poorly on a specific task"],"best_for":["cost-conscious builders who want to optimize AI spending across multiple providers","teams evaluating different AI platforms and wanting to test them in a structured workflow","developers building on open-source or self-hosted AI models who need provider flexibility","organizations with existing relationships with multiple AI vendors (Google Cloud, Anthropic, OpenAI)"],"limitations":["Prompt templates must be manually adapted for provider-specific syntax differences (Claude's thinking tags vs ChatGPT's reasoning, etc.)","No automatic provider selection based on task type; users must manually choose providers at each stage","Output format consistency is not guaranteed across providers — some may produce more detailed PRDs, others more concise designs","Switching providers mid-pipeline may require re-running stages if output formats diverge significantly"],"requires":["API keys or access to at least one conversational AI platform (Gemini, Claude, ChatGPT, or equivalent)","Understanding of each provider's pricing model and rate limits","Ability to copy-paste prompts between platforms or use API-based automation","Knowledge of provider-specific prompt formatting and output expectations"],"input_types":["platform-agnostic prompt templates (markdown files with stage-specific instructions)"],"output_types":["structured artifacts compatible with downstream stages, regardless of source provider"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-khazp--vibe-coding-prompt-template__cap_3","uri":"capability://text.generation.language.structured.prd.generation.with.mvp.scope.definition","name":"structured prd generation with mvp scope definition","description":"Generates product requirement documents (PRDs) that explicitly define MVP scope, feature prioritization, and user stories through a guided prompt template (part2-prd-mvp.md) that consumes research artifacts from Stage 1. The system produces PRD-YourApp-MVP.md with structured sections for product vision, user personas, feature requirements, acceptance criteria, and MVP boundaries, enabling downstream technical design to focus on implementable scope rather than aspirational features. This prevents scope creep by explicitly documenting what is and is not included in the MVP.","intents":["I need to define MVP scope clearly so technical design focuses on what's actually buildable","I want to generate a professional PRD without hiring a product manager","I need to document user stories and acceptance criteria for AI agents to understand feature requirements","I want to prevent scope creep by explicitly defining MVP boundaries and out-of-scope features"],"best_for":["solo founders and non-technical builders who need professional PRDs but lack product management experience","teams building MVPs with limited scope and timeline","developers who want AI agents to understand feature requirements in structured format","projects where clear MVP definition is critical to avoid over-engineering"],"limitations":["PRD quality depends heavily on research artifacts from Stage 1; weak research produces weak PRDs","No built-in user validation — generated PRDs reflect AI interpretation of market needs, not actual user feedback","Difficult to incorporate existing product roadmaps or stakeholder requirements; template assumes greenfield projects","MVP scope definition is AI-generated and may not align with actual business constraints or market realities"],"requires":["Completion of Stage 1 (research artifacts) or manual research notes","Access to conversational AI platform (any provider works)","Clear application idea or concept to base PRD on","Understanding of what constitutes MVP vs full product"],"input_types":["research-YourApp.txt (from Stage 1) or manual research notes","optional existing product vision or feature list"],"output_types":["PRD-YourApp-MVP.md with sections: product vision, user personas, feature requirements, acceptance criteria, MVP scope, out-of-scope features"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-khazp--vibe-coding-prompt-template__cap_4","uri":"capability://text.generation.language.technical.design.generation.with.architecture.and.stack.selection","name":"technical design generation with architecture and stack selection","description":"Generates technical design documents (TechDesign-YourApp-MVP.md) that specify system architecture, technology stack, implementation approach, and technical constraints through a guided prompt template (part3-tech-design-mvp.md) that consumes PRD and research artifacts. The system produces structured technical designs with sections for architecture diagrams (as ASCII or descriptions), technology choices with justifications, data models, API specifications, and implementation roadmap, enabling AI coding agents to understand the intended technical approach before implementation. This bridges the gap between product requirements and code generation.","intents":["I need to define technical architecture before asking AI agents to code, so they understand the intended design","I want to document technology stack choices and justify why specific frameworks/languages were selected","I need to specify data models, API contracts, and system boundaries so AI agents can implement consistently","I want to identify technical constraints and dependencies before implementation begins"],"best_for":["developers who want to guide AI coding agents with explicit architectural decisions","teams building systems with specific technical constraints (performance, scalability, compliance)","projects where technology stack selection is critical to project success","builders who want to document architectural decisions for future reference and team alignment"],"limitations":["Technical design quality depends on PRD clarity; vague requirements produce vague designs","AI-generated designs may not account for operational concerns (monitoring, logging, deployment) unless explicitly prompted","No validation that proposed architecture is actually implementable within project constraints; requires manual review","Technology stack recommendations are AI-generated and may not reflect latest best practices or team expertise"],"requires":["Completion of Stage 2 (PRD) or manual product requirements","Access to conversational AI platform with strong reasoning capabilities (Claude Sonnet or equivalent)","Basic understanding of software architecture patterns and technology stacks","Knowledge of project constraints (performance requirements, scalability needs, team expertise)"],"input_types":["PRD-YourApp-MVP.md (from Stage 2)","research-YourApp.txt (from Stage 1)","optional existing technical constraints or architecture preferences"],"output_types":["TechDesign-YourApp-MVP.md with sections: architecture overview, technology stack, data models, API specifications, implementation roadmap, technical constraints"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-khazp--vibe-coding-prompt-template__cap_5","uri":"capability://tool.use.integration.agent.instruction.generation.with.tool.configuration","name":"agent instruction generation with tool configuration","description":"Transforms human-readable documentation (PRD, technical design) into machine-actionable agent instructions through a guided prompt template (part4-notes-for-agent.md) that generates AGENTS.md, agent_docs/ directory structure, and tool-specific configuration files (.cursorrules, CLAUDE.md, etc.). The system decomposes comprehensive specifications into modular instruction files organized by feature or component, enabling AI coding agents to understand project context, implementation approach, and tool-specific requirements without exceeding context windows. This stage acts as a transformation hub that converts documentation into agent-consumable format.","intents":["I need to convert my PRD and technical design into instructions that AI coding agents can actually follow","I want to create tool-specific configurations (.cursorrules for Cursor, CLAUDE.md for Claude Code) without manual file editing","I need to decompose large projects into modular instruction files so agents can load only relevant context","I want to specify implementation patterns, code style, and tool-specific requirements for AI agents to follow"],"best_for":["developers using AI coding agents (Cursor, Claude Code) who need structured context","teams building multi-file applications where comprehensive context exceeds agent token limits","builders who want to enforce consistent code style and implementation patterns across AI-generated code","projects requiring tool-specific configurations for different AI agents"],"limitations":["Instruction generation quality depends on technical design clarity; vague designs produce vague instructions","No automatic validation that generated instructions are actually followable by target AI agents","Tool-specific configurations may require manual adjustment based on actual agent behavior and capabilities","Modular instruction structure requires careful organization; poor decomposition can make context navigation difficult for agents"],"requires":["Completion of Stage 3 (technical design) or manual technical specifications","Access to conversational AI platform for instruction generation","Knowledge of target AI agent's capabilities and configuration format (.cursorrules syntax, CLAUDE.md structure, etc.)","Understanding of how to decompose projects into modular instruction files"],"input_types":["TechDesign-YourApp-MVP.md (from Stage 3)","PRD-YourApp-MVP.md (from Stage 2)","optional existing code style guides or implementation patterns"],"output_types":["AGENTS.md (master plan with project overview and implementation strategy)","agent_docs/ directory with modular instruction files (features/, components/, patterns/, etc.)",".cursorrules file (Cursor-specific configuration)","CLAUDE.md file (Claude Code configuration)","tool-specific config files (environment setup, dependencies, etc.)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-khazp--vibe-coding-prompt-template__cap_6","uri":"capability://safety.moderation.quality.control.through.verification.echo.pattern","name":"quality control through verification echo pattern","description":"Implements a quality assurance mechanism where AI agents verify their understanding of specifications by echoing back key requirements, constraints, and implementation decisions before proceeding with code generation. The verification echo pattern uses structured prompts that ask agents to summarize their understanding of the project scope, technical approach, and acceptance criteria, enabling human review to catch specification misunderstandings before implementation begins. This creates a feedback loop that validates specification clarity without requiring formal testing infrastructure.","intents":["I want to verify that AI agents actually understand my specifications before they start coding","I need to catch specification ambiguities or misinterpretations early, before implementation begins","I want a lightweight quality gate that doesn't require formal testing or code review infrastructure","I need to ensure AI agents are aligned on project scope and technical approach before implementation"],"best_for":["solo developers and small teams who lack formal QA processes","projects where specification clarity is critical to avoid rework","builders using AI coding agents who want to validate agent understanding before implementation","teams building MVPs with tight timelines who need fast feedback loops"],"limitations":["Verification echo is manual process; requires human review of agent summaries to be effective","No automatic detection of specification gaps; relies on human judgment to identify misunderstandings","Echo verification adds 10-15 minutes per project stage; increases total pipeline duration","Agents may produce plausible-sounding echoes that mask actual misunderstandings; requires careful human review"],"requires":["Completion of Stage 4 (agent instructions) or manual specification documents","Access to AI agent or conversational AI platform for verification prompts","Human reviewer with domain knowledge to evaluate agent understanding","Time for manual review of verification echoes (10-15 minutes per stage)"],"input_types":["agent instructions (AGENTS.md, agent_docs/)","technical design and PRD documents"],"output_types":["verification echo summary (agent's understanding of project scope, technical approach, acceptance criteria)","human review notes identifying specification gaps or misunderstandings"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-khazp--vibe-coding-prompt-template__cap_7","uri":"capability://automation.workflow.user.type.specific.workflow.paths.with.progressive.disclosure","name":"user type-specific workflow paths with progressive disclosure","description":"Implements three distinct user type paths (Vibe-Coder, Developer, In-Between) that customize the workflow complexity and documentation depth based on user expertise and project needs. The system uses progressive disclosure to show different levels of detail: Vibe-Coders see simplified prompts and high-level guidance, Developers see technical depth and architecture details, and In-Between users see balanced documentation. Each path maintains the same five-stage pipeline but adjusts prompt complexity, output verbosity, and technical depth to match user capabilities and project requirements.","intents":["I want a simplified workflow that doesn't overwhelm me with technical details","I need detailed technical documentation and architectural guidance for complex projects","I want to balance simplicity with technical depth based on my current project needs","I need the workflow to adapt to my skill level without losing important information"],"best_for":["non-technical founders and product managers (Vibe-Coder path)","experienced developers building complex systems (Developer path)","teams with mixed skill levels who need flexible documentation depth","builders who want to grow from Vibe-Coder to Developer path as their expertise increases"],"limitations":["User type selection is manual; no automatic detection of user skill level or project complexity","Simplified paths may omit important technical details that become critical later in development","Detailed paths may overwhelm less technical users with unnecessary complexity","Switching between paths mid-project requires re-running stages with different prompt templates"],"requires":["Self-assessment of user type (Vibe-Coder, Developer, or In-Between)","Understanding of project complexity and technical requirements","Access to appropriate prompt templates for selected user type path"],"input_types":["user type selection (Vibe-Coder, Developer, In-Between)","project complexity assessment"],"output_types":["user-type-specific prompt templates","documentation with appropriate technical depth","guidance tailored to user expertise level"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-khazp--vibe-coding-prompt-template__cap_8","uri":"capability://safety.moderation.anti.vibe.engineering.rules.and.meta.cognition.validation","name":"anti-vibe engineering rules and meta-cognition validation","description":"Enforces a set of quality standards and anti-patterns (Anti-Vibe Engineering Rules) that prevent common pitfalls in AI-assisted development, such as over-engineering, premature optimization, and scope creep. The system includes meta-cognition rules that prompt AI agents and users to reflect on their decision-making process, validate assumptions, and identify potential issues before implementation. These rules are embedded in prompts and verification steps to ensure the workflow produces pragmatic, implementable solutions rather than over-engineered designs.","intents":["I want to avoid over-engineering and premature optimization in my MVP","I need to validate my assumptions about technical approach before implementation","I want AI agents to think critically about design decisions rather than blindly following specifications","I need to ensure my project stays focused on MVP scope and doesn't expand into unnecessary features"],"best_for":["teams building MVPs with tight timelines who need to avoid over-engineering","projects where pragmatism and speed are more important than architectural perfection","builders who want AI agents to validate assumptions and identify potential issues","teams that have experienced scope creep or over-engineering in previous projects"],"limitations":["Anti-vibe rules are guidelines, not enforced constraints; require human judgment to apply effectively","Meta-cognition validation adds time to the workflow (5-10 minutes per stage) without guaranteeing better outcomes","Rules may conflict with legitimate technical requirements in some projects; requires context-aware application","Effectiveness depends on user understanding and commitment to pragmatic development approach"],"requires":["Understanding of anti-vibe engineering principles and when to apply them","Commitment to pragmatic development approach focused on MVP delivery","Time for meta-cognition validation steps (5-10 minutes per stage)","Human judgment to balance pragmatism with legitimate technical requirements"],"input_types":["project specifications and design documents","implementation decisions and technical approach"],"output_types":["validated specifications with over-engineering risks identified","implementation approach with assumptions documented and validated"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-khazp--vibe-coding-prompt-template__cap_9","uri":"capability://tool.use.integration.model.context.protocol.mcp.integration.for.tool.extension","name":"model context protocol (mcp) integration for tool extension","description":"Supports integration with Model Context Protocol (MCP) for extending AI agent capabilities with custom tools, databases, and external services. The system enables agents to access specialized tools (code search, database queries, API calls) through standardized MCP interfaces, allowing agents to gather additional context or perform actions beyond text generation. This enables more sophisticated agent behaviors like querying existing codebases, accessing documentation systems, or integrating with external APIs during implementation.","intents":["I want AI agents to access my existing codebase or documentation during implementation","I need agents to query databases or external APIs to understand data models and integrations","I want to extend agent capabilities with custom tools specific to my project or organization","I need agents to perform actions beyond text generation, like code search or documentation lookup"],"best_for":["teams with existing codebases who want agents to understand code patterns and conventions","projects requiring integration with external APIs or databases","organizations with custom tools or documentation systems that agents need to access","advanced users who want to extend agent capabilities beyond standard text generation"],"limitations":["MCP integration requires custom tool implementation; not available out-of-the-box","Tool implementation complexity depends on target system (simple for REST APIs, complex for custom databases)","No built-in MCP tools provided; users must implement tools specific to their needs","MCP support varies by AI agent; not all agents have full MCP implementation"],"requires":["Understanding of Model Context Protocol (MCP) specification and implementation","Ability to implement custom MCP tools for target systems","AI agent with MCP support (Claude Code, Cursor with extensions, etc.)","Access to systems that tools need to integrate with (codebases, databases, APIs)"],"input_types":["MCP tool specifications and implementations","external system access credentials and documentation"],"output_types":["extended agent capabilities through MCP tools","agent access to external systems and custom tools"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":35,"verified":false,"data_access_risk":"high","permissions":["API access to at least one conversational AI platform (Gemini 3 Pro, Claude Sonnet, or ChatGPT)","Access to an AI coding agent for Stage 5 (Cursor, Claude Code, or equivalent)","Ability to copy-paste prompts and outputs between platforms (or integration with automation tools)","Basic understanding of how to structure application ideas into research questions","AI coding agent that supports file-based context loading (Cursor, Claude Code, or similar)","Ability to organize documentation into hierarchical directory structures (agent_docs/, tool configs)","Understanding of target AI agent's context window size and token counting","Tool-specific knowledge for creating configuration files (.cursorrules syntax, CLAUDE.md format, etc.)","Completion of Stage 5 (implementation) or working application code","Test specification generation (manual or AI-assisted)"],"failure_modes":["Linear pipeline design means backtracking to earlier stages requires re-running all downstream stages","Quality of Stage 5 output depends heavily on quality of Stage 1-4 artifacts; garbage-in-garbage-out risk if early stages are poorly prompted","No built-in feedback loops or validation gates between stages — relies on manual review to catch specification errors before implementation","Pipeline assumes conversational AI platforms have sufficient context window to consume all previous artifacts; very large projects may exceed token limits","Requires manual file organization and naming conventions; no automatic context discovery or indexing","AI agents must be explicitly instructed which files to load; no built-in mechanism for agents to determine relevant context","Layered structure adds complexity to documentation maintenance — changes to core specifications must be propagated across multiple files","Progressive disclosure pattern assumes agents can handle file references and selective loading; older or simpler AI tools may not support this workflow","Visual verification workflows are manual process; requires human execution or automation infrastructure","Self-healing tests require careful pattern design; overly flexible patterns may miss real bugs","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.2752946217646374,"quality":0.34,"ecosystem":0.7000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.15,"quality":0.25,"ecosystem":0.1,"match_graph":0.45,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:59:55.148Z","last_commit":"2026-04-19T22:22:18Z"},"community":{"stars":2334,"forks":294,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=khazp--vibe-coding-prompt-template","compare_url":"https://unfragile.ai/compare?artifact=khazp--vibe-coding-prompt-template"}},"signature":"hPxTX3ZfV/3XkjKRPr/yFZFftA626fVULIa/rHg1fR9jwMJgoIpMtaEg1h6v7YY2Zi7t7pbkUB72TzGzDFAPDw==","signedAt":"2026-06-20T13:28:24.491Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/khazp--vibe-coding-prompt-template","artifact":"https://unfragile.ai/khazp--vibe-coding-prompt-template","verify":"https://unfragile.ai/api/v1/verify?slug=khazp--vibe-coding-prompt-template","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"}}