{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_promptboom","slug":"promptboom","name":"PromptBoom","type":"product","url":"https://promptboom.com","page_url":"https://unfragile.ai/promptboom","categories":["text-writing"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_promptboom__cap_0","uri":"capability://text.generation.language.seo.optimized.prompt.template.generation","name":"seo-optimized prompt template generation","description":"Generates pre-built prompt templates specifically engineered for SEO-focused content tasks (keyword targeting, meta descriptions, title optimization, content briefs). The system likely uses a template library indexed by SEO intent patterns and keyword density heuristics, allowing users to select a content type and automatically populate prompt structures that bias AI outputs toward search-engine-friendly characteristics without manual prompt crafting.","intents":["I need to generate 50 product descriptions that rank for specific keywords without writing each prompt manually","I want my AI-generated blog outlines to naturally incorporate target keywords without sounding forced","I need meta descriptions that hit character limits and include primary keywords consistently"],"best_for":["SEO-focused content marketers managing high-volume content production","Agencies running multiple client campaigns with keyword-driven requirements","Solo content creators who lack prompt engineering expertise but need SEO compliance"],"limitations":["Templates are static and don't adapt to evolving search intent or SERP dynamics in real-time","No integration with actual SEO tools (Ahrefs, SEMrush) to pull live keyword data, requiring manual keyword input","Limited to English-language SEO patterns; non-English markets may see reduced effectiveness"],"requires":["Target keywords or content topic as input","Access to an AI generation platform (ChatGPT, Claude, etc.) to execute the generated prompts","Basic understanding of SEO fundamentals to select appropriate template"],"input_types":["text (content type selection, target keywords, brand voice preferences)"],"output_types":["text (structured prompt template ready for copy-paste into AI tools)"],"categories":["text-generation-language","seo-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_promptboom__cap_1","uri":"capability://text.generation.language.prompt.quality.scoring.and.optimization.feedback","name":"prompt quality scoring and optimization feedback","description":"Analyzes user-submitted prompts against a quality rubric (likely measuring clarity, specificity, constraint definition, and output format specification) and provides actionable feedback to improve prompt effectiveness. The system probably uses pattern matching or lightweight NLP to detect common prompt anti-patterns (vague instructions, missing context, undefined output format) and suggests specific rewrites that increase AI model compliance and output consistency.","intents":["I want to understand why my prompts produce inconsistent outputs and how to fix them","I need to audit a batch of prompts my team wrote to ensure they're high-quality before running them at scale","I want to learn prompt engineering best practices by seeing how my prompts compare to optimized versions"],"best_for":["Teams scaling AI content production who need quality gates before batch execution","Prompt engineers and AI practitioners learning to improve their craft","Content agencies managing prompts across multiple clients and need consistency standards"],"limitations":["Scoring rubric is likely generic and may not account for domain-specific prompt requirements (medical, legal, technical writing)","Feedback is prescriptive rather than explanatory—doesn't teach underlying reasoning for why a prompt is weak","No feedback loop to learn from user outcomes; scoring doesn't improve based on which prompts actually produce better results"],"requires":["A prompt text (any length, any topic)","Optional: target AI model type (GPT-4, Claude, Llama) for model-specific optimization suggestions"],"input_types":["text (raw prompt to be analyzed)"],"output_types":["text (scored feedback with specific rewrite suggestions and quality metrics)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_promptboom__cap_2","uri":"capability://text.generation.language.content.type.specific.prompt.library.with.customization","name":"content-type-specific prompt library with customization","description":"Maintains a curated library of pre-optimized prompts organized by content type (blog posts, product descriptions, email campaigns, social media, landing pages, etc.) with built-in customization fields for brand voice, tone, target audience, and keyword insertion. Users browse the library, select a template, fill in context-specific variables, and receive a ready-to-use prompt that can be immediately pasted into their AI tool of choice.","intents":["I need a prompt for generating product descriptions but don't want to write one from scratch","I want to maintain consistent brand voice across all AI-generated content by using standardized prompts","I need to quickly generate 10 different email subject line prompts without manually engineering each one"],"best_for":["Non-technical content creators and marketers who lack prompt engineering skills","Teams needing standardized prompts to ensure consistency across multiple content creators","Agencies managing multiple client brands with different voice/tone requirements"],"limitations":["Library is static and curated by PromptBoom; users cannot easily add custom templates or contribute improvements","Customization is limited to simple variable substitution (brand name, keywords, tone) rather than conditional logic or dynamic prompt branching","No version control or A/B testing framework to compare which prompt variants produce better outputs"],"requires":["Selection of content type from available library categories","Input of customization variables (brand name, target keywords, tone, audience)","Access to an external AI tool to execute the generated prompt"],"input_types":["text (content type selection, customization variables like brand voice, keywords, target audience)"],"output_types":["text (fully customized prompt ready for use in external AI tools)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_promptboom__cap_3","uri":"capability://data.processing.analysis.batch.prompt.optimization.and.multi.prompt.comparison","name":"batch prompt optimization and multi-prompt comparison","description":"Accepts multiple prompts at once (e.g., a CSV or list of prompts) and applies optimization scoring and rewrite suggestions across the batch, enabling users to identify weak prompts at scale and compare alternative versions side-by-side. The system likely processes each prompt through the quality rubric, ranks them by score, and highlights which prompts would benefit most from revision before batch execution against an AI model.","intents":["I have 50 prompts my team wrote and need to identify which ones are low-quality before we run them at scale","I want to compare 3 different versions of the same prompt to see which one is most optimized","I need to audit all prompts in our content pipeline to ensure they meet minimum quality standards"],"best_for":["Content agencies and teams managing large prompt inventories across multiple projects","Prompt engineers optimizing prompt performance before expensive batch inference runs","QA teams implementing quality gates in AI content production workflows"],"limitations":["Batch processing speed depends on library size; no indication of whether processing is parallelized or sequential","Comparison view is limited to scoring metrics; no actual output comparison (i.e., doesn't run prompts against AI models to show quality differences)","No integration with prompt versioning or git-like history tracking for iterative improvements"],"requires":["Multiple prompts in supported format (CSV, JSON, plain text list, or direct paste)","Optional: metadata tags or grouping to organize batch results"],"input_types":["text (multiple prompts in batch format)"],"output_types":["structured data (scored prompts with rankings, comparison matrix, optimization recommendations)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_promptboom__cap_4","uri":"capability://data.processing.analysis.prompt.to.output.quality.correlation.tracking","name":"prompt-to-output quality correlation tracking","description":"Optionally integrates with user AI tool outputs to track which optimized prompts actually produce better results, creating a feedback loop where prompt quality scores are validated against real-world output quality. The system may accept user feedback (ratings, manual quality assessments) on generated content and correlate it back to the original prompt characteristics, enabling data-driven refinement of the quality rubric and template recommendations over time.","intents":["I want to know if the prompts PromptBoom optimized actually produced better content than my original prompts","I need to understand which prompt characteristics correlate with high-quality outputs for my specific use case","I want PromptBoom to learn from my content quality feedback to improve its recommendations over time"],"best_for":["Data-driven teams with the infrastructure to track and measure content quality systematically","Agencies running high-volume content production with clear quality metrics (engagement, conversion, SEO ranking)","Prompt engineers building proprietary prompt optimization models for their organization"],"limitations":["Requires manual quality feedback or integration with external quality measurement tools; no automatic output quality detection","Correlation analysis requires sufficient data volume (likely 50+ prompt-output pairs) to be statistically meaningful","Privacy concerns if users share actual generated content with PromptBoom for analysis","Unknown whether feedback loop is implemented; may be a planned feature rather than current capability"],"requires":["Generated outputs from AI tools (text, structured data, or quality scores)","User feedback mechanism (ratings, quality assessments, or integration with external QA tools)","Sufficient historical data (50+ prompt-output pairs) for correlation analysis"],"input_types":["text (generated outputs from AI tools)","structured data (quality scores, user ratings, engagement metrics)"],"output_types":["structured data (correlation analysis, prompt characteristic rankings, personalized recommendations)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_promptboom__cap_5","uri":"capability://text.generation.language.multi.model.prompt.adaptation.and.compatibility.checking","name":"multi-model prompt adaptation and compatibility checking","description":"Analyzes prompts for compatibility with different AI models (GPT-4, Claude, Llama, Gemini, etc.) and suggests model-specific optimizations or rewrites. The system likely maintains a knowledge base of model-specific behaviors (instruction-following strengths, output format preferences, token limits) and flags prompts that may not work well with certain models, or automatically generates model-specific variants of the same prompt.","intents":["I want to know if my prompt will work with Claude or if I need to rewrite it for better results","I need to generate variants of my prompt optimized for both GPT-4 and open-source Llama models","I want to check if my prompt respects token limits for the model I'm using"],"best_for":["Teams using multiple AI models and needing to maintain prompt consistency across them","Developers building multi-model applications who need to optimize prompts per model","Cost-conscious teams evaluating cheaper open-source models and needing prompt adaptation"],"limitations":["Model-specific knowledge base requires constant updates as new models are released and model behaviors change","Adaptation suggestions are likely heuristic-based rather than empirically tested; no guarantee that adapted prompts actually perform better","Limited to models PromptBoom has explicitly trained on; custom or proprietary models are not supported"],"requires":["Target AI model(s) specified by user","Prompt text to be analyzed","Optional: model-specific constraints (token limit, API version)"],"input_types":["text (prompt to be analyzed)","text (target model name or list of models)"],"output_types":["text (compatibility assessment, model-specific optimization suggestions, or adapted prompt variants)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_promptboom__cap_6","uri":"capability://automation.workflow.prompt.versioning.and.iteration.history","name":"prompt versioning and iteration history","description":"Maintains a version history of prompts as users iterate and refine them, allowing users to track changes, revert to previous versions, and compare different iterations side-by-side. The system likely stores metadata about each version (timestamp, quality score, user notes, performance metrics if available) and enables branching to explore multiple optimization paths without losing the original.","intents":["I want to see how my prompt has evolved over 10 iterations and compare the original to the current version","I accidentally deleted a prompt variant that was working well; I need to restore it from history","I want to branch my prompt into two different versions to test different approaches without overwriting the original"],"best_for":["Prompt engineers and teams iterating on prompts over time","Organizations needing audit trails for prompt changes (compliance, quality assurance)","Collaborative teams where multiple people edit the same prompts"],"limitations":["Version history is likely stored in PromptBoom's database; no export to external version control (git) for integration with development workflows","Branching and merging capabilities are unknown; may be limited to simple linear history","No indication of whether version history is preserved if a user deletes a prompt"],"requires":["Prompts created and edited within PromptBoom (not imported from external sources)","User account with persistent storage"],"input_types":["text (prompt edits and refinements)"],"output_types":["text (version history with metadata, side-by-side comparison views, restored versions)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":37,"verified":false,"data_access_risk":"high","permissions":["Target keywords or content topic as input","Access to an AI generation platform (ChatGPT, Claude, etc.) to execute the generated prompts","Basic understanding of SEO fundamentals to select appropriate template","A prompt text (any length, any topic)","Optional: target AI model type (GPT-4, Claude, Llama) for model-specific optimization suggestions","Selection of content type from available library categories","Input of customization variables (brand name, target keywords, tone, audience)","Access to an external AI tool to execute the generated prompt","Multiple prompts in supported format (CSV, JSON, plain text list, or direct paste)","Optional: metadata tags or grouping to organize batch results"],"failure_modes":["Templates are static and don't adapt to evolving search intent or SERP dynamics in real-time","No integration with actual SEO tools (Ahrefs, SEMrush) to pull live keyword data, requiring manual keyword input","Limited to English-language SEO patterns; non-English markets may see reduced effectiveness","Scoring rubric is likely generic and may not account for domain-specific prompt requirements (medical, legal, technical writing)","Feedback is prescriptive rather than explanatory—doesn't teach underlying reasoning for why a prompt is weak","No feedback loop to learn from user outcomes; scoring doesn't improve based on which prompts actually produce better results","Library is static and curated by PromptBoom; users cannot easily add custom templates or contribute improvements","Customization is limited to simple variable substitution (brand name, keywords, tone) rather than conditional logic or dynamic prompt branching","No version control or A/B testing framework to compare which prompt variants produce better outputs","Batch processing speed depends on library size; no indication of whether processing is parallelized or sequential","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.2833333333333333,"quality":0.63,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:32.438Z","last_scraped_at":"2026-04-05T13:23:42.562Z","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=promptboom","compare_url":"https://unfragile.ai/compare?artifact=promptboom"}},"signature":"XwjW3w3DdH0obn2TuCMe7BrUkM4YkDe08MOffqb6oHQLYtr7oduBd7hZrQy37w3JqDfxute6BgLkGznE8N1qDw==","signedAt":"2026-06-20T15:01:18.578Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/promptboom","artifact":"https://unfragile.ai/promptboom","verify":"https://unfragile.ai/api/v1/verify?slug=promptboom","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"}}