{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-flowgpt","slug":"flowgpt","name":"FlowGPT","type":"product","url":"https://flowgpt.com/","page_url":"https://unfragile.ai/flowgpt","categories":["prompt-engineering","automation"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-flowgpt__cap_0","uri":"capability://search.retrieval.prompt.library.search.and.discovery","name":"prompt-library-search-and-discovery","description":"Enables users to search and discover pre-written, community-curated prompts across multiple domains and use cases through a centralized indexed repository. The system implements full-text search with categorical filtering and popularity/rating-based ranking to surface high-quality prompts matching user intent. Users can browse by domain (writing, coding, marketing, etc.) and filter by use case, difficulty, or community ratings to find prompts optimized for specific LLM models.","intents":["Find a well-tested prompt for a specific task without writing from scratch","Discover prompt patterns and techniques used by expert practitioners","Compare multiple prompt approaches for the same problem","Access prompts optimized for specific models like GPT-4, Claude, or Llama"],"best_for":["LLM application developers building production systems","Non-technical users learning prompt engineering best practices","Teams standardizing on prompt templates across projects"],"limitations":["Search quality depends on community curation — no automated validation of prompt effectiveness","Prompts may become stale or incompatible as LLM model versions update","No version control or change tracking for prompt iterations","Limited ability to test prompts against multiple models before adoption"],"requires":["Web browser with JavaScript enabled","FlowGPT account (registration required)","API key for target LLM provider (OpenAI, Anthropic, etc.) to test prompts"],"input_types":["text search queries","categorical filters","model/provider selection"],"output_types":["prompt text","metadata (author, rating, use case tags)","model compatibility information"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-flowgpt__cap_1","uri":"capability://automation.workflow.prompt.composition.and.chaining","name":"prompt-composition-and-chaining","description":"Allows users to combine multiple prompts sequentially or in parallel workflows, with variable substitution and output chaining between steps. The system supports templating syntax to inject outputs from one prompt as inputs to subsequent prompts, enabling multi-step reasoning chains and complex task decomposition. Users can define conditional branching based on prompt outputs and reuse common prompt patterns across different workflows.","intents":["Chain multiple LLM calls together to solve complex problems requiring sequential reasoning","Build reusable workflow templates that combine multiple prompts","Pass outputs from one prompt as context to the next without manual copy-paste","Create conditional logic that routes to different prompts based on intermediate results"],"best_for":["Developers building multi-step LLM agents and agentic workflows","Teams creating standardized prompt pipelines for content generation or analysis","Non-technical users building automation without coding"],"limitations":["No built-in error handling or retry logic for failed intermediate steps","Latency compounds with each chained step — no parallel execution optimization","Limited debugging visibility into intermediate prompt outputs","Variable scoping and state management across chains can become complex at scale"],"requires":["FlowGPT account with workflow creation permissions","API keys for LLM providers used in each step","Understanding of prompt templating syntax (variable syntax, conditionals)"],"input_types":["prompt templates with variable placeholders","conditional logic expressions","output mapping definitions"],"output_types":["chained prompt results","workflow execution logs","final aggregated output"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-flowgpt__cap_2","uri":"capability://automation.workflow.prompt.versioning.and.iteration","name":"prompt-versioning-and-iteration","description":"Tracks changes to prompts over time with version history, allowing users to compare different versions, revert to previous iterations, and annotate changes with reasoning. The system maintains a changelog of modifications with timestamps and author information, enabling teams to understand how prompts evolved and why specific changes were made. Users can branch prompts to experiment with variations while preserving the original version.","intents":["Track how a prompt's performance changed across different versions","Revert to a previous prompt version if a recent change degraded output quality","Compare two prompt versions side-by-side to understand what changed","Maintain a team-approved prompt baseline while allowing experimentation branches"],"best_for":["Teams managing production prompts across multiple applications","Researchers iterating on prompt engineering techniques","Organizations requiring audit trails for compliance or quality assurance"],"limitations":["No automated A/B testing framework to measure version performance differences","Version history storage may incur costs for large-scale prompt repositories","No built-in rollback automation — manual promotion of versions required","Limited integration with CI/CD pipelines for automated version deployment"],"requires":["FlowGPT account with version control permissions","Team collaboration features (may require paid tier)"],"input_types":["prompt text modifications","version annotations/commit messages","branching requests"],"output_types":["version history timeline","diff views between versions","version metadata (author, timestamp, change notes)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-flowgpt__cap_3","uri":"capability://planning.reasoning.multi.model.prompt.testing","name":"multi-model-prompt-testing","description":"Enables side-by-side testing of the same prompt against multiple LLM providers and model versions (GPT-4, Claude, Llama, etc.) to compare outputs and identify model-specific behavior. The system sends identical prompts to different models and displays results in a comparative interface, allowing users to evaluate which model produces the best output for their use case. Testing can be configured with specific parameters (temperature, max tokens) and results are cached for cost optimization.","intents":["Compare how different LLM models respond to the same prompt","Identify which model produces the highest quality output for a specific task","Test prompt robustness across model variations before production deployment","Optimize costs by finding the cheapest model that meets quality thresholds"],"best_for":["Developers evaluating which LLM provider to use for production","Teams optimizing for cost-quality tradeoffs across multiple models","Researchers studying model-specific prompt sensitivity"],"limitations":["Testing costs scale linearly with number of models tested — can become expensive","Model outputs may vary due to temperature/randomness — requires multiple runs for statistical significance","No built-in statistical analysis or significance testing","Requires API keys for all models being tested — increases credential management complexity"],"requires":["FlowGPT account with testing features","API keys for each LLM provider (OpenAI, Anthropic, Together AI, etc.)","Sufficient API credits across all providers"],"input_types":["prompt text","model selection list","test parameters (temperature, max_tokens, etc.)"],"output_types":["side-by-side model outputs","quality comparison metrics","cost analysis per model","execution time benchmarks"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-flowgpt__cap_4","uri":"capability://automation.workflow.prompt.sharing.and.collaboration","name":"prompt-sharing-and-collaboration","description":"Enables users to share prompts with team members or the public, with granular permission controls (view-only, edit, fork) and collaborative editing capabilities. The system tracks who created, modified, and used each prompt, and supports commenting/annotation for team feedback. Shared prompts can be published to the community library or kept private within an organization, with usage analytics showing how many users have adopted each prompt.","intents":["Share a well-crafted prompt with team members without duplicating effort","Publish a prompt to the community and track its adoption/impact","Collaborate on prompt refinement with team feedback and comments","Control who can view, edit, or fork a prompt within an organization"],"best_for":["Teams standardizing on shared prompt libraries","Open-source communities building public prompt repositories","Organizations with IP concerns requiring private sharing controls"],"limitations":["Public sharing may expose proprietary prompt techniques or domain knowledge","No built-in licensing or attribution enforcement for shared prompts","Collaborative editing without conflict resolution can lead to version confusion","Usage analytics may be limited or delayed in real-time"],"requires":["FlowGPT account with sharing permissions","Team/organization setup for private sharing (may require paid tier)","Recipient accounts for private sharing"],"input_types":["prompt text","permission level selection","recipient list (for private sharing)","comments/feedback"],"output_types":["shareable links","permission matrices","usage analytics","comment threads"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-flowgpt__cap_5","uri":"capability://text.generation.language.prompt.template.library.with.variables","name":"prompt-template-library-with-variables","description":"Provides pre-built prompt templates with parameterized variables that users can customize for their specific context without rewriting from scratch. Templates include placeholders for domain-specific information (e.g., {{product_name}}, {{target_audience}}) that are substituted at runtime. The system includes templates for common tasks (content generation, code review, data analysis) across multiple domains, with guidance on which variables are required vs. optional.","intents":["Quickly generate a prompt for a common task by filling in a few variables","Ensure consistency across similar prompts by using a standardized template","Learn prompt engineering patterns by examining template structure and variable usage","Reduce time spent on prompt writing for routine tasks"],"best_for":["Non-technical users who need prompts but lack prompt engineering skills","Teams standardizing on prompt patterns across projects","Developers building applications that need dynamic prompt generation"],"limitations":["Templates may not fit all use cases — customization still required for edge cases","Variable naming and semantics must be learned for each template","No validation that substituted variables are semantically appropriate","Templates become outdated as LLM capabilities evolve"],"requires":["FlowGPT account","Understanding of template syntax and variable placeholders"],"input_types":["template selection","variable values (text, structured data)"],"output_types":["instantiated prompt text","template metadata (required variables, examples)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-flowgpt__cap_6","uri":"capability://data.processing.analysis.prompt.performance.analytics","name":"prompt-performance-analytics","description":"Tracks metrics on how prompts perform in production, including success rates, output quality scores, latency, and cost per execution. The system aggregates data from prompt executions and provides dashboards showing trends over time, allowing users to identify which prompts are most effective and cost-efficient. Analytics can be filtered by model, user, time period, or custom tags to understand performance in specific contexts.","intents":["Identify which prompts are producing the highest quality outputs in production","Monitor cost per prompt execution and optimize for budget constraints","Track how prompt changes impact performance metrics over time","Understand which prompts are most frequently used and by whom"],"best_for":["Teams managing production LLM applications at scale","Organizations optimizing for cost-quality tradeoffs","Data-driven teams making prompt selection decisions based on metrics"],"limitations":["Quality metrics require manual annotation or external evaluation — no automated quality scoring","Analytics latency may delay insights into recent prompt changes","Correlation between prompt changes and performance changes requires statistical analysis","Privacy concerns with tracking prompt usage across users"],"requires":["FlowGPT account with analytics features (may require paid tier)","Integration with LLM provider APIs to capture execution metrics","Optional: external quality evaluation system for output scoring"],"input_types":["prompt execution data","quality annotations (manual or automated)","filter/grouping criteria"],"output_types":["performance dashboards","trend charts","comparative metrics tables","cost analysis reports"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-flowgpt__cap_7","uri":"capability://planning.reasoning.prompt.optimization.suggestions","name":"prompt-optimization-suggestions","description":"Analyzes prompts and provides AI-generated suggestions for improvement based on prompt engineering best practices and performance data. The system evaluates prompt clarity, specificity, structure, and alignment with known effective patterns, then recommends concrete changes (e.g., 'add role-playing context', 'break into steps', 'specify output format'). Suggestions are ranked by estimated impact and can be applied with one click.","intents":["Improve a prompt's quality without manually researching best practices","Understand why a prompt might be underperforming and get specific fixes","Learn prompt engineering techniques by examining suggested improvements","Quickly iterate on prompts with AI-assisted refinement"],"best_for":["Developers new to prompt engineering seeking guidance","Teams rapidly iterating on prompts without dedicated prompt engineers","Organizations scaling prompt creation across many use cases"],"limitations":["Suggestions are heuristic-based and may not apply to all domains or models","No guarantee that suggested changes will improve actual performance — requires testing","Suggestions may be generic and not account for domain-specific constraints","Over-reliance on suggestions may prevent learning fundamental prompt engineering principles"],"requires":["FlowGPT account with optimization features","Sufficient API credits for running suggestion analysis"],"input_types":["prompt text","optional: performance metrics or quality feedback"],"output_types":["ranked list of improvement suggestions","before/after prompt comparisons","explanation of why each suggestion is recommended"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["Web browser with JavaScript enabled","FlowGPT account (registration required)","API key for target LLM provider (OpenAI, Anthropic, etc.) to test prompts","FlowGPT account with workflow creation permissions","API keys for LLM providers used in each step","Understanding of prompt templating syntax (variable syntax, conditionals)","FlowGPT account with version control permissions","Team collaboration features (may require paid tier)","FlowGPT account with testing features","API keys for each LLM provider (OpenAI, Anthropic, Together AI, etc.)"],"failure_modes":["Search quality depends on community curation — no automated validation of prompt effectiveness","Prompts may become stale or incompatible as LLM model versions update","No version control or change tracking for prompt iterations","Limited ability to test prompts against multiple models before adoption","No built-in error handling or retry logic for failed intermediate steps","Latency compounds with each chained step — no parallel execution optimization","Limited debugging visibility into intermediate prompt outputs","Variable scoping and state management across chains can become complex at scale","No automated A/B testing framework to measure version performance differences","Version history storage may incur costs for large-scale prompt repositories","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.26,"ecosystem":0.35000000000000003,"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":"2026-06-17T09:51:03.040Z","last_scraped_at":"2026-05-03T14:00:20.516Z","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=flowgpt","compare_url":"https://unfragile.ai/compare?artifact=flowgpt"}},"signature":"5pMYU4IUNVjO9ajwyInm33urtXn9N67oWUSruzmPrifGlyQjle/CEDFnKhF7QLHbVVRoa90egAtz6+6ufGMPCw==","signedAt":"2026-06-20T18:42:57.137Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/flowgpt","artifact":"https://unfragile.ai/flowgpt","verify":"https://unfragile.ai/api/v1/verify?slug=flowgpt","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"}}