{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_promptmetheus","slug":"promptmetheus","name":"Promptmetheus","type":"prompt","url":"http://promptmetheus.com","page_url":"https://unfragile.ai/promptmetheus","categories":["prompt-engineering"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_promptmetheus__cap_0","uri":"capability://text.generation.language.structured.prompt.composition.with.section.based.lego.blocks","name":"structured prompt composition with section-based lego blocks","description":"Enforces a compositional prompt structure decomposing prompts into discrete, reusable sections (Context → Task → Instructions → Samples → Primer) that can be independently authored, versioned, and substituted. Each section is treated as a modular building block allowing variant generation without rewriting entire prompts. The system maintains section-level metadata and enables LEGO-like recombination across prompt variants.","intents":["I want to break down my prompt into reusable components so I can test different instruction phrasings without rewriting the entire prompt","I need to maintain consistency across multiple prompts by reusing context and primer sections across different tasks","I want to systematically explore how changing one section (e.g., samples) affects output quality while keeping other sections constant"],"best_for":["prompt engineers optimizing multi-variant prompt families","teams building prompt libraries with shared context and instruction patterns","developers iterating on prompt structure for production LLM applications"],"limitations":["Requires understanding of prompt structure model (Context/Task/Instructions/Samples/Primer) — not intuitive for users unfamiliar with prompt engineering best practices","No support for nested or conditional section logic — sections are flat and sequential","Section-level versioning may create combinatorial explosion when testing many variants across multiple sections"],"requires":["Web browser with minimum 12-inch screen","Familiarity with prompt engineering concepts and LLM behavior","API credentials for at least one supported LLM provider"],"input_types":["text (prompt sections)","structured metadata (section type, version, tags)"],"output_types":["composed prompt text","prompt variant definitions","section changelog with version history"],"categories":["text-generation-language","prompt-engineering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_promptmetheus__cap_1","uri":"capability://data.processing.analysis.multi.model.batch.testing.with.dynamic.dataset.injection","name":"multi-model batch testing with dynamic dataset injection","description":"Executes a single prompt variant against multiple LLM providers and models simultaneously by injecting test datasets (context variables) into the prompt template, collecting completions from all models in parallel, and aggregating results for comparative analysis. The system dispatches API calls to 15 different provider endpoints, handles asynchronous completion collection, and correlates results by model and variant for statistical comparison.","intents":["I want to test my prompt against 5 different models (GPT-4, Claude, Mistral, etc.) to see which one produces the best output for my use case","I need to run my prompt against a dataset of 100 test cases and see how different models perform on each case","I want to compare model performance across different parameter configurations (temperature, top-p) to find the optimal settings"],"best_for":["developers selecting optimal LLM providers for production applications","teams evaluating model performance on domain-specific tasks before deployment","prompt engineers benchmarking prompt effectiveness across model families"],"limitations":["Rate limits imposed by upstream LLM providers (OpenAI, Anthropic, etc.) may throttle batch testing of large datasets","Cost scales linearly with number of models tested and dataset size — testing 100 cases across 10 models incurs 1000 API calls","No built-in cost optimization or caching — duplicate test runs against same model/prompt/input incur full API charges","Maximum dataset size for batch testing is unknown — may have undocumented limits on concurrent requests or total tokens processed"],"requires":["API credentials for at least one LLM provider (Anthropic, OpenAI, Mistral, Groq, etc.)","Test dataset in unknown format (CSV, JSON, or other structured format)","Sufficient API quota/credits across selected providers to cover batch testing costs"],"input_types":["prompt template with variable placeholders","test dataset (format unknown)","model selection list (provider + model name + parameters)","variable bindings (key-value pairs for injection)"],"output_types":["completion results with metadata (model, latency, token count, cost)","statistical aggregations (success rate, average quality score by model)","comparative visualizations (model performance charts)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_promptmetheus__cap_10","uri":"capability://tool.use.integration.multi.provider.api.abstraction.with.unified.credential.management","name":"multi-provider api abstraction with unified credential management","description":"Abstracts away provider-specific API differences by implementing unified interface supporting 15 LLM providers (Anthropic, OpenAI, DeepMind, Mistral, Perplexity, xAI, DeepSeek, Cohere, Groq, Fetch AI, OpenRouter, AI21 Labs, Venice, Moonshot AI, Deep Infra) and 150+ models. Credential management stores API keys securely (encryption mechanism unknown) and enables users to add/remove providers without code changes. Provider selection is decoupled from prompt definition, allowing same prompt to be tested against different providers.","intents":["I want to test my prompt against both OpenAI and Anthropic models without writing separate code for each provider's API","I need to switch from OpenAI to a cheaper provider (e.g., Groq) without modifying my prompts or test datasets","I want to add a new LLM provider to my testing workflow without updating my prompt definitions"],"best_for":["developers avoiding provider lock-in by testing across multiple LLM APIs","teams evaluating new LLM providers without refactoring existing prompts","organizations optimizing costs by comparing providers without code changes"],"limitations":["Provider-specific features are not exposed — no support for provider-specific parameters (e.g., OpenAI's function_call, Anthropic's tool_use) that differ across APIs","Custom model configuration mechanism is unknown — unclear how to add new models or configure provider-specific settings","Credential security details are unknown — encryption method, key rotation, and access controls are unspecified","Provider availability and rate limits are inherited from upstream APIs — Promptmetheus has no control over provider outages or throttling","No built-in fallback or retry logic — unclear how system handles provider failures"],"requires":["API credentials for at least one supported LLM provider","Active Promptmetheus account"],"input_types":["API credentials (API key, organization ID, etc.)","provider selection (provider name + model name)","model parameters (temperature, top_p, max_tokens, etc.)"],"output_types":["unified completion results (model, latency, token count, cost)","provider status (available models, rate limits, pricing)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_promptmetheus__cap_11","uri":"capability://automation.workflow.model.parameter.tuning.interface.with.configuration.persistence","name":"model parameter tuning interface with configuration persistence","description":"Provides UI for configuring model-specific parameters (temperature, top_p, max_tokens, frequency_penalty, presence_penalty, etc.) for each model in batch tests. Parameter configurations are persisted and reusable across test runs, enabling systematic exploration of parameter space. The system maintains parameter presets (e.g., 'creative', 'precise', 'balanced') that can be applied to multiple models.","intents":["I want to test my prompt with different temperature settings (0.0, 0.5, 1.0) to find the optimal balance between consistency and creativity","I need to set max_tokens to 500 for all models in my batch test to ensure consistent output length","I want to save a 'precise' parameter configuration (low temperature, low top_p) and reuse it across multiple prompts"],"best_for":["prompt engineers fine-tuning model behavior through parameter optimization","teams standardizing parameter presets across prompts","developers exploring parameter sensitivity and impact on output quality"],"limitations":["Parameter support varies by provider — not all parameters are supported for all models (e.g., frequency_penalty not supported by all providers)","Parameter validation is unknown — unclear if system prevents invalid parameter combinations (e.g., top_p > 1.0)","Parameter impact analysis is unsupported — no built-in visualization of how parameter changes affect output quality","Preset management is unspecified — unclear if presets are shared across team or user-specific","Parameter documentation is unknown — unclear if system provides guidance on parameter meanings and recommended ranges"],"requires":["Model selection (provider + model name)","Understanding of model parameters and their effects"],"input_types":["parameter values (temperature, top_p, max_tokens, etc.)","preset name and description"],"output_types":["parameter configuration (saved for reuse)","parameter presets (reusable across prompts)","completion results with parameter metadata"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_promptmetheus__cap_2","uri":"capability://automation.workflow.prompt.versioning.with.changelog.tracking.and.variant.management","name":"prompt versioning with changelog tracking and variant management","description":"Maintains complete version history of prompt sections and variants with timestamped changelogs, enabling rollback to previous versions and tracking design decisions across iterations. Each version captures section content, variable definitions, and metadata. The system supports branching variants (testing different section combinations) while maintaining lineage to parent versions, allowing comparison of performance across versions.","intents":["I want to see what changed in my prompt between version 3 and version 5 to understand why performance degraded","I need to revert to a previous prompt version that was working well before my recent edits","I want to maintain multiple prompt variants (e.g., 'aggressive' vs 'conservative' tone) and track which performs better over time"],"best_for":["teams iterating on prompts over weeks/months and needing audit trails","prompt engineers A/B testing variants and tracking performance history","organizations requiring change documentation for compliance or knowledge management"],"limitations":["Version control strategy is unknown — no information on branching, merging, or conflict resolution for collaborative edits","Changelog granularity is unclear — unknown whether changes are tracked at section level or full-prompt level","No apparent support for annotating versions with performance metrics or A/B test results — changelog appears text-only","Rollback mechanism is unspecified — unknown if rollback is instantaneous or requires manual confirmation"],"requires":["Web browser with minimum 12-inch screen","Active Promptmetheus project with at least one prompt"],"input_types":["prompt section edits (text)","version annotations (optional notes/tags)"],"output_types":["version history timeline","diff view showing changes between versions","changelog entries with timestamps and author (if team account)","rollback confirmation and restored prompt state"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_promptmetheus__cap_3","uri":"capability://data.processing.analysis.manual.completion.rating.and.custom.evaluator.execution","name":"manual completion rating and custom evaluator execution","description":"Provides dual evaluation pathways: (1) manual quality assessment where users rate completions on custom scales (e.g., 1-5 stars, pass/fail), and (2) automated constraint validation via custom evaluators that programmatically assess completions against defined criteria. Custom evaluators execute against completion results (implementation language/format unknown) and produce pass/fail or scored outputs. Ratings are aggregated into statistical summaries by model and variant.","intents":["I want to manually review and rate the quality of completions from different models to identify which produces the best output","I need to automatically validate that completions meet specific constraints (e.g., response length < 500 tokens, contains required keywords)","I want to correlate manual quality ratings with automated metrics to understand which automated checks predict human satisfaction"],"best_for":["teams with domain expertise to manually evaluate output quality","developers building constraint-based validation into prompt optimization workflows","organizations requiring human-in-the-loop evaluation before production deployment"],"limitations":["Custom evaluator specification language/format is unknown — unclear how to define evaluation logic (Python, JavaScript, regex, DSL, etc.)","Evaluator execution environment is unspecified — unknown if evaluators run server-side or client-side, and whether they have access to external APIs","Manual rating is labor-intensive and doesn't scale beyond small datasets — no built-in sampling or prioritization for large result sets","No apparent inter-rater agreement metrics or conflict resolution for team-based manual evaluation","Evaluator performance/latency impact on batch testing is unknown"],"requires":["Completed batch test run with completions to evaluate","For custom evaluators: knowledge of evaluator specification format (unknown)","For manual rating: human reviewer time"],"input_types":["completion results (text from LLM)","manual rating input (numeric scale or categorical)","custom evaluator definition (format unknown)","evaluation context (original prompt, input variables, expected output)"],"output_types":["rating scores (numeric or categorical)","evaluator pass/fail results","aggregated statistics (average rating by model, pass rate by variant)","evaluation metadata (timestamp, evaluator ID if team account)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_promptmetheus__cap_4","uri":"capability://text.generation.language.project.level.variable.definition.and.prompt.level.substitution","name":"project-level variable definition and prompt-level substitution","description":"Supports two-tier variable scoping: project-level variables (shared across all prompts in a project, e.g., company name, API endpoint) and prompt-level variables (specific to individual prompts, e.g., user query, context). Variables are defined as key-value pairs and substituted into prompt templates using placeholder syntax (format unknown). During batch testing, dataset rows are injected as variable bindings, enabling dynamic context injection without prompt rewriting.","intents":["I want to define company-wide variables (e.g., brand voice, product name) once and reuse them across 20 different prompts","I need to test my prompt with different user inputs by injecting variables from a CSV file without manually editing the prompt each time","I want to parameterize prompts so non-technical team members can use them by filling in variable values without understanding the underlying prompt structure"],"best_for":["teams managing multiple prompts with shared context (company name, brand guidelines, API details)","developers building prompt templates for end-user consumption","organizations standardizing prompt variables across teams"],"limitations":["Variable scoping rules are unclear — unknown whether prompt-level variables can override project-level variables, or if naming conflicts are prevented","Placeholder syntax is unspecified — unknown if variables use {{var}}, ${var}, {var}, or other syntax","No apparent support for variable transformations or computed variables — variables appear to be simple key-value substitution only","Variable validation is unknown — no information on type checking, required vs optional variables, or default values","No built-in variable documentation or schema — teams must maintain external documentation of available variables"],"requires":["Active Promptmetheus project","Understanding of variable naming conventions and placeholder syntax (format unknown)"],"input_types":["variable definitions (key-value pairs)","variable scope (project or prompt level)","dataset with variable bindings (format unknown, likely CSV or JSON)"],"output_types":["substituted prompt text with variables replaced","variable binding metadata (which variables were injected, source dataset row)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_promptmetheus__cap_5","uri":"capability://data.processing.analysis.cost.calculation.and.token.level.expense.tracking","name":"cost calculation and token-level expense tracking","description":"Automatically calculates API costs for each completion based on model pricing, input token count, and output token count. Costs are aggregated by model, variant, and dataset to provide per-completion and batch-level expense summaries. The system maintains pricing data for 150+ models across 15 providers and updates pricing as providers change rates. Cost estimates are displayed during batch test planning to enable cost-aware model selection.","intents":["I want to estimate how much a batch test will cost before running it so I can decide whether to test all 10 models or just the top 3","I need to compare the cost-per-completion across models to find the cheapest option that still meets quality requirements","I want to track total spending on prompt engineering across my team to manage API budgets"],"best_for":["cost-conscious teams optimizing API spending during prompt development","developers selecting models based on cost-quality tradeoffs","organizations with limited API budgets needing cost visibility"],"limitations":["Pricing data freshness is unknown — unclear how often pricing is updated when providers change rates","Cost calculation assumes standard pricing — no support for volume discounts, enterprise pricing, or custom rate agreements","No cost optimization recommendations — system shows costs but doesn't suggest cheaper alternatives or batching strategies","Cost tracking is limited to Promptmetheus-executed tests — no integration with external API usage tracking or billing systems","Export format for cost reports is unknown — unclear if costs can be exported for accounting/budgeting systems"],"requires":["API credentials for selected LLM providers (cost calculation requires provider pricing data)","Completed batch test run (costs calculated post-execution, not pre-execution estimates)"],"input_types":["model selection (provider + model name)","prompt content (for token counting)","test dataset (for volume estimation)"],"output_types":["per-completion cost (input tokens × input rate + output tokens × output rate)","batch-level cost summary (total cost, cost by model, cost by variant)","cost-per-quality-point (if manual ratings provided)","cost comparison visualizations"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_promptmetheus__cap_6","uri":"capability://planning.reasoning.prompt.chain.optimization.with.error.compounding.analysis","name":"prompt chain optimization with error compounding analysis","description":"Analyzes multi-step prompt chains (agents/workflows) to identify error propagation and compounding effects where failures in early steps cascade through downstream steps. The system models chain execution paths, calculates cumulative error probability, and provides optimization recommendations to reduce failure rates. Analysis includes identifying bottleneck steps with highest failure rates and suggesting prompt modifications to improve reliability.","intents":["I'm building a multi-step agent that extracts data, validates it, and generates a report — I want to understand how errors in extraction affect downstream validation and reporting","I want to optimize my 5-step prompt chain to reduce the overall failure rate from 15% to under 5%","I need to identify which step in my chain is most likely to fail so I can focus prompt engineering effort there"],"best_for":["developers building multi-step LLM agents and workflows","teams optimizing complex prompt chains for production reliability","organizations analyzing failure modes in LLM-powered systems"],"limitations":["Chain definition format is unknown — unclear how to specify multi-step workflows in Promptmetheus","Error propagation model is unspecified — unknown if system uses Bayesian networks, Markov chains, or simpler linear error compounding","Optimization recommendations are unspecified — unclear whether system suggests specific prompt changes or only identifies bottlenecks","Analysis assumes independent step failures — may not account for correlated failures or shared error modes across steps","Scalability to large chains (10+ steps) is unknown"],"requires":["Multi-step prompt chain definition (format unknown)","Test dataset with ground truth labels for each step","Completed test runs of chain with success/failure annotations"],"input_types":["prompt chain definition (step sequence, dependencies)","test dataset with expected outputs at each step","completion results with success/failure labels"],"output_types":["error propagation analysis (cumulative failure probability by step)","bottleneck identification (steps with highest failure rates)","optimization recommendations (prompt modifications to reduce failures)","reliability improvement projections (estimated failure rate after optimization)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_promptmetheus__cap_7","uri":"capability://data.processing.analysis.statistical.pattern.detection.and.correlation.analysis","name":"statistical pattern detection and correlation analysis","description":"Analyzes completion results across models, variants, and test cases to identify statistical patterns and correlations. The system computes metrics like success rate by model, quality distribution by variant, and correlations between input characteristics (e.g., prompt length, variable values) and output quality. Visualizations highlight patterns (e.g., 'Claude performs 20% better on reasoning tasks') enabling data-driven prompt optimization decisions.","intents":["I want to understand why Claude performs better than GPT-4 on my task — is it the model, the prompt structure, or the input characteristics?","I need to identify which prompt variants perform best across different input types so I can select the right variant for each use case","I want to find correlations between prompt characteristics (length, tone, structure) and output quality to guide future prompt design"],"best_for":["data-driven prompt engineers using statistical analysis to guide optimization","teams with large test datasets (100+ cases) where patterns emerge","researchers studying LLM behavior across models and prompts"],"limitations":["Statistical metrics computed are unknown — unclear which correlations, distributions, or pattern types are supported","Minimum dataset size for meaningful analysis is unspecified — small datasets (< 20 cases) may produce spurious correlations","Causality inference is limited — correlations don't imply causation, and system may not distinguish correlation from confounding","Pattern detection algorithm is unspecified — unknown if system uses simple aggregation, clustering, or more sophisticated ML techniques","Visualization customization is unknown — unclear if users can define custom metrics or are limited to built-in analyses"],"requires":["Completed batch test run with multiple models and variants","Test dataset with sufficient size and diversity (minimum size unknown)","Manual quality ratings or automated evaluator results for correlation analysis"],"input_types":["completion results with metadata (model, variant, input, output, quality score)","test dataset characteristics (input length, complexity, category)"],"output_types":["statistical summaries (mean, median, std dev of quality by model/variant)","correlation matrices (input characteristics vs output quality)","pattern visualizations (charts showing performance trends)","anomaly detection (outlier completions or unexpected patterns)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_promptmetheus__cap_8","uri":"capability://automation.workflow.private.workspace.isolation.with.team.account.collaboration","name":"private workspace isolation with team account collaboration","description":"Provides user-level workspace isolation where each user has a private workspace containing their prompts, datasets, and test results. Team accounts enable shared workspaces where multiple users can access and edit the same prompts and datasets. Real-time collaborative editing allows simultaneous edits (conflict resolution mechanism unknown). Shared prompt library enables teams to publish reusable prompts for organization-wide consumption.","intents":["I want to experiment with prompts privately without affecting my team's shared library until I'm confident in the changes","My team of 5 prompt engineers needs to collaborate on a shared set of prompts and see each other's test results in real-time","I want to publish a high-performing prompt to our team library so other engineers can use it as a starting point"],"best_for":["teams with multiple prompt engineers needing shared workspace access","organizations building internal prompt libraries for reuse","distributed teams requiring real-time collaboration on prompts"],"limitations":["Conflict resolution for simultaneous edits is unknown — unclear how system handles two users editing same section simultaneously","Collaboration features are limited — editorial summary notes 'no collaboration features for teams, severely limiting utility for organizations'","Shared library management is unspecified — unknown if there are approval workflows, versioning, or access controls for published prompts","Team account pricing and user limits are unknown","Audit trail for collaborative edits is unspecified — unclear if system tracks who made which changes"],"requires":["Team account (pricing unknown)","Invitation to shared workspace (mechanism unknown)","Web browser with minimum 12-inch screen"],"input_types":["prompt edits (text)","workspace sharing invitations (email or other mechanism)"],"output_types":["shared workspace access","real-time edit notifications","shared prompt library","collaboration metadata (edit history, contributor attribution)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_promptmetheus__cap_9","uri":"capability://automation.workflow.project.level.organization.with.dashboard.and.completion.history","name":"project-level organization with dashboard and completion history","description":"Organizes prompts, datasets, and test results into projects as top-level containers. Each project has a dashboard displaying relevant statistics (e.g., number of prompts, recent test runs, average quality scores). Completion history maintains full metadata for each test execution including timestamp, model, variant, dataset, results, and ratings. Projects enable logical grouping of related prompts and datasets for organizational clarity.","intents":["I want to organize my prompts into separate projects by use case (customer support, content generation, data extraction) so I can manage them independently","I need a dashboard showing the status of all my prompts — which ones are performing well, which need optimization, and recent test activity","I want to review the complete history of a specific prompt's performance over time to understand how optimizations have affected quality"],"best_for":["teams managing multiple prompt engineering projects simultaneously","organizations needing project-level organization and reporting","developers tracking prompt performance over weeks/months"],"limitations":["Project structure is flat — no support for nested projects or project hierarchies","Dashboard customization is unknown — unclear if users can define custom metrics or are limited to built-in statistics","Completion history retention policy is unknown — unclear if old test results are archived or deleted","Export capabilities for project data are unknown — unclear if projects can be exported for backup or migration","Cross-project analysis is unsupported — no apparent ability to compare prompts across projects"],"requires":["Active Promptmetheus account","At least one project created"],"input_types":["project metadata (name, description)","prompts and datasets (added to project)"],"output_types":["project dashboard with statistics","completion history with full metadata","project-level reports and visualizations"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":42,"verified":false,"data_access_risk":"high","permissions":["Web browser with minimum 12-inch screen","Familiarity with prompt engineering concepts and LLM behavior","API credentials for at least one supported LLM provider","API credentials for at least one LLM provider (Anthropic, OpenAI, Mistral, Groq, etc.)","Test dataset in unknown format (CSV, JSON, or other structured format)","Sufficient API quota/credits across selected providers to cover batch testing costs","Active Promptmetheus account","Model selection (provider + model name)","Understanding of model parameters and their effects","Active Promptmetheus project with at least one prompt"],"failure_modes":["Requires understanding of prompt structure model (Context/Task/Instructions/Samples/Primer) — not intuitive for users unfamiliar with prompt engineering best practices","No support for nested or conditional section logic — sections are flat and sequential","Section-level versioning may create combinatorial explosion when testing many variants across multiple sections","Rate limits imposed by upstream LLM providers (OpenAI, Anthropic, etc.) may throttle batch testing of large datasets","Cost scales linearly with number of models tested and dataset size — testing 100 cases across 10 models incurs 1000 API calls","No built-in cost optimization or caching — duplicate test runs against same model/prompt/input incur full API charges","Maximum dataset size for batch testing is unknown — may have undocumented limits on concurrent requests or total tokens processed","Provider-specific features are not exposed — no support for provider-specific parameters (e.g., OpenAI's function_call, Anthropic's tool_use) that differ across APIs","Custom model configuration mechanism is unknown — unclear how to add new models or configure provider-specific settings","Credential security details are unknown — encryption method, key rotation, and access controls are unspecified","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.78,"ecosystem":0.15000000000000002,"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":"2026-05-24T12:16:32.438Z","last_scraped_at":"2026-04-05T13:23:42.551Z","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=promptmetheus","compare_url":"https://unfragile.ai/compare?artifact=promptmetheus"}},"signature":"GX+hxZA+YmRetryaMIZePW8cjR0JCZZRd6TkA4DXIiltaD1G0WpGOK6aKVMqTQtdB60dU2iLOa2/u5leKM0pDw==","signedAt":"2026-06-21T07:57:07.530Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/promptmetheus","artifact":"https://unfragile.ai/promptmetheus","verify":"https://unfragile.ai/api/v1/verify?slug=promptmetheus","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"}}