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The system maintains goal state across sessions and uses the LLM to reason about progress relative to user-defined targets, enabling adaptive feedback without hardcoded rule engines.","intents":["I want to log my daily nutrition intake and get AI-powered feedback on whether I'm meeting my macro/calorie targets","I need an agent that tracks multiple personal goals simultaneously and identifies correlations between them","I want to receive personalized insights about my progress patterns without manually calculating statistics"],"best_for":["individuals building personal health tracking systems","developers creating multi-goal habit-tracking applications","teams prototyping AI-driven wellness platforms"],"limitations":["LLM-based analysis introduces latency (typically 1-5 seconds per analysis cycle) and token costs scale with goal complexity","No built-in persistence layer — requires external database integration for multi-session state management","Analysis quality depends on LLM model capability; may produce inconsistent insights with weaker models"],"requires":["Python 3.8+","API key for OpenAI, Anthropic, or compatible LLM provider","User input data in structured format (JSON or CSV for nutrition logs)"],"input_types":["text (user goal descriptions, daily logs)","structured data (JSON nutrition data, habit completion records)"],"output_types":["text (LLM-generated insights and recommendations)","structured data (progress metrics, trend analysis)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-promethai__cap_1","uri":"capability://data.processing.analysis.nutrition.data.extraction.and.normalization.from.unstructured.logs","name":"nutrition data extraction and normalization from unstructured logs","description":"Parses free-form user nutrition input (e.g., 'had 2 eggs, toast, and coffee') using LLM-powered natural language understanding to extract food items, quantities, and estimated macronutrients. The system normalizes extracted data into a canonical format (calories, protein, carbs, fats) and optionally cross-references a nutrition database to improve accuracy, enabling users to log meals conversationally without structured forms.","intents":["I want to log meals in natural language without manually entering structured nutrition data","I need the system to automatically extract and normalize nutrition information from casual food descriptions","I want to avoid friction in daily logging by accepting voice or text input and converting it to structured nutrition records"],"best_for":["mobile-first nutrition tracking applications","voice-enabled health assistants","developers building conversational nutrition interfaces"],"limitations":["LLM-based extraction may misidentify portion sizes or uncommon foods, requiring user confirmation for accuracy","Accuracy depends on nutrition database quality and coverage — niche or regional foods may not resolve correctly","Extraction latency (1-3 seconds per meal) may feel slow for real-time voice logging"],"requires":["Python 3.8+","LLM API access (OpenAI, Anthropic, or compatible)","Optional: nutrition database API (USDA FoodData Central, Nutritionix, or similar) for macro lookups"],"input_types":["text (free-form meal descriptions, voice transcripts)"],"output_types":["structured data (JSON with food items, quantities, macronutrients)","text (confidence scores, clarification requests)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-promethai__cap_2","uri":"capability://planning.reasoning.agent.driven.goal.decomposition.and.task.planning","name":"agent-driven goal decomposition and task planning","description":"Accepts high-level user goals (e.g., 'lose 10 pounds in 3 months') and uses an LLM agent to decompose them into actionable sub-goals and daily tasks with specific metrics. The agent reasons about goal feasibility, identifies dependencies between tasks, and generates a prioritized plan that the user can execute incrementally. The system maintains the plan state and adjusts it based on progress feedback.","intents":["I want to break down a big health goal into weekly and daily milestones automatically","I need an AI agent to validate whether my goal is realistic and suggest adjustments","I want the system to generate a personalized action plan that adapts as I report progress"],"best_for":["individuals using AI coaching for personal development","developers building goal-setting and habit-tracking platforms","teams creating AI-powered wellness or productivity applications"],"limitations":["LLM-generated plans may be overly ambitious or unrealistic without domain expertise validation","Plan adjustments require re-prompting the LLM, introducing latency and token costs","No built-in constraint solver — cannot optimize across conflicting goals or resource constraints"],"requires":["Python 3.8+","LLM API access with function calling support (OpenAI, Anthropic, or compatible)","User input: goal description, timeline, and constraints"],"input_types":["text (goal description, progress updates, user feedback)"],"output_types":["structured data (task list, milestones, metrics)","text (reasoning and explanations for plan decisions)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-promethai__cap_3","uri":"capability://memory.knowledge.multi.session.state.management.and.context.persistence","name":"multi-session state management and context persistence","description":"Maintains user state across multiple conversation sessions by storing goal definitions, progress history, and previous LLM interactions in a persistent backend. The system retrieves relevant context when the user returns and injects it into new LLM prompts, enabling the agent to provide continuous, contextual feedback without requiring users to re-explain their goals or history.","intents":["I want the agent to remember my goals and progress from previous sessions without me re-explaining them","I need the system to track long-term trends and reference historical data when giving feedback","I want to resume goal tracking across multiple devices and sessions seamlessly"],"best_for":["mobile and web applications requiring persistent user state","multi-platform health tracking systems","developers building long-term habit or wellness platforms"],"limitations":["No built-in database — requires external storage (PostgreSQL, MongoDB, Firebase, etc.) for production use","Context injection into LLM prompts increases token usage and latency as history grows","No automatic data cleanup or archival — old sessions accumulate and may degrade performance"],"requires":["Python 3.8+","External database (PostgreSQL, MongoDB, Firebase, or similar)","Session management library (e.g., Flask-Session, custom implementation)"],"input_types":["structured data (user ID, session ID, goal state, progress records)"],"output_types":["structured data (retrieved context, session history, user profile)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-promethai__cap_4","uri":"capability://planning.reasoning.adaptive.feedback.generation.based.on.progress.patterns","name":"adaptive feedback generation based on progress patterns","description":"Analyzes user progress data over time (nutrition logs, goal completion rates, habit streaks) and uses an LLM agent to generate contextual, personalized feedback that adapts to detected patterns. The system identifies trends (e.g., weekend diet slips, morning consistency) and generates targeted recommendations without requiring explicit rule configuration, enabling dynamic coaching that evolves with user behavior.","intents":["I want the agent to notice patterns in my behavior and give me specific, actionable feedback","I need personalized recommendations that adapt based on what's working and what isn't","I want the system to identify my weak points and suggest targeted interventions"],"best_for":["health coaching and wellness platforms","habit-tracking applications with AI coaching","developers building adaptive feedback systems"],"limitations":["Pattern detection requires sufficient historical data (typically 2+ weeks) to be reliable","LLM-generated feedback may be generic or miss domain-specific insights without expert validation","Feedback generation latency (2-5 seconds) may be too slow for real-time coaching scenarios"],"requires":["Python 3.8+","LLM API access","Historical user data (minimum 7-14 days of progress records)","Data analysis library (pandas, numpy, or similar)"],"input_types":["structured data (progress history, goal metrics, habit completion records)"],"output_types":["text (personalized feedback and recommendations)","structured data (identified patterns, confidence scores)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-promethai__cap_5","uri":"capability://tool.use.integration.multi.provider.llm.integration.with.fallback.and.cost.optimization","name":"multi-provider llm integration with fallback and cost optimization","description":"Abstracts LLM provider selection (OpenAI, Anthropic, Ollama, local models) behind a unified interface, enabling runtime provider switching based on cost, latency, or availability constraints. The system implements fallback logic (e.g., use Anthropic if OpenAI quota is exhausted) and cost-aware routing (e.g., use cheaper models for simple tasks, expensive models for complex reasoning), reducing operational costs and improving resilience.","intents":["I want to use multiple LLM providers and switch between them based on cost or availability","I need the system to fall back to alternative providers if my primary provider is down or quota-limited","I want to optimize costs by routing simple tasks to cheaper models and complex tasks to more capable models"],"best_for":["cost-conscious teams deploying LLM-based applications","developers building multi-provider LLM orchestration","applications requiring high availability and resilience"],"limitations":["Provider abstraction adds ~50-100ms latency per LLM call due to routing logic","Cost optimization requires manual configuration of provider preferences and task routing rules","Fallback logic may produce inconsistent results if different providers generate different outputs for the same prompt"],"requires":["Python 3.8+","API keys for at least one LLM provider (OpenAI, Anthropic, Ollama, etc.)","Configuration file or environment variables for provider settings"],"input_types":["text (prompts, configuration)","structured data (provider preferences, cost budgets)"],"output_types":["text (LLM responses)","structured data (provider metadata, cost tracking)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-promethai__cap_6","uri":"capability://text.generation.language.conversational.goal.refinement.with.clarification.loops","name":"conversational goal refinement with clarification loops","description":"Engages users in multi-turn conversations to refine vague or ambiguous goals through LLM-driven clarification questions. The agent asks targeted questions about constraints, timelines, and success metrics, then iteratively updates the goal definition based on user responses. This reduces friction in goal setup and ensures the system understands user intent before generating plans.","intents":["I want to describe my goal in natural language and have the agent ask clarifying questions","I need the system to help me define success metrics and realistic timelines for my goals","I want to refine my goal iteratively through conversation rather than filling out forms"],"best_for":["conversational health and wellness applications","goal-setting platforms with AI coaching","developers building interactive goal definition interfaces"],"limitations":["Multi-turn conversations increase total latency and token costs compared to single-prompt goal definition","LLM-generated clarification questions may be repetitive or miss important constraints","No built-in validation — users can confirm goals that are unrealistic or contradictory"],"requires":["Python 3.8+","LLM API access with multi-turn conversation support","Session management for maintaining conversation state"],"input_types":["text (user goal descriptions, responses to clarification questions)"],"output_types":["text (clarification questions, refined goal summaries)","structured data (goal definition with metrics and constraints)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-promethai__cap_7","uri":"capability://data.processing.analysis.progress.visualization.and.metric.aggregation","name":"progress visualization and metric aggregation","description":"Aggregates multi-dimensional progress data (nutrition metrics, habit completion, goal milestones) into unified dashboards and visualizations. The system computes derived metrics (weekly averages, trend lines, streak counts) and formats them for display, enabling users to see progress at multiple time scales without manual calculation.","intents":["I want to see my progress across multiple goals in a single dashboard","I need to visualize trends over time (daily, weekly, monthly) to understand my patterns","I want to track habit streaks and milestone completion without manual counting"],"best_for":["mobile and web health tracking applications","habit-tracking platforms with analytics","developers building progress visualization systems"],"limitations":["Metric computation adds latency (typically 100-500ms) as data volume grows","Visualization requires frontend integration — backend only provides data, not rendered charts","No built-in time-series database — requires external storage for efficient historical queries"],"requires":["Python 3.8+","Data aggregation library (pandas, SQLAlchemy, or similar)","Frontend framework for visualization (React, Vue, etc.) or charting library (Chart.js, D3.js)"],"input_types":["structured data (progress records, goal definitions, habit logs)"],"output_types":["structured data (aggregated metrics, trend data, JSON for frontend)","text (summary statistics)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-promethai__cap_8","uri":"capability://data.processing.analysis.user.feedback.collection.and.model.improvement.loops","name":"user feedback collection and model improvement loops","description":"Captures explicit user feedback on LLM-generated recommendations and insights (e.g., 'this feedback was helpful', 'this plan is unrealistic') and stores it for analysis. The system can use this feedback to identify failure modes, retrain or fine-tune models, and improve future recommendations without requiring manual annotation.","intents":["I want to provide feedback on the agent's recommendations to help it improve","I need the system to learn from my corrections and adjust future suggestions","I want to track which types of feedback are most helpful for my goals"],"best_for":["teams building AI-powered health and wellness platforms","developers implementing continuous model improvement loops","applications requiring user feedback for model fine-tuning"],"limitations":["Feedback collection requires explicit user action — implicit feedback (e.g., ignored recommendations) is not captured","Model improvement requires sufficient feedback volume and manual retraining — not automatic","No built-in privacy controls — feedback data may contain sensitive health information"],"requires":["Python 3.8+","Feedback storage (database, data warehouse)","Optional: model fine-tuning infrastructure (OpenAI fine-tuning API, Hugging Face, etc.)"],"input_types":["structured data (feedback ratings, binary helpful/not-helpful, text comments)"],"output_types":["structured data (feedback records, aggregated feedback statistics)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","API key for OpenAI, Anthropic, or compatible LLM provider","User input data in structured format (JSON or CSV for nutrition logs)","LLM API access (OpenAI, Anthropic, or compatible)","Optional: nutrition database API (USDA FoodData Central, Nutritionix, or similar) for macro lookups","LLM API access with function calling support (OpenAI, Anthropic, or compatible)","User input: goal description, timeline, and constraints","External database (PostgreSQL, MongoDB, Firebase, or similar)","Session management library (e.g., Flask-Session, custom implementation)","LLM API access"],"failure_modes":["LLM-based analysis introduces latency (typically 1-5 seconds per analysis cycle) and token costs scale with goal complexity","No built-in persistence layer — requires external database integration for multi-session state management","Analysis quality depends on LLM model capability; may produce inconsistent insights with weaker models","LLM-based extraction may misidentify portion sizes or uncommon foods, requiring user confirmation for accuracy","Accuracy depends on nutrition database quality and coverage — niche or regional foods may not resolve correctly","Extraction latency (1-3 seconds per meal) may feel slow for real-time voice logging","LLM-generated plans may be overly ambitious or unrealistic without domain expertise validation","Plan adjustments require re-prompting the LLM, introducing latency and token costs","No built-in constraint solver — cannot optimize across conflicting goals or resource constraints","No built-in database — requires external storage (PostgreSQL, MongoDB, Firebase, etc.) for production use","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.28,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.047Z","last_scraped_at":"2026-05-03T14:00:10.321Z","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=promethai","compare_url":"https://unfragile.ai/compare?artifact=promethai"}},"signature":"l2CUzQ8JPxHfhOEWCjjmvP0c5E8GyVKrGoUI2U9jhapuCPtP2p1uzpMba169DHWGwY8QbB6bU47sC1cESO+rCQ==","signedAt":"2026-06-22T12:07:58.430Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/promethai","artifact":"https://unfragile.ai/promethai","verify":"https://unfragile.ai/api/v1/verify?slug=promethai","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"}}