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The system appears to weight multiple data sources (financial APIs, news aggregators, trend data) and cross-references them with the decision context to surface relevant factors the user may not have considered.","intents":["I need to validate a business decision using current market conditions, not outdated training data","I want to understand what real-time factors should influence my choice between options","I need to see how recent news or market shifts affect my decision"],"best_for":["Individual professionals making time-sensitive decisions (hiring, investment, product launches)","Researchers validating hypotheses against current data","Solo founders who need quick market validation without analyst reports"],"limitations":["Real-time data integration latency likely 2-5 seconds per query depending on API availability","Free tier probably rate-limited to 5-10 queries per day, restricting iterative decision refinement","Data freshness depends on upstream API providers — gaps in coverage for niche markets or emerging trends","No apparent caching of decision contexts, so each query must re-fetch data"],"requires":["Active internet connection for real-time data fetching","Browser with JavaScript enabled (Vercel-hosted SPA)","No authentication required for free tier"],"input_types":["natural language decision description","multiple choice options (text)","optional context/constraints (text)"],"output_types":["structured recommendation with weighted factors","supporting data citations (news, market data)","confidence scores or uncertainty ranges"],"categories":["planning-reasoning","data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_whybot__cap_1","uri":"capability://planning.reasoning.multi.factor.decision.decomposition.and.weighting","name":"multi-factor decision decomposition and weighting","description":"Breaks down complex decisions into discrete factors (financial, strategic, operational, risk-based) and assigns relative weights to each based on the decision context and available data. The system likely uses a decision tree or factor-scoring model that normalizes heterogeneous inputs (quantitative metrics, qualitative risks, time horizons) into a comparable framework, then ranks options by aggregated weighted scores.","intents":["I need to see which factors matter most for this decision and why","I want to understand how different weightings would change the recommendation","I need to explain my decision to stakeholders with a clear factor breakdown"],"best_for":["Professionals who need to justify decisions to non-technical stakeholders","Teams evaluating vendor or hire decisions with multiple criteria","Researchers building decision models that need interpretability"],"limitations":["Factor weighting is opaque — no visibility into how the system assigns weights (learned vs. rule-based)","No sensitivity analysis or 'what-if' tools to explore how weight changes affect outcomes","Limited to pre-defined factor categories; custom factors likely not supported in free tier","Aggregation method (linear sum, multiplicative, Bayesian) not documented, making reproducibility difficult"],"requires":["Decision with 2-5 distinct options","Ability to articulate decision criteria in natural language"],"input_types":["natural language decision description","multiple options (text)","optional priority hints (e.g., 'cost is critical')"],"output_types":["factor breakdown (structured list with weights)","ranked options with scores","factor contribution to each option"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_whybot__cap_2","uri":"capability://text.generation.language.no.friction.decision.input.and.natural.language.parsing","name":"no-friction decision input and natural language parsing","description":"Accepts unstructured natural language descriptions of decisions without requiring form-filling, structured templates, or authentication. The system parses the input to extract decision options, constraints, and implicit context using NLP techniques (entity recognition, intent classification, relationship extraction), then maps these to internal decision representations without requiring users to pre-format their input.","intents":["I want to describe my decision in my own words without filling out a form","I need to quickly get a recommendation without signing up or creating an account","I want to paste a messy email or Slack message and get instant analysis"],"best_for":["Solo professionals making ad-hoc decisions who value speed over persistence","Users who are skeptical of account creation and want to try the tool immediately","Teams using WhyBot for quick validation before formal decision processes"],"limitations":["No session persistence — decisions are not saved, so users cannot revisit or iterate on previous analyses","Parsing errors likely occur with ambiguous or poorly-structured input (e.g., 'should I hire Alice or Bob?' without context)","No ability to clarify ambiguous inputs — the system must infer intent from a single pass","Free tier likely has no conversation history, limiting iterative refinement"],"requires":["Browser with JavaScript enabled","No authentication or account creation required"],"input_types":["unstructured natural language text","email or message excerpts","bullet-point lists"],"output_types":["parsed decision structure (options, constraints, context)","clarification requests if input is ambiguous"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_whybot__cap_3","uri":"capability://planning.reasoning.contextual.recommendation.generation.with.confidence.indicators","name":"contextual recommendation generation with confidence indicators","description":"Generates actionable recommendations by synthesizing real-time data, factor analysis, and decision context through an LLM reasoning pipeline. The system produces not just a recommendation but also confidence scores, uncertainty ranges, and caveats that indicate when the recommendation is high-confidence vs. speculative. This likely involves prompting strategies that ask the LLM to reason through trade-offs and surface assumptions.","intents":["I need a clear recommendation with an honest assessment of how confident the AI is","I want to understand the assumptions behind the recommendation so I can challenge them","I need to know when the data is insufficient to make a strong recommendation"],"best_for":["Risk-averse professionals who need to understand recommendation confidence","Teams making high-stakes decisions where false confidence is dangerous","Researchers who need to audit AI reasoning for bias or missing factors"],"limitations":["Confidence scores are likely heuristic-based (e.g., data freshness, factor agreement) rather than Bayesian, so they may not reflect true epistemic uncertainty","No explicit uncertainty quantification — confidence is probably a single score rather than credible intervals","Caveats and assumptions are generated by the LLM and may miss important edge cases or domain-specific risks","No audit trail showing which data sources contributed to confidence scores"],"requires":["Decision with sufficient context for the LLM to reason about confidence","Real-time data availability for the decision domain"],"input_types":["decision description with context","options to evaluate"],"output_types":["primary recommendation (text)","confidence score (0-100 or similar)","supporting factors and caveats","alternative recommendations with lower confidence"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_whybot__cap_4","uri":"capability://data.processing.analysis.comparative.option.evaluation.with.trade.off.visualization","name":"comparative option evaluation with trade-off visualization","description":"Evaluates multiple decision options side-by-side by scoring each against identified factors and presenting trade-offs in a structured format. The system likely generates a comparison matrix or visualization showing how each option performs on key dimensions (cost, timeline, risk, strategic fit), enabling users to see which option wins on which factors and where compromises exist.","intents":["I need to see how options compare across multiple criteria at a glance","I want to understand the trade-offs between options (e.g., cost vs. speed)","I need to present a comparison to stakeholders in a clear visual format"],"best_for":["Teams evaluating vendor or tool options with multiple selection criteria","Hiring managers comparing candidate profiles","Product managers choosing between feature implementation approaches"],"limitations":["Visualization is likely text-based or simple charts, not interactive dashboards","No ability to customize which factors are displayed or how they are weighted for different stakeholders","Trade-off analysis is static — no 'what-if' tools to explore how changing weights affects rankings","Comparison limited to options provided by user; no ability to suggest additional options to consider"],"requires":["2-5 distinct options to compare","Clear decision criteria or factors"],"input_types":["multiple options (text descriptions)","decision criteria or factors"],"output_types":["comparison matrix (structured data)","trade-off analysis (text)","visual comparison (likely text-based or simple charts)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_whybot__cap_5","uri":"capability://automation.workflow.stateless.decision.analysis.without.persistence","name":"stateless decision analysis without persistence","description":"Operates as a stateless web application where each decision analysis is independent and not persisted to a database. Users submit a decision, receive analysis, and the session ends without saving context, history, or allowing follow-up refinements. This architectural choice eliminates backend complexity and data storage requirements but sacrifices continuity and iterative analysis capabilities.","intents":["I need a quick one-off decision analysis without committing to an account","I want to use this tool without worrying about data privacy or persistence","I need instant results without waiting for backend processing"],"best_for":["Users making isolated decisions who don't need historical tracking","Privacy-conscious professionals who prefer not to store decision data","Teams using WhyBot for quick validation before formal decision processes"],"limitations":["No decision history — users cannot review past analyses or learn from previous decisions","No ability to refine or iterate on a decision — each query is independent","No collaborative features — decisions cannot be shared or discussed within the platform","No personalization — the system cannot learn user preferences or improve recommendations over time","Free tier likely has no way to export or save analysis results"],"requires":["Browser with JavaScript enabled","No account or authentication required"],"input_types":["decision description"],"output_types":["analysis results (text, structured data)","recommendation"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_whybot__cap_6","uri":"capability://data.processing.analysis.multi.source.data.integration.and.synthesis","name":"multi-source data integration and synthesis","description":"Fetches and synthesizes data from multiple external sources (financial APIs, news aggregators, market data providers, trend databases) to build a comprehensive context for decision analysis. The system orchestrates parallel API calls, handles failures gracefully, and merges heterogeneous data types (structured metrics, unstructured news, time-series data) into a unified decision context that the LLM can reason over.","intents":["I need to see how recent market trends affect my decision","I want to understand the current competitive landscape for this choice","I need to validate my assumptions against real-time data"],"best_for":["Professionals making decisions in fast-moving markets (finance, tech, startups)","Researchers who need current data rather than training-data-based analysis","Teams evaluating market opportunities or competitive threats"],"limitations":["Data integration latency: likely 2-5 seconds per query due to multiple async API calls","Coverage gaps: niche markets, emerging trends, or non-English-language data may not be available","Data quality varies by source: some APIs may return stale or incomplete data","No caching of data between queries, so each analysis re-fetches data (increases latency and API costs)","Free tier likely has limited data source access — premium tiers may include additional APIs"],"requires":["Active internet connection","API keys or credentials for data providers (likely managed by WhyBot backend)","Decision domain with available real-time data (finance, news, trends)"],"input_types":["decision description","optional data source preferences"],"output_types":["synthesized data context (structured and unstructured)","data citations with sources and timestamps","confidence scores based on data freshness and agreement"],"categories":["data-processing-analysis","search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_whybot__cap_7","uri":"capability://text.generation.language.decision.context.inference.from.minimal.input","name":"decision context inference from minimal input","description":"Infers implicit decision context, constraints, and priorities from sparse or ambiguous user input using NLP and domain knowledge. When a user provides minimal information (e.g., 'should I hire Alice or Bob?'), the system infers relevant factors (cost, team fit, timeline, risk) and asks clarifying questions or makes reasonable assumptions to enable analysis without requiring exhaustive user input.","intents":["I want to describe my decision briefly without spelling out every detail","I need the system to ask smart follow-up questions to fill in missing context","I want the system to infer what factors matter based on the decision type"],"best_for":["Busy professionals who don't have time to fill out detailed decision forms","Users making routine decisions (hiring, vendor selection) where context is often similar","Teams using WhyBot for rapid prototyping of decisions"],"limitations":["Inference errors: the system may misinterpret ambiguous input or make incorrect assumptions about priorities","No explicit confirmation: users may not realize what assumptions the system made, leading to misaligned recommendations","Limited to common decision types: novel or domain-specific decisions may not be handled well","No iterative refinement: if initial inference is wrong, users must start over rather than correcting assumptions"],"requires":["Decision description with at least 2 options","Implicit context that the system can infer (e.g., hiring decisions imply cost and team fit)"],"input_types":["minimal natural language description","optional clarifications or constraints"],"output_types":["inferred decision context","clarifying questions (if needed)","assumptions made by the system"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["Active internet connection for real-time data fetching","Browser with JavaScript enabled (Vercel-hosted SPA)","No authentication required for free tier","Decision with 2-5 distinct options","Ability to articulate decision criteria in natural language","Browser with JavaScript enabled","No authentication or account creation required","Decision with sufficient context for the LLM to reason about confidence","Real-time data availability for the decision domain","2-5 distinct options to compare"],"failure_modes":["Real-time data integration latency likely 2-5 seconds per query depending on API availability","Free tier probably rate-limited to 5-10 queries per day, restricting iterative decision refinement","Data freshness depends on upstream API providers — gaps in coverage for niche markets or emerging trends","No apparent caching of decision contexts, so each query must re-fetch data","Factor weighting is opaque — no visibility into how the system assigns weights (learned vs. rule-based)","No sensitivity analysis or 'what-if' tools to explore how weight changes affect outcomes","Limited to pre-defined factor categories; custom factors likely not supported in free tier","Aggregation method (linear sum, multiplicative, Bayesian) not documented, making reproducibility difficult","No session persistence — decisions are not saved, so users cannot revisit or iterate on previous analyses","Parsing errors likely occur with ambiguous or poorly-structured input (e.g., 'should I hire Alice or Bob?' without context)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.67,"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":"2026-05-24T12:16:34.117Z","last_scraped_at":"2026-04-05T13:23:42.553Z","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=whybot","compare_url":"https://unfragile.ai/compare?artifact=whybot"}},"signature":"BYFSraj5n7zrRqsBExw5dtDsghD1LBqs5Y/iVCFjHJFkNkgS79UOurs9Mh5TAyQ8PwKVjsh8gCutDiEXgbM3CQ==","signedAt":"2026-06-22T10:26:15.968Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/whybot","artifact":"https://unfragile.ai/whybot","verify":"https://unfragile.ai/api/v1/verify?slug=whybot","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"}}