Results for “Cognitive Distortion Identifier”

Identifies and labels cognitive distortions in input text (e.g., catastrophizing, black‑and‑white thinking), returning types, text spans, and brief explanations.

AI-driven enhancer that improves domain-specific clarity, relevance, and cognitive impact of text and content.

Uses AI to identify molecular pathways and interactions linking vitamins to diseases, revealing mechanisms for research and clinical insights.

Generates personalized vitamin and nootropic stacks tailored to a user’s goals, lifestyle, and safety constraints.

AI endpoint that analyzes exercise images or video to identify and label targeted muscle groups and regions.

AI identifies a user's spirit animal from text or images and returns the matched animal with traits and meaning.

Uses AI to analyze child dietary and health data to identify nutrient deficiencies, prioritize gaps, and suggest targeted interventions.

Identifies exercise equipment in images or text, returning type, attributes, and confidence scores for fitness apps and inventories.

Uses AI to identify compounds, excipients, or formulation features that enhance vitamin bioavailability from ingredient lists or text.

AI-driven endpoint that analyzes a child's behavior report and identifies likely underlying causes and contributing factors.

AI endpoint that identifies and annotates vitamin-related metabolites from molecular structures, spectra, or metabolomics data.

Classifies cognitive states (e.g., stress, focus, drowsiness) from breath signals using machine learning.

AI-driven breath training that delivers personalized exercises and real-time guidance to enhance cognitive focus, clarity, and performance.

AI-powered image identifier that recognizes vitamin and supplement pills from photos and returns likely matches with metadata.

AI-driven identifier that detects vitamins and nutraceuticals likely to modify biological aging, returning predicted effects, evidence links, and confidence scores.

Provides trust, security metadata, and contextual cognitive responses for x402 protocol agents via a paid JSON API.

Identifies emotional triggers in text by detecting words, phrases, and patterns likely to provoke specific emotional responses.

Identifies a learner’s preferred learning style (visual, auditory, kinesthetic, etc.) from text or responses to help personalize instruction.