{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_ai-scam-detective","slug":"ai-scam-detective","name":"AI Scam Detective","type":"product","url":"https://www.aiscamdetective.com","page_url":"https://unfragile.ai/ai-scam-detective","categories":["automation"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_ai-scam-detective__cap_0","uri":"capability://safety.moderation.real.time.text.based.scam.pattern.detection","name":"real-time text-based scam pattern detection","description":"Analyzes submitted text (emails, messages, offers) against a trained model to identify linguistic and structural patterns commonly associated with scam communications. The system likely uses NLP feature extraction (keyword matching, phrase patterns, urgency indicators, grammar anomalies) combined with a classification model to assign scam probability scores. Returns instant risk assessment without requiring external API calls or domain verification.","intents":["I need to quickly check if this suspicious email is a phishing attempt before clicking any links","I want to verify if this job offer message contains common scam red flags","I need a fast first-pass screening for incoming messages without manual analysis"],"best_for":["Individual users receiving frequent suspicious communications","Small business owners handling customer inquiries and job applications","Non-technical users who need accessible scam detection without learning curve"],"limitations":["Text-only analysis misses visual indicators (fake logos, spoofed sender addresses, malicious URLs)","No sender reputation or domain verification integration—cannot detect compromised legitimate accounts","Cannot analyze attachments, images, or HTML formatting that sophisticated scams exploit","Model accuracy and false positive/negative rates are undisclosed; effectiveness depends on training data recency","No context about evolving scam tactics—detection quality degrades as scammers adapt language patterns"],"requires":["Internet connection for real-time analysis","Text input (minimum ~20 characters for meaningful analysis)","Web browser or API access to AI Scam Detective service"],"input_types":["plain text","email body","message content","offer descriptions"],"output_types":["scam probability score (likely 0-100 or percentage)","risk classification (e.g., 'likely scam', 'suspicious', 'appears legitimate')","brief explanation of detected red flags"],"categories":["safety-moderation","text-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_ai-scam-detective__cap_1","uri":"capability://safety.moderation.instant.scam.risk.classification.with.confidence.scoring","name":"instant scam risk classification with confidence scoring","description":"Processes text input through a trained classification model that outputs discrete risk categories (likely scam, suspicious, legitimate) with associated confidence scores. The system likely uses a neural network or ensemble classifier trained on labeled scam/non-scam datasets, returning structured predictions that indicate both the classification and the model's certainty level. Results are delivered synchronously with minimal latency.","intents":["I need to know the confidence level of the scam detection—is this a borderline case or clearly suspicious?","I want to make a quick go/no-go decision on whether to engage with this message","I need to understand why the system flagged this text as risky"],"best_for":["Users making rapid triage decisions on incoming communications","Scenarios where false positives are costly (e.g., legitimate business inquiries flagged as scams)","Situations requiring confidence thresholds to determine escalation"],"limitations":["Confidence scores may not correlate with actual accuracy—high confidence does not guarantee correct classification","No explanation of which specific phrases or patterns triggered the scam flag","Binary or ternary classification may oversimplify nuanced cases (e.g., legitimate but poorly written messages)","No feedback loop to improve model accuracy based on user corrections","Confidence calibration unknown—scores may be poorly calibrated relative to true probability"],"requires":["Text input in supported language (likely English; other language support unclear)","Minimum text length for meaningful classification (exact threshold unknown)"],"input_types":["plain text"],"output_types":["risk category label","confidence score (0-100 or 0-1 range)","optional: brief reasoning or detected red flags"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_ai-scam-detective__cap_2","uri":"capability://safety.moderation.linguistic.red.flag.extraction.and.highlighting","name":"linguistic red flag extraction and highlighting","description":"Identifies and surfaces specific linguistic markers commonly associated with scams (urgency language, grammatical errors, unusual phrasing, requests for sensitive information, too-good-to-be-true offers). The system likely uses pattern matching, keyword extraction, and NLP feature analysis to isolate suspicious elements within the submitted text. Results highlight which portions of the input triggered scam indicators, enabling users to understand the detection rationale.","intents":["I want to see exactly which phrases in this message are suspicious","I need to understand why this text was flagged as a potential scam","I want to learn what scam indicators to watch for in future messages"],"best_for":["Users who want to develop scam detection intuition","Educators teaching digital literacy and security awareness","Analysts reviewing scam patterns for research or training"],"limitations":["Red flag extraction may produce false positives—legitimate messages with urgency language or informal grammar may be flagged","Context-dependent indicators (e.g., 'urgent' is legitimate in some business contexts) are not disambiguated","No weighting of red flags—all detected indicators treated equally despite varying significance","Cannot distinguish between intentional scam tactics and accidental poor writing","Red flag explanations may be generic rather than tailored to specific scam type"],"requires":["Text input with sufficient length for pattern matching"],"input_types":["plain text"],"output_types":["highlighted text with flagged phrases","list of detected red flags with categories (urgency, grammar, requests for info, etc.)","optional: brief explanation of why each flag is suspicious"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_ai-scam-detective__cap_3","uri":"capability://safety.moderation.stateless.zero.persistence.scam.analysis","name":"stateless, zero-persistence scam analysis","description":"Processes each text submission independently without maintaining user history, conversation context, or persistent state. The system treats every analysis request as atomic—no learning from previous user submissions, no personalization based on past interactions, no feedback loop to improve future detections. This architecture prioritizes privacy and simplicity over adaptive intelligence, enabling the service to operate without user accounts or data retention.","intents":["I want to check if this message is a scam without creating an account or sharing my history","I need to analyze multiple unrelated messages without the system building a profile of my behavior","I want to use this tool anonymously without any data persistence"],"best_for":["Privacy-conscious users who avoid account creation","One-off scam checks without ongoing relationship with the service","Scenarios where user history could introduce bias or privacy concerns"],"limitations":["No personalization—cannot learn that certain senders are consistently legitimate or suspicious","No feedback mechanism—users cannot correct misclassifications to improve future detections","No context accumulation—analyzing a series of related messages provides no advantage over analyzing each independently","Cannot detect patterns across multiple messages from the same scammer","Model improvement requires manual retraining; no online learning from user interactions"],"requires":["No account or authentication required"],"input_types":["plain text"],"output_types":["scam risk assessment"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_ai-scam-detective__cap_4","uri":"capability://automation.workflow.synchronous.low.latency.scam.detection.inference","name":"synchronous, low-latency scam detection inference","description":"Executes scam detection model inference in real-time with sub-second response times, enabling users to receive instant feedback without waiting for batch processing or asynchronous job completion. The system likely uses optimized model serving (quantized models, edge inference, or lightweight architectures) to minimize latency while maintaining accuracy. Results are returned synchronously within a single HTTP request-response cycle.","intents":["I need to know immediately if this message is a scam—I'm deciding whether to respond right now","I want to check multiple messages in rapid succession without waiting between analyses","I need instant feedback to make a quick decision about engaging with this sender"],"best_for":["Real-time decision-making scenarios where delays are costly","Mobile users expecting instant feedback","High-volume screening where latency compounds across many submissions"],"limitations":["Sub-second latency may require model simplification, reducing detection accuracy","No time for complex analysis (e.g., cross-referencing against scam databases, checking sender reputation)","Synchronous architecture cannot scale to extremely high request volumes without infrastructure investment","No caching or memoization across requests—identical text submitted twice requires full re-inference","Latency SLA unknown; actual response times may vary based on server load"],"requires":["Internet connection with reasonable latency to service endpoint","Service availability and uptime"],"input_types":["plain text"],"output_types":["scam risk assessment"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["Internet connection for real-time analysis","Text input (minimum ~20 characters for meaningful analysis)","Web browser or API access to AI Scam Detective service","Text input in supported language (likely English; other language support unclear)","Minimum text length for meaningful classification (exact threshold unknown)","Text input with sufficient length for pattern matching","No account or authentication required","Internet connection with reasonable latency to service endpoint","Service availability and uptime"],"failure_modes":["Text-only analysis misses visual indicators (fake logos, spoofed sender addresses, malicious URLs)","No sender reputation or domain verification integration—cannot detect compromised legitimate accounts","Cannot analyze attachments, images, or HTML formatting that sophisticated scams exploit","Model accuracy and false positive/negative rates are undisclosed; effectiveness depends on training data recency","No context about evolving scam tactics—detection quality degrades as scammers adapt language patterns","Confidence scores may not correlate with actual accuracy—high confidence does not guarantee correct classification","No explanation of which specific phrases or patterns triggered the scam flag","Binary or ternary classification may oversimplify nuanced cases (e.g., legitimate but poorly written messages)","No feedback loop to improve model accuracy based on user corrections","Confidence calibration unknown—scores may be poorly calibrated relative to true probability","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":"2026-05-24T12:16:29.132Z","last_scraped_at":"2026-04-05T13:23:42.561Z","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=ai-scam-detective","compare_url":"https://unfragile.ai/compare?artifact=ai-scam-detective"}},"signature":"wLODmgjujhTF5zuW7LTwiIhpu2WTmqgBIuZPEG4/2w2I9bRJLWM+slCGDcdO/rG/nZkNYiIDcxp9d6NSpGy/Cw==","signedAt":"2026-06-20T21:33:52.209Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/ai-scam-detective","artifact":"https://unfragile.ai/ai-scam-detective","verify":"https://unfragile.ai/api/v1/verify?slug=ai-scam-detective","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"}}