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The system likely uses DOM parsing and text selection APIs to capture context, then submits claims to a backend verification engine that cross-references against fact-checking databases and knowledge sources.","intents":["I want to verify a claim I'm reading on a news website without leaving the page or copying text manually","I need to quickly check if a social media post contains false information before sharing it","I'm researching a topic and want inline verification of claims as I browse"],"best_for":["journalists and newsroom staff verifying sources during article research","social media managers screening user-generated content for misinformation","researchers and academics fact-checking sources in real-time"],"limitations":["accuracy depends entirely on underlying fact-checking databases and training data, which are not transparently disclosed","no confidence scoring or uncertainty quantification — results presented as binary true/false without nuance for gray-area claims","browser extension adds latency to page load and may not work reliably with JavaScript-heavy or dynamically-rendered content","limited to claims that can be extracted as discrete text units — struggles with visual misinformation or implicit claims"],"requires":["modern web browser with extension support (Chrome, Firefox, Edge)","internet connection for backend API calls","free Debunkd account or API key"],"input_types":["selected text from web pages","full page content","social media post text"],"output_types":["verification status (true/false/unverifiable)","brief explanation or source reference"],"categories":["safety-moderation","browser-extension"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_debunkd__cap_1","uri":"capability://data.processing.analysis.ai.powered.claim.extraction.and.normalization","name":"ai-powered claim extraction and normalization","description":"Debunkd uses natural language processing to parse unstructured text and extract discrete, verifiable claims from longer passages, normalizing them into a canonical form suitable for fact-checking. This likely involves NLP models (possibly transformer-based) that identify claim boundaries, resolve pronouns and references, and convert colloquial phrasing into standardized statements that can be matched against fact-checking databases.","intents":["I want to automatically identify the specific claims in a long article or speech without manually parsing it","I need to normalize variations of the same claim (e.g., 'COVID vaccines cause autism' vs 'vaccines linked to autism') to check them against the same fact-check","I want to extract claims from unstructured user-generated content for bulk verification"],"best_for":["content moderation teams processing high volumes of user posts","researchers analyzing misinformation spread across multiple sources","newsrooms automating claim extraction from speeches or press releases"],"limitations":["NLP-based extraction may miss implicit or contextual claims that require world knowledge","normalization can introduce false positives by over-generalizing distinct claims into a single category","performance degrades on non-English text or highly specialized domain language","no mechanism to handle claims that are true in some contexts but false in others (context-dependent claims)"],"requires":["text input in supported languages (primarily English)","API access to Debunkd backend","sufficient context window for multi-sentence passages"],"input_types":["unstructured text","articles","social media posts","transcribed speech"],"output_types":["structured claim objects with extracted text and metadata","normalized claim representation"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_debunkd__cap_2","uri":"capability://search.retrieval.multi.source.fact.check.database.lookup.and.aggregation","name":"multi-source fact-check database lookup and aggregation","description":"Debunkd queries multiple fact-checking databases and knowledge sources (likely including Snopes, FactCheck.org, PolitiFact, and academic fact-checking datasets) to retrieve existing fact-checks for extracted claims, then aggregates results to surface consensus or disagreement across sources. The system likely uses semantic similarity matching or claim-to-fact-check indexing to find relevant fact-checks even when phrasing differs.","intents":["I want to know what established fact-checkers have already verified about a claim","I need to see if there's consensus across multiple fact-checking sources or if they disagree","I want to find the most authoritative or recent fact-check for a claim without manually searching multiple websites"],"best_for":["journalists verifying claims against established fact-checking consensus","content moderators needing quick reference to existing fact-checks","researchers studying misinformation patterns across fact-checking organizations"],"limitations":["fact-check coverage is incomplete — many claims, especially recent or niche ones, may not have existing fact-checks in aggregated databases","fact-checking sources themselves have varying methodologies and biases; aggregation doesn't resolve underlying disagreements","no transparency about which sources are queried or how conflicts between sources are resolved","relies on external fact-checking organizations' update frequency — may return outdated fact-checks for evolving claims"],"requires":["API access to fact-checking databases (Snopes, FactCheck.org, etc.)","internet connectivity for database queries","claim in a form that can be matched against existing fact-checks"],"input_types":["normalized claim text","claim metadata (date, context)"],"output_types":["list of matching fact-checks with sources","aggregated verdict (true/false/mixed/unverifiable)","source citations and URLs"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_debunkd__cap_3","uri":"capability://automation.workflow.free.tier.fact.checking.with.optional.premium.verification","name":"free-tier fact-checking with optional premium verification","description":"Debunkd offers a freemium model where basic fact-checking (claim extraction, database lookup, verdict retrieval) is available without payment, with premium tiers offering enhanced features like deeper verification, confidence scoring, or priority processing. The system likely uses rate-limiting and feature gating to differentiate tiers while keeping the core verification pipeline accessible to all users.","intents":["I want to try fact-checking without committing to a paid subscription","I need basic verification for occasional fact-checks but don't want to pay for enterprise features","I want to upgrade to premium verification only for high-stakes claims that need deeper investigation"],"best_for":["individual content creators and researchers with occasional fact-checking needs","non-profits and NGOs with limited budgets for verification tools","students and academics exploring fact-checking capabilities"],"limitations":["free tier likely has rate limits (e.g., X fact-checks per day) that may be insufficient for high-volume content moderation","premium features are not clearly specified in available documentation, making it difficult to assess upgrade value","free tier may have reduced accuracy or slower response times compared to premium","no indication of whether free tier includes access to all fact-checking sources or a limited subset"],"requires":["free Debunkd account (email signup)","no payment method required for free tier"],"input_types":["text claims"],"output_types":["verification results","optional premium features (if upgraded)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_debunkd__cap_4","uri":"capability://automation.workflow.batch.claim.verification.for.content.moderation.workflows","name":"batch claim verification for content moderation workflows","description":"Debunkd supports processing multiple claims in bulk, enabling content moderation teams to verify large volumes of user-generated content efficiently. The system likely accepts batch API requests or CSV uploads, processes claims in parallel or queued fashion, and returns structured results suitable for integration into moderation dashboards or automated content filtering pipelines.","intents":["I need to fact-check hundreds of social media posts daily without manually checking each one","I want to integrate fact-checking into my content moderation pipeline to automatically flag potentially false claims","I need to generate reports on the prevalence of specific false claims across my platform"],"best_for":["social media platforms and community managers moderating user-generated content","news organizations processing high volumes of reader comments or submissions","misinformation research teams analyzing claim spread across datasets"],"limitations":["batch processing may introduce latency — results may not be available in real-time for live moderation","no indication of how batch processing handles rate limits or prioritization of urgent claims","accuracy may degrade when processing claims without sufficient context (e.g., isolated social media posts)","batch results lack human review, so false positives/negatives could lead to over-moderation or missed misinformation"],"requires":["API access to Debunkd batch endpoints","structured input format (JSON, CSV, or similar)","sufficient API quota for batch size"],"input_types":["CSV with claim text","JSON array of claim objects","bulk text uploads"],"output_types":["structured verification results with verdicts","CSV or JSON export of results","moderation flags or confidence scores"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_debunkd__cap_5","uri":"capability://memory.knowledge.claim.context.preservation.and.source.attribution","name":"claim context preservation and source attribution","description":"Debunkd maintains metadata about the source, date, and context of claims being verified, enabling users to understand where claims originated and how they've been used. The system likely stores claim provenance (URL, timestamp, author) and links fact-checks back to original sources, supporting traceability and helping users assess whether a fact-check applies to their specific claim instance.","intents":["I want to know where a claim originated and when it started circulating","I need to verify that a fact-check applies to the specific version of a claim I'm investigating","I want to track how a false claim has spread across different sources and platforms"],"best_for":["journalists tracing the origin and spread of misinformation","researchers studying how false claims evolve and propagate","fact-checkers verifying that their fact-checks apply to specific claim instances"],"limitations":["context preservation depends on user providing source information — browser extension may not capture full context from dynamic pages","no indication of how system handles claims that have evolved or been reworded over time","source attribution may be incomplete for claims from private or paywalled sources","no built-in mechanism to track claim mutations or variations across platforms"],"requires":["source URL or metadata provided by user or extracted from browser context","timestamp of claim (automatic from browser, or manual input)"],"input_types":["claim text with source URL","claim with timestamp","claim with author information"],"output_types":["claim record with full provenance metadata","fact-check results linked to specific claim instance","claim spread/propagation data"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_debunkd__cap_6","uri":"capability://tool.use.integration.api.based.programmatic.fact.checking.integration","name":"api-based programmatic fact-checking integration","description":"Debunkd exposes REST or GraphQL APIs allowing developers to integrate fact-checking capabilities into custom applications, workflows, or platforms. The API likely accepts claim text and optional metadata, returns structured verification results, and supports authentication via API keys, enabling third-party developers to build fact-checking into their own tools without reimplementing verification logic.","intents":["I want to add fact-checking to my custom content moderation tool without building verification from scratch","I need to integrate Debunkd fact-checking into my newsroom workflow management system","I want to build a fact-checking chatbot or assistant that uses Debunkd as a backend verification service"],"best_for":["developers building custom fact-checking tools or integrations","platform teams adding fact-checking to existing content moderation systems","startups building misinformation detection products"],"limitations":["API documentation and rate limits are not publicly detailed, making it difficult to assess scalability","no indication of SLA or uptime guarantees for API availability","API responses may lack confidence scores or nuance handling, limiting ability to build sophisticated verification UIs","authentication and billing model for API usage not clearly specified"],"requires":["API key from Debunkd account","HTTP client library (any language)","internet connectivity"],"input_types":["JSON request with claim text","optional metadata (source, date, context)"],"output_types":["JSON response with verification verdict","source citations","optional confidence or nuance data"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["modern web browser with extension support (Chrome, Firefox, Edge)","internet connection for backend API calls","free Debunkd account or API key","text input in supported languages (primarily English)","API access to Debunkd backend","sufficient context window for multi-sentence passages","API access to fact-checking databases (Snopes, FactCheck.org, etc.)","internet connectivity for database queries","claim in a form that can be matched against existing fact-checks","free Debunkd account (email signup)"],"failure_modes":["accuracy depends entirely on underlying fact-checking databases and training data, which are not transparently disclosed","no confidence scoring or uncertainty quantification — results presented as binary true/false without nuance for gray-area claims","browser extension adds latency to page load and may not work reliably with JavaScript-heavy or dynamically-rendered content","limited to claims that can be extracted as discrete text units — struggles with visual misinformation or implicit claims","NLP-based extraction may miss implicit or contextual claims that require world knowledge","normalization can introduce false positives by over-generalizing distinct claims into a single category","performance degrades on non-English text or highly specialized domain language","no mechanism to handle claims that are true in some contexts but false in others (context-dependent claims)","fact-check coverage is incomplete — many claims, especially recent or niche ones, may not have existing fact-checks in aggregated databases","fact-checking sources themselves have varying methodologies and biases; aggregation doesn't resolve underlying disagreements","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.7300000000000001,"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":"2026-05-24T12:16:30.283Z","last_scraped_at":"2026-04-05T13:23:42.552Z","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=debunkd","compare_url":"https://unfragile.ai/compare?artifact=debunkd"}},"signature":"fm126sPdGJmhcKdxHtJytoxTC1QVqW66f3dRyyagB/ws94w/NEKioiKCoo6Y7a6yAmVlGD/vK6mk4w++uNg3CA==","signedAt":"2026-06-23T02:12:57.434Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/debunkd","artifact":"https://unfragile.ai/debunkd","verify":"https://unfragile.ai/api/v1/verify?slug=debunkd","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"}}