{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_silatus","slug":"silatus","name":"Silatus","type":"product","url":"https://silatus.com","page_url":"https://unfragile.ai/silatus","categories":["text-writing"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_silatus__cap_0","uri":"capability://text.generation.language.fact.checked.content.generation.with.source.attribution","name":"fact-checked content generation with source attribution","description":"Generates written content (articles, reports, blog posts) while simultaneously verifying claims against a knowledge base and external sources, returning only statements that pass fact-checking validation. The system appears to use a verify-as-you-generate approach rather than post-hoc fact-checking, embedding source lookups into the generation pipeline to prevent hallucinations before they're committed to output. Each claim is tagged with source citations, enabling readers to trace assertions back to their origins.","intents":["Generate news articles or research summaries without manually fact-checking every claim","Create compliance-ready documentation where every statement must be verifiable and sourced","Produce academic or journalistic content that requires transparent attribution of sources","Reduce editorial review cycles by eliminating hallucinated facts at generation time"],"best_for":["Journalists and news organizations publishing under editorial standards","Researchers and academics writing literature reviews or reports","Compliance and legal professionals drafting regulated content","Enterprise teams where factual accuracy is non-negotiable (healthcare, finance, government)"],"limitations":["Fact-checking overhead introduces latency — generation speed is slower than unchecked AI writers, likely 2-5x slower depending on claim density","Accuracy is bounded by source database coverage — claims about niche topics, recent events, or proprietary data may fail verification","Requires pre-indexed knowledge base or real-time API access to fact-checking services, adding infrastructure dependency","Cannot generate speculative, hypothetical, or creative content that lacks factual grounding"],"requires":["Internet connectivity for source verification and citation lookup","Access to fact-checking knowledge base (proprietary or third-party API)","Content topic must be within scope of indexed sources (news, academic, public domain preferred)"],"input_types":["text prompts describing desired content topic and scope","structured briefs with key facts to incorporate","source documents or URLs to synthesize from"],"output_types":["formatted articles with inline citations","structured JSON with claims and source metadata","markdown or HTML with hyperlinked sources"],"categories":["text-generation-language","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_silatus__cap_1","uri":"capability://data.processing.analysis.claim.extraction.and.verification.from.user.provided.content","name":"claim extraction and verification from user-provided content","description":"Analyzes existing text (drafts, articles, reports) to identify factual claims, then validates each claim against a fact-checking knowledge base, flagging unverified or contradicted statements. This operates as a content audit tool, scanning for hallucinations or inaccuracies in human-written or AI-generated text and surfacing them with confidence scores and source evidence.","intents":["Audit AI-generated content before publication to catch hallucinations","Verify claims in user-drafted articles or reports without manual research","Identify which statements in a document lack sufficient source support","Generate fact-check reports for editorial review workflows"],"best_for":["Editorial teams reviewing AI-assisted or human-written content","Content moderation and fact-checking workflows","Compliance teams auditing documentation for accuracy","Publishers and news organizations with multi-stage review processes"],"limitations":["Fact-checking accuracy depends on source database quality — obscure or recent claims may be marked unverifiable even if true","Cannot distinguish between intentional creative fiction and factual errors","Requires significant computational overhead to extract and verify all claims in long documents","False positives/negatives in claim extraction reduce utility for automated workflows"],"requires":["Text input (minimum ~100 words for meaningful claim extraction)","Access to fact-checking knowledge base or API","Configured confidence thresholds for claim verification"],"input_types":["plain text articles or documents","markdown or HTML formatted content","AI-generated text outputs"],"output_types":["annotated text with flagged claims","JSON report with claim-level verification metadata","confidence scores and source evidence for each claim"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_silatus__cap_2","uri":"capability://memory.knowledge.source.aware.context.retrieval.for.content.generation","name":"source-aware context retrieval for content generation","description":"Retrieves relevant, verified sources (articles, research papers, databases) based on content topic and incorporates them as grounding context for generation. The system prioritizes high-quality, authoritative sources and makes source selection transparent to the user, allowing them to see which documents informed each generated claim. This is a memory-knowledge capability that uses source retrieval to constrain the generation space.","intents":["Generate content grounded in specific authoritative sources rather than generic training data","Ensure generated content cites recent or domain-specific sources relevant to the topic","Allow users to control which sources inform content generation for transparency","Reduce hallucinations by limiting generation to claims supported by retrieved sources"],"best_for":["Researchers synthesizing literature reviews from curated sources","Journalists writing stories that require specific source documents","Compliance professionals generating documentation from regulatory sources","Teams needing transparent, auditable content generation with known source lineage"],"limitations":["Source retrieval quality depends on indexing coverage — niche topics or proprietary data may have limited source availability","Retrieval latency adds overhead to generation (likely 500ms-2s per request depending on source database size)","User must configure source preferences upfront; dynamic source discovery during generation is computationally expensive","Cannot generate content about topics with no indexed sources, even if user has external knowledge"],"requires":["Indexed source database (proprietary or third-party knowledge base)","Topic specification or query to drive source retrieval","Semantic search or retrieval infrastructure (vector embeddings or keyword indexing)"],"input_types":["content topic or query","source preferences or constraints (e.g., 'only peer-reviewed sources', 'published after 2020')","optional: specific source documents to prioritize"],"output_types":["generated content with source citations","metadata showing which sources informed each claim","ranked list of retrieved sources with relevance scores"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_silatus__cap_3","uri":"capability://planning.reasoning.interactive.claim.refinement.and.source.negotiation","name":"interactive claim refinement and source negotiation","description":"Allows users to iteratively refine generated content by challenging specific claims, requesting alternative sources, or adjusting fact-checking strictness. The system re-generates or modifies content based on user feedback, showing how different source selections or verification thresholds affect the final output. This creates a human-in-the-loop workflow where users maintain editorial control while leveraging AI for generation.","intents":["Adjust fact-checking sensitivity for different content types (news vs. opinion vs. analysis)","Request alternative sources or citations for claims the user disputes","Understand why specific claims were flagged or rejected by the fact-checker","Iteratively improve content quality through guided refinement cycles"],"best_for":["Editorial teams with domain expertise who want to override or negotiate fact-checking decisions","Researchers synthesizing complex topics where source authority is subjective","Compliance professionals who need to document decision rationale for content choices","Content creators who want AI assistance but maintain final editorial authority"],"limitations":["Iterative refinement adds latency and complexity to content creation workflows","User must have domain expertise to meaningfully negotiate with fact-checker (non-experts may override valid corrections)","No built-in audit trail of user overrides — requires external logging for compliance workflows","Refinement cycles can become open-ended, reducing time savings vs. manual fact-checking"],"requires":["Interactive UI or API supporting multi-turn conversations","Fact-checking system that can explain rejection reasons and alternative sources","User authentication and session management for iterative workflows"],"input_types":["user feedback on generated claims (accept/reject/modify)","requests for alternative sources or citations","adjustments to fact-checking parameters (confidence thresholds, source preferences)"],"output_types":["refined content reflecting user feedback","explanation of fact-checking decisions and alternatives","audit trail of user overrides and rationale"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_silatus__cap_4","uri":"capability://text.generation.language.multi.format.content.generation.with.consistent.fact.checking","name":"multi-format content generation with consistent fact-checking","description":"Generates content in multiple formats (articles, summaries, social media posts, reports) from the same source material while maintaining consistent fact-checking across all outputs. The system ensures that claims made in a summary match those in the full article, and that social media excerpts don't misrepresent the original sources. This prevents the common problem of different formats contradicting each other.","intents":["Create article + summary + social media posts from one set of sources without fact inconsistencies","Generate multiple content variants (short-form, long-form, technical, non-technical) that all cite the same sources","Ensure marketing copy derived from research doesn't misrepresent the underlying facts","Maintain factual consistency across a content suite (blog post, newsletter, social media, press release)"],"best_for":["Content marketing teams producing multi-channel content from research","News organizations publishing stories across web, print, and social media","Enterprise communications teams creating consistent messaging across formats","Academic and research teams publishing findings in multiple venues"],"limitations":["Requires explicit format specifications and tone preferences, adding configuration overhead","Fact-checking consistency checks add computational cost — likely 30-50% slower than single-format generation","Cannot guarantee semantic equivalence across formats — some nuance may be lost in condensation","Requires source material to be sufficiently rich to support multiple format variants"],"requires":["Source material or topic specification","Format specifications (article length, summary length, social media character limits, etc.)","Tone/style preferences for each format"],"input_types":["source documents or topic","format specifications (article, summary, social post, report, etc.)","tone and audience preferences per format"],"output_types":["multiple formatted outputs (article, summary, social posts, etc.)","shared source citations across all formats","consistency report showing claim alignment across formats"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_silatus__cap_5","uri":"capability://data.processing.analysis.source.credibility.scoring.and.authority.ranking","name":"source credibility scoring and authority ranking","description":"Evaluates and ranks sources by credibility metrics (publication reputation, author expertise, peer review status, recency, citation count) rather than just relevance. The system assigns authority scores to sources and uses these to weight claims during generation, prioritizing information from high-credibility sources. This is a data-processing capability that transforms raw source metadata into actionable credibility signals.","intents":["Understand which sources are most authoritative for a given topic","Prioritize claims from peer-reviewed research over opinion pieces or blogs","Identify when sources conflict and understand the credibility difference","Generate content that preferentially cites high-authority sources"],"best_for":["Academic and research teams where source authority is critical","Journalists evaluating competing claims from different sources","Compliance and legal professionals who must cite authoritative sources","Teams working in regulated industries (healthcare, finance) where source credibility is non-negotiable"],"limitations":["Credibility scoring is heuristic-based and may not capture domain-specific authority (e.g., a niche expert may have low citation count but high domain authority)","Requires metadata about sources (publication, author, peer review status, etc.) — works poorly with proprietary or unstructured sources","Authority scoring can be gamed or outdated (e.g., a previously credible source may have lost reputation)","Cannot distinguish between legitimate disagreement among credible sources and actual misinformation"],"requires":["Indexed source database with credibility metadata (publication, author, peer review status, citation metrics)","Credibility scoring model or algorithm (proprietary or third-party)","Access to citation metrics and publication reputation data"],"input_types":["source documents or URLs","topic or domain context for authority evaluation","optional: custom credibility criteria or weights"],"output_types":["credibility scores for sources (0-100 or similar scale)","ranked source lists by authority","explanation of credibility factors for each source","content generated with source authority weighting"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_silatus__cap_6","uri":"capability://text.generation.language.real.time.fact.checking.during.content.editing","name":"real-time fact-checking during content editing","description":"Monitors user edits in real-time and flags claims as they're typed or pasted, providing instant feedback on factual accuracy without requiring a full document re-check. This operates as a live fact-checking layer integrated into the editing interface, similar to spell-check but for factual claims. The system uses lightweight claim detection and quick lookups to minimize latency.","intents":["Get instant feedback on claim accuracy while drafting content","Catch hallucinations or inaccuracies immediately rather than in post-hoc review","Understand which claims need sources before finalizing content","Reduce editorial review cycles by pre-checking content during creation"],"best_for":["Individual writers and journalists drafting content in real-time","Editorial teams using shared editing tools with built-in fact-checking","Compliance professionals drafting regulated content who need instant accuracy feedback","Content creators who want to catch errors before submission"],"limitations":["Real-time fact-checking requires low-latency infrastructure — adds 100-500ms per keystroke in worst case, which can feel sluggish","Claim detection in real-time is less accurate than full-document analysis (may miss complex multi-sentence claims)","Cannot fact-check incomplete sentences or claims in progress, leading to false positives","Requires significant computational resources to scale to many concurrent users"],"requires":["Real-time editing interface (web editor, VS Code extension, Google Docs plugin, etc.)","Low-latency fact-checking backend (likely cached or indexed for speed)","Lightweight claim detection model optimized for speed over accuracy"],"input_types":["text being typed or edited in real-time","document context for claim interpretation"],"output_types":["inline annotations/highlights for flagged claims","tooltips with fact-check status and sources","optional: suggestions for corrected claims"],"categories":["text-generation-language","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_silatus__cap_7","uri":"capability://memory.knowledge.domain.specific.fact.checking.with.custom.knowledge.bases","name":"domain-specific fact-checking with custom knowledge bases","description":"Allows organizations to configure custom fact-checking knowledge bases for domain-specific content (internal policies, proprietary data, specialized terminology). The system can be trained on or indexed with organization-specific documents, enabling fact-checking against internal truth rather than just public sources. This is a memory-knowledge capability that extends the fact-checking system to private/proprietary domains.","intents":["Fact-check internal documentation against company policies and standards","Verify claims about proprietary products, services, or data","Ensure marketing copy accurately represents internal specifications","Maintain consistency across internal communications using a shared knowledge base"],"best_for":["Enterprise teams with proprietary knowledge bases or internal standards","Regulated industries (healthcare, finance) with domain-specific terminology and standards","Organizations with strict brand guidelines or messaging standards","Teams managing internal communications, training materials, or documentation"],"limitations":["Requires significant effort to build and maintain custom knowledge bases (data entry, curation, updates)","Custom knowledge bases may become stale or inconsistent without governance processes","Cannot fact-check against external sources if custom knowledge base is the only source","Requires data security and access controls to protect proprietary information"],"requires":["Custom knowledge base or document collection (internal policies, specs, standards, etc.)","Indexing or embedding infrastructure to make custom knowledge base searchable","Data governance process to maintain knowledge base accuracy and currency","Access controls and authentication for proprietary information"],"input_types":["custom documents or knowledge base to index","content to fact-check against custom knowledge base","optional: domain-specific terminology or ontology"],"output_types":["fact-check results against custom knowledge base","citations to internal documents","flagged inconsistencies with internal standards"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["Internet connectivity for source verification and citation lookup","Access to fact-checking knowledge base (proprietary or third-party API)","Content topic must be within scope of indexed sources (news, academic, public domain preferred)","Text input (minimum ~100 words for meaningful claim extraction)","Access to fact-checking knowledge base or API","Configured confidence thresholds for claim verification","Indexed source database (proprietary or third-party knowledge base)","Topic specification or query to drive source retrieval","Semantic search or retrieval infrastructure (vector embeddings or keyword indexing)","Interactive UI or API supporting multi-turn conversations"],"failure_modes":["Fact-checking overhead introduces latency — generation speed is slower than unchecked AI writers, likely 2-5x slower depending on claim density","Accuracy is bounded by source database coverage — claims about niche topics, recent events, or proprietary data may fail verification","Requires pre-indexed knowledge base or real-time API access to fact-checking services, adding infrastructure dependency","Cannot generate speculative, hypothetical, or creative content that lacks factual grounding","Fact-checking accuracy depends on source database quality — obscure or recent claims may be marked unverifiable even if true","Cannot distinguish between intentional creative fiction and factual errors","Requires significant computational overhead to extract and verify all claims in long documents","False positives/negatives in claim extraction reduce utility for automated workflows","Source retrieval quality depends on indexing coverage — niche topics or proprietary data may have limited source availability","Retrieval latency adds overhead to generation (likely 500ms-2s per request depending on source database size)","builder identity is not verified yet","no observed match outcomes 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