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The system handles multi-page crawling, respects robots.txt, and deduplicates content to build a comprehensive knowledge base without manual content upload or formatting.","intents":["I want to train a chatbot on my existing documentation without manually uploading files","I need to keep the chatbot's knowledge base synchronized with my live documentation site","I want to avoid the overhead of manually structuring and tagging knowledge base articles"],"best_for":["SaaS companies with public documentation sites","Service businesses with comprehensive help centers","Teams without dedicated knowledge management infrastructure"],"limitations":["Crawling depth limited to publicly accessible content — cannot index behind authentication or paywalls","Dynamic content loaded via JavaScript may not be fully captured depending on crawler capabilities","Large documentation sites (10,000+ pages) may require extended crawl times or rate limiting","No support for proprietary document formats — requires HTML, markdown, or plain text sources"],"requires":["Publicly accessible website or documentation URL","Standard HTTP/HTTPS protocol support","Reasonable robots.txt compliance (no aggressive blocking)"],"input_types":["website URL","documentation site URL","HTML content","markdown content"],"output_types":["indexed vector embeddings","normalized text chunks","metadata (source URL, page title, hierarchy)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_doks__cap_1","uri":"capability://memory.knowledge.retrieval.augmented.generation.with.source.grounding","name":"retrieval-augmented generation with source grounding","description":"When a user asks the chatbot a question, Doks retrieves the most relevant content chunks from the indexed knowledge base using semantic similarity search, then passes those chunks as context to an LLM to generate a response grounded in the source material. This approach reduces hallucination by constraining the model to only synthesize information present in the training content, and includes citations or source links in responses.","intents":["I want my chatbot to answer questions accurately without making up information","I need responses to cite the source documentation so users can verify answers","I want to reduce support costs by providing accurate, sourced answers automatically"],"best_for":["Customer support teams prioritizing accuracy over conversational flexibility","Compliance-heavy industries requiring documented sources for answers","Businesses with well-structured, authoritative documentation"],"limitations":["Responses are constrained to information in the training content — cannot answer questions outside the knowledge base scope","Retrieval quality depends on documentation clarity and completeness — poorly written docs produce poor answers","Semantic search may fail on highly technical jargon or domain-specific terminology not well-represented in embeddings","Source citations may be inaccurate if multiple similar chunks exist in the knowledge base"],"requires":["Indexed knowledge base from website/documentation crawl","Vector database with semantic search capability","LLM API access (OpenAI, Anthropic, or similar)"],"input_types":["user question (text)"],"output_types":["generated response (text)","source citations (URLs or document references)","confidence score (optional)"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_doks__cap_2","uri":"capability://automation.workflow.no.code.chatbot.configuration.and.deployment","name":"no-code chatbot configuration and deployment","description":"Doks provides a visual interface for configuring chatbot behavior (tone, response length, fallback messages) and deploying the chatbot to websites via embedded widget, Slack, or other channels without requiring code. The system handles conversation state management, message routing, and channel-specific formatting automatically, allowing non-technical users to launch and iterate on chatbots.","intents":["I want to deploy a chatbot to my website without hiring a developer","I need to customize chatbot tone and behavior without touching code","I want to quickly test different chatbot configurations and measure impact"],"best_for":["Non-technical founders and product managers","Small-to-mid-market businesses without engineering resources","Teams needing rapid iteration on chatbot behavior"],"limitations":["Limited customization compared to code-based solutions — cannot implement custom logic or complex conversation flows","No support for advanced routing (e.g., conditional escalation to human agents based on sentiment or intent)","Widget styling options limited to predefined templates — cannot fully match custom brand designs","No API for programmatic chatbot control or integration with external systems beyond basic webhooks"],"requires":["Doks account with active subscription","Website with HTML access (for widget embedding) or Slack workspace admin access","No coding knowledge required"],"input_types":["configuration parameters (tone, response length, fallback messages)","brand assets (colors, logo)"],"output_types":["embedded chatbot widget","Slack bot integration","conversation logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_doks__cap_3","uri":"capability://search.retrieval.semantic.search.and.relevance.ranking.over.indexed.content","name":"semantic search and relevance ranking over indexed content","description":"Doks uses vector embeddings to convert both user queries and indexed documentation chunks into semantic representations, then ranks chunks by cosine similarity to find the most contextually relevant content for answering a question. The ranking system considers both semantic relevance and metadata (recency, source importance) to surface the best sources for LLM context.","intents":["I want the chatbot to find the most relevant documentation for each user question","I need search results ranked by relevance, not just keyword matching","I want to ensure the chatbot uses the most up-to-date or authoritative sources"],"best_for":["Businesses with large documentation sets (100+ pages) where keyword search is insufficient","Teams with diverse documentation sources that need intelligent prioritization","Support teams handling complex, multi-faceted customer questions"],"limitations":["Semantic search quality depends on embedding model quality — may struggle with domain-specific terminology or rare concepts","Ranking cannot distinguish between contradictory information in the knowledge base — will surface both equally","No support for explicit query expansion or synonym handling — relies on embedding model's understanding","Metadata ranking (recency, importance) requires manual tagging or heuristics — not automatically inferred"],"requires":["Indexed knowledge base with vector embeddings","Vector database with similarity search (e.g., Pinecone, Weaviate, Milvus)","Embedding model (OpenAI, Cohere, or open-source)"],"input_types":["user query (text)"],"output_types":["ranked list of relevant content chunks","similarity scores","source metadata"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_doks__cap_4","uri":"capability://memory.knowledge.conversation.history.and.context.management","name":"conversation history and context management","description":"Doks maintains conversation state across multiple turns, storing user messages and chatbot responses in a session-scoped context window. The system uses conversation history to provide coherent multi-turn interactions, allowing users to ask follow-up questions and the chatbot to maintain context without re-explaining previous answers. Context is managed per user session and automatically cleared after inactivity.","intents":["I want users to have natural multi-turn conversations with the chatbot","I need the chatbot to remember context from previous messages in the same conversation","I want to avoid the chatbot repeating information already discussed in the conversation"],"best_for":["Support scenarios requiring clarification and follow-up questions","Conversational interfaces where users expect natural dialogue flow","Businesses wanting to reduce friction in customer interactions"],"limitations":["Context window is limited to current session — no cross-session memory or user profile learning","Long conversations may exceed token limits of underlying LLM, requiring context truncation or summarization","No explicit conversation branching or alternative path exploration — linear conversation flow only","Session timeout is fixed — no option for persistent user profiles or conversation resumption across sessions"],"requires":["Session storage backend (in-memory, Redis, or database)","LLM with sufficient context window (typically 4K+ tokens)","User session tracking mechanism"],"input_types":["user message (text)","conversation history (previous messages)"],"output_types":["contextually aware response (text)","updated conversation history"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_doks__cap_5","uri":"capability://planning.reasoning.fallback.and.escalation.handling.for.out.of.scope.questions","name":"fallback and escalation handling for out-of-scope questions","description":"When a user question falls outside the scope of the indexed knowledge base (low confidence match or no relevant content found), Doks can be configured to provide a fallback response, suggest related topics, or escalate to a human agent. The system uses confidence thresholds to determine when to escalate rather than risk providing inaccurate information, and can route escalations to email, Slack, or ticketing systems.","intents":["I want the chatbot to know when it doesn't know something and escalate appropriately","I need to prevent the chatbot from making up answers to questions outside its knowledge base","I want to capture escalations and route them to the right support team"],"best_for":["Support teams wanting to use chatbots without risking customer frustration from hallucinations","Businesses with partial documentation coverage that need graceful degradation","Teams using chatbots to triage and route support requests"],"limitations":["Confidence thresholds are static — no dynamic adjustment based on conversation context or user history","Escalation routing is limited to predefined channels — no intelligent agent assignment or load balancing","No feedback loop to improve confidence thresholds based on escalation outcomes","Fallback messages are generic — cannot be personalized based on user context or previous interactions"],"requires":["Confidence scoring mechanism (typically based on retrieval similarity scores)","Escalation channel configuration (email, Slack, ticketing system API)","Fallback message templates"],"input_types":["user question (text)","retrieval confidence score"],"output_types":["fallback response (text)","escalation ticket (structured data)","suggested topics (list)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_doks__cap_6","uri":"capability://tool.use.integration.multi.channel.chatbot.deployment.web.widget.slack.email","name":"multi-channel chatbot deployment (web widget, slack, email)","description":"Doks abstracts the underlying chatbot logic and deploys it across multiple channels (website widget, Slack bot, email integration) with channel-specific formatting and interaction patterns. The system maintains a single knowledge base and conversation engine while adapting the interface and message format for each channel, allowing users to interact with the same chatbot through their preferred medium.","intents":["I want my chatbot available on my website, Slack, and email without building separate integrations","I need consistent chatbot behavior across channels while respecting channel-specific constraints","I want to meet customers where they are without duplicating chatbot logic"],"best_for":["Teams with diverse customer communication channels","Businesses wanting to reduce support overhead across multiple touchpoints","Organizations standardizing on a single chatbot platform"],"limitations":["Channel support is limited to predefined integrations — no custom channel adapters or API for third-party channels","Message formatting is template-based — cannot fully customize appearance or interaction patterns per channel","Conversation context is not shared across channels — users must start new conversations when switching channels","Rate limiting and usage tracking are per-channel, not unified — difficult to monitor total chatbot usage"],"requires":["Doks account with active subscription","Website with HTML access (for widget) or Slack workspace admin access","Email forwarding setup (for email integration)"],"input_types":["user message (text) from any supported channel"],"output_types":["formatted response (text) adapted to channel","channel-specific metadata (e.g., Slack thread ID, email headers)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_doks__cap_7","uri":"capability://data.processing.analysis.analytics.and.conversation.monitoring","name":"analytics and conversation monitoring","description":"Doks tracks chatbot interactions, including user questions, chatbot responses, escalations, and user satisfaction signals (thumbs up/down, ratings). The system provides dashboards showing conversation volume, common questions, escalation rates, and user satisfaction trends, enabling teams to identify gaps in documentation and optimize chatbot performance over time.","intents":["I want to understand what questions users are asking and how well the chatbot is answering them","I need to identify gaps in my documentation based on escalation patterns","I want to measure the impact of the chatbot on support costs and customer satisfaction"],"best_for":["Support teams wanting data-driven insights into chatbot performance","Product managers optimizing documentation and chatbot behavior","Businesses measuring ROI of chatbot deployment"],"limitations":["Analytics are limited to chatbot interactions — no integration with broader customer journey or CRM data","User satisfaction signals are optional and often not provided — analytics may be skewed by low response rates","No predictive analytics or anomaly detection — only historical reporting","Custom report building is not supported — limited to predefined dashboards"],"requires":["Active chatbot deployment with user interactions","Analytics dashboard access (included in Doks subscription)"],"input_types":["conversation logs (user questions, responses, escalations)","user satisfaction ratings (optional)"],"output_types":["analytics dashboards (conversation volume, escalation rates, satisfaction)","common questions report","escalation trends"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_doks__cap_8","uri":"capability://data.processing.analysis.knowledge.base.versioning.and.update.management","name":"knowledge base versioning and update management","description":"Doks tracks versions of the indexed knowledge base as documentation is updated, allowing teams to review what content changed and when. The system can re-crawl documentation sources on a schedule or on-demand, detect changes, and update the vector index incrementally without requiring full re-indexing. Teams can also manually add, edit, or remove content from the knowledge base.","intents":["I want the chatbot's knowledge base to stay synchronized with my live documentation","I need to track what documentation changes affect the chatbot","I want to manually supplement the chatbot's knowledge base with content not on my website"],"best_for":["Teams with frequently updated documentation","Businesses wanting to maintain knowledge base freshness without manual intervention","Support teams needing to add internal knowledge (policies, workarounds) not in public docs"],"limitations":["Incremental updates are limited to detected changes — no support for semantic versioning or content deprecation","Manual content additions are not tracked separately from crawled content — difficult to distinguish internal vs. public knowledge","No rollback capability — cannot revert to previous knowledge base versions","Update frequency is limited to predefined schedules — no real-time synchronization with documentation sources"],"requires":["Doks account with active subscription","Documentation source accessible for re-crawling (website URL or manual uploads)","Update schedule configuration"],"input_types":["updated documentation source (website URL or manual content)","update schedule configuration"],"output_types":["updated vector index","change log (what content was added/removed/modified)","version history"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_doks__cap_9","uri":"capability://text.generation.language.custom.tone.and.response.style.configuration","name":"custom tone and response style configuration","description":"Doks allows teams to configure the chatbot's tone (friendly, professional, technical) and response style (concise, detailed, with examples) through configuration parameters that are passed to the LLM as system prompts. The system applies these style preferences consistently across all responses without requiring prompt engineering or code changes, enabling non-technical users to customize chatbot personality.","intents":["I want the chatbot to match my brand voice and tone","I need to adjust response length and detail level based on user preferences","I want to customize chatbot behavior without hiring a developer"],"best_for":["Brand-conscious teams wanting chatbot personality to match company voice","Businesses with diverse user bases requiring different response styles","Non-technical teams wanting to iterate on chatbot behavior"],"limitations":["Tone configuration is limited to predefined templates — no freeform system prompt editing","Style preferences are global — cannot vary tone per user, conversation, or topic","No A/B testing framework — cannot easily compare different tone configurations","LLM may not consistently follow tone instructions, especially for complex or ambiguous requests"],"requires":["Doks account with chatbot configuration access","No coding knowledge required"],"input_types":["tone preference (friendly, professional, technical, etc.)","response style preference (concise, detailed, with examples, etc.)"],"output_types":["configured system prompt","chatbot responses styled according to preferences"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["Publicly accessible website or documentation URL","Standard HTTP/HTTPS protocol support","Reasonable robots.txt compliance (no aggressive blocking)","Indexed knowledge base from website/documentation crawl","Vector database with semantic search capability","LLM API access (OpenAI, Anthropic, or similar)","Doks account with active subscription","Website with HTML access (for widget embedding) or Slack workspace admin access","No coding knowledge required","Indexed knowledge base with vector embeddings"],"failure_modes":["Crawling depth limited to publicly accessible content — cannot index behind authentication or paywalls","Dynamic content loaded via JavaScript may not be fully captured depending on crawler capabilities","Large documentation sites (10,000+ pages) may require extended crawl times or rate limiting","No support for proprietary document formats — requires HTML, markdown, or plain text sources","Responses are constrained to information in the training content — cannot answer questions outside the knowledge base scope","Retrieval quality depends on documentation clarity and completeness — poorly written docs produce poor answers","Semantic search may fail on highly technical jargon or domain-specific terminology not well-represented in embeddings","Source citations may be inaccurate if multiple similar chunks exist in the knowledge base","Limited customization compared to code-based solutions — cannot implement custom logic or complex conversation flows","No support for advanced routing (e.g., conditional escalation to human agents based on sentiment or intent)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.78,"ecosystem":0.25,"match_graph":0.25,"freshness":0.6,"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=doks","compare_url":"https://unfragile.ai/compare?artifact=doks"}},"signature":"PbrZPgG0g7nlqCR8RHKYpsVcxiRawTHvy1YciXG+0Zx0cXBXzV8ocCbGBpkHe2zyoQfAeZDtgeCLnMxO1v0JDQ==","signedAt":"2026-07-08T03:05:35.094Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/doks","artifact":"https://unfragile.ai/doks","verify":"https://unfragile.ai/api/v1/verify?slug=doks","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"}}