{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_proseable","slug":"proseable","name":"Proseable","type":"agent","url":"https://www.proseable.com","page_url":"https://unfragile.ai/proseable","categories":["chatbots-assistants"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_proseable__cap_0","uri":"capability://text.generation.language.conversational.dialogue.practice.with.ai.tutor","name":"conversational-dialogue-practice-with-ai-tutor","description":"Enables real-time two-way conversation between learner and AI language model, simulating natural dialogue without human tutors. The system maintains conversation context across multiple turns, adapts difficulty based on learner responses, and generates contextually appropriate follow-up prompts to sustain engagement. Uses LLM-based turn-taking with conversation state management to track dialogue history and learner proficiency signals.","intents":["Practice speaking and writing in a target language through natural conversation without waiting for human tutor availability","Engage in unscripted dialogue that adapts to my proficiency level rather than following rigid lesson scripts","Have unlimited conversation practice sessions without per-minute tutoring costs"],"best_for":["Intermediate language learners seeking conversational fluency without subscription costs","Self-motivated learners who can self-direct practice without structured curriculum scaffolding","Learners in regions with limited access to affordable human tutors"],"limitations":["No persistent conversation memory across sessions—each dialogue starts fresh without learner history context","AI responses may not catch subtle cultural nuances or regional dialect variations that human tutors would naturally address","Lacks ability to correct deeply ingrained pronunciation patterns through repeated feedback loops over weeks","Cannot simulate real-world social pressure or emotional stakes that motivate language use in authentic contexts"],"requires":["Internet connection for real-time API calls to LLM backend","Web browser or mobile app with text input capability","Basic proficiency in target language (A2+ CEFR level recommended for meaningful dialogue)"],"input_types":["text (user messages in target language)","optional: audio (if speech-to-text preprocessing is implemented)"],"output_types":["text (AI tutor responses in target language)","optional: audio (if text-to-speech synthesis is implemented)"],"categories":["text-generation-language","conversational-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_proseable__cap_1","uri":"capability://text.generation.language.real.time.grammar.and.syntax.feedback","name":"real-time-grammar-and-syntax-feedback","description":"Analyzes learner text input for grammatical errors, syntax violations, and structural mistakes in the target language, providing immediate corrective feedback with explanations. The system identifies error type (tense, agreement, word order, etc.), highlights the problematic phrase, and explains the grammatical rule violated. Uses NLP-based error detection (likely dependency parsing or rule-based grammar checkers) combined with LLM-generated explanations to contextualize corrections within the learner's current dialogue.","intents":["Receive immediate correction when I make grammar mistakes so I can learn the rule in context","Understand WHY my sentence is wrong, not just that it's wrong, to internalize grammatical patterns","Identify recurring grammar errors across multiple practice sessions to focus remediation efforts"],"best_for":["Intermediate learners transitioning from basic vocabulary to complex sentence construction","Self-directed learners who can interpret grammatical explanations without teacher scaffolding","Learners seeking to accelerate grammar mastery through high-frequency feedback loops"],"limitations":["Grammar rule explanations may be overly technical or use metalanguage unfamiliar to beginner learners","Cannot distinguish between intentional stylistic choices and genuine errors (e.g., poetic word order)","Feedback latency may exceed 500ms for complex error analysis, disrupting conversational flow","No tracking of error patterns across sessions—each feedback event is isolated without learner error history"],"requires":["Target language must have robust NLP tooling (dependency parsers, POS taggers) available","Learner must have intermediate grammar knowledge to understand rule-based explanations","API access to grammar checking service (LanguageTool, custom rule engine, or LLM-based analysis)"],"input_types":["text (learner-generated sentences in target language)"],"output_types":["structured feedback (error location, error type, corrected form, rule explanation)","text (human-readable explanation of grammatical rule)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_proseable__cap_2","uri":"capability://image.visual.pronunciation.feedback.and.accent.assessment","name":"pronunciation-feedback-and-accent-assessment","description":"Analyzes learner speech input to assess pronunciation accuracy, identify accent patterns, and provide corrective guidance on phoneme production. The system likely uses speech-to-text conversion to capture phonetic output, compares against target language phoneme inventory, and generates feedback on specific sounds requiring improvement. May employ acoustic feature analysis or phoneme-level error detection to pinpoint mispronunciations beyond simple transcription errors.","intents":["Get feedback on my pronunciation of specific words or phrases to improve accent and intelligibility","Identify which phonemes I struggle with most so I can focus pronunciation drills on problem sounds","Practice speaking without fear of judgment, receiving objective feedback on speech quality"],"best_for":["Intermediate learners with basic speaking ability seeking accent reduction and pronunciation refinement","Learners in non-English-speaking regions where access to native speaker feedback is limited","Self-conscious learners who prefer AI feedback over human judgment for pronunciation practice"],"limitations":["Speech-to-text accuracy degrades significantly for non-native accents, creating circular feedback problems where mispronunciations aren't recognized as errors","Cannot assess suprasegmental features (stress, intonation, rhythm) that significantly impact intelligibility—only phoneme-level accuracy","Requires quiet environment and quality microphone; background noise causes false negatives in error detection","No long-term tracking of pronunciation improvement—each session is independent without learner progress history"],"requires":["Microphone or audio input device with minimum SNR (signal-to-noise ratio) of 20dB","Speech-to-text API (Google Cloud Speech-to-Text, Azure Speech Services, or Whisper) with target language support","Phoneme inventory and pronunciation rules for target language","Quiet environment for accurate audio capture"],"input_types":["audio (learner speech in target language)"],"output_types":["text (phonetic transcription of learner speech)","structured feedback (phoneme-level errors, accuracy score, corrected pronunciation)","audio (native speaker pronunciation example for comparison)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_proseable__cap_3","uri":"capability://planning.reasoning.adaptive.difficulty.progression.within.dialogue","name":"adaptive-difficulty-progression-within-dialogue","description":"Dynamically adjusts conversation complexity, vocabulary level, and grammatical structures based on real-time assessment of learner performance during dialogue. The system monitors response accuracy, response latency, vocabulary recognition, and grammar correctness to infer proficiency level, then modulates AI tutor prompts to maintain optimal challenge level (zone of proximal development). Uses learner signal classification (error rate, response time, vocabulary coverage) to trigger difficulty adjustments without explicit learner input.","intents":["Have conversations that automatically match my current proficiency level without manual difficulty selection","Progress naturally from simpler to more complex topics as I demonstrate mastery during practice","Avoid frustration from overly difficult content or boredom from content that's too easy"],"best_for":["Self-directed intermediate learners who lack structured curriculum guidance and need automatic scaffolding","Learners with variable proficiency across skills (e.g., strong reading, weak speaking) who need skill-specific adaptation","Learners progressing through multiple proficiency levels who need seamless transitions without manual level switching"],"limitations":["Difficulty assessment relies on dialogue performance alone—cannot account for external factors (fatigue, distraction, topic familiarity) that affect performance","No persistent learner profile across sessions—difficulty adaptation resets with each new conversation, losing historical proficiency signals","Adaptation lag: system may take 5-10 exchanges to detect proficiency shift, causing temporary mismatch between learner ability and content difficulty","Cannot distinguish between knowledge gaps and temporary performance dips, potentially over-adjusting difficulty based on noise"],"requires":["Real-time performance monitoring system tracking error rates, response latency, and vocabulary coverage","Proficiency classification model (rule-based or ML-based) to map performance signals to CEFR levels or equivalent","Dialogue content library with difficulty-tagged prompts and vocabulary lists across multiple proficiency levels","Minimum 3-5 dialogue turns per session to establish reliable proficiency signal"],"input_types":["text (learner responses in target language)","implicit signals (response latency, error patterns, vocabulary usage)"],"output_types":["text (AI tutor prompts adjusted to inferred proficiency level)","metadata (proficiency assessment, difficulty adjustment rationale)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_proseable__cap_4","uri":"capability://memory.knowledge.vocabulary.recognition.and.contextual.definition.lookup","name":"vocabulary-recognition-and-contextual-definition-lookup","description":"Identifies unfamiliar vocabulary in AI tutor responses and learner input, provides on-demand definitions with contextual usage examples, and tracks vocabulary exposure across dialogue sessions. The system integrates vocabulary lookup (dictionary API or embedded lexicon) with dialogue context to provide definitions that match the specific usage in conversation. May track vocabulary frequency and learner exposure to identify high-value vocabulary for focused study.","intents":["Look up word meanings during conversation without breaking dialogue flow to maintain immersion","See how vocabulary is used in context rather than isolated dictionary definitions","Track which vocabulary I've encountered to identify gaps and focus study on high-frequency words"],"best_for":["Intermediate learners building active vocabulary through conversational exposure","Learners seeking to understand vocabulary in authentic dialogue context rather than through isolated word lists","Self-directed learners who want to identify high-value vocabulary for focused study based on exposure frequency"],"limitations":["Dictionary lookups may return multiple definitions without context-specific disambiguation, requiring learner judgment","Vocabulary tracking requires persistent storage across sessions—free tier may have limited storage capacity","Cannot distinguish between passive vocabulary (recognition) and active vocabulary (production) from exposure alone","Vocabulary definitions may be in English or other non-target language, reducing immersion and comprehensible input"],"requires":["Dictionary API or embedded lexicon with target language vocabulary (minimum 10,000 entries for intermediate level)","Persistent storage for vocabulary tracking (user database or local storage)","Context extraction mechanism to identify relevant usage example from dialogue"],"input_types":["text (vocabulary words from dialogue context)"],"output_types":["text (word definition, part of speech, usage example)","metadata (vocabulary frequency, learner exposure count)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_proseable__cap_5","uri":"capability://data.processing.analysis.learner.proficiency.assessment.and.level.placement","name":"learner-proficiency-assessment-and-level-placement","description":"Evaluates learner language proficiency across multiple dimensions (speaking, writing, listening comprehension, grammar, vocabulary) through dialogue interaction and generates proficiency level assessment aligned to CEFR or equivalent framework. The system aggregates performance signals from multiple dialogue exchanges (error rates, vocabulary coverage, grammatical complexity, response latency) to infer overall proficiency and skill-specific strengths/weaknesses. May use rule-based scoring or ML-based proficiency classification.","intents":["Determine my current language proficiency level to know where to focus learning efforts","Identify skill-specific gaps (e.g., strong reading but weak speaking) to prioritize practice areas","Track proficiency improvement over time to measure learning progress and adjust study strategy"],"best_for":["Learners starting with Proseable who need baseline proficiency assessment for appropriate content matching","Self-directed learners without formal language training who lack external proficiency validation","Learners seeking to track progress over weeks/months to maintain motivation and adjust learning strategy"],"limitations":["Proficiency assessment based on dialogue performance alone—cannot assess reading/writing skills in isolation from conversational context","No standardized proficiency certification—assessment is internal to Proseable and may not align with official CEFR or TOEFL/IELTS standards","Assessment accuracy depends on dialogue sample size; limited dialogue (< 10 exchanges) produces unreliable proficiency estimates","Cannot account for topic familiarity effects—learner may perform better on familiar topics, inflating proficiency estimates"],"requires":["Minimum 10-15 dialogue exchanges to generate reliable proficiency assessment","Performance signal aggregation system tracking error rates, vocabulary coverage, grammatical complexity, response latency","Proficiency classification model (rule-based rubric or ML classifier) mapping performance signals to CEFR levels","Persistent learner profile storage to track proficiency changes over time"],"input_types":["text (learner dialogue responses)","implicit signals (error patterns, vocabulary usage, response latency)"],"output_types":["structured assessment (overall proficiency level, skill-specific scores, strengths/weaknesses)","text (proficiency report with recommendations)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_proseable__cap_6","uri":"capability://text.generation.language.multi.language.support.with.language.pair.selection","name":"multi-language-support-with-language-pair-selection","description":"Enables learners to select target language and optionally native language for instruction, supporting multiple language pairs with language-specific NLP pipelines (grammar rules, pronunciation phoneme inventories, vocabulary lists). The system routes learner input to language-specific processors for grammar checking, pronunciation analysis, and vocabulary lookup. Supports both major languages (Spanish, French, German, Mandarin) and potentially less common language pairs depending on available NLP tooling.","intents":["Practice any language I want to learn, not just the most popular languages","Have instruction and feedback in my native language to understand grammatical explanations","Switch between multiple target languages without creating separate accounts"],"best_for":["Polyglots learning multiple languages who want single platform for all language practice","Learners of less common languages (e.g., Portuguese, Polish, Turkish) underserved by mainstream platforms","Learners preferring instruction in native language rather than English-only platforms"],"limitations":["Limited language pair offerings compared to Duolingo (50+ languages) or Babbel (14 languages)—Proseable likely supports 5-10 major languages","NLP quality varies significantly across languages; less-resourced languages (e.g., Swahili, Vietnamese) have lower-quality grammar checking and pronunciation analysis","No cross-language transfer learning—proficiency in one language doesn't inform learning in another language","Instruction language limited to major languages; learners of less common native languages may not have instruction available in their language"],"requires":["Language-specific NLP tooling (dependency parsers, POS taggers, phoneme inventories) for each supported language pair","Multilingual LLM or language-specific LLM instances for dialogue generation in each target language","Vocabulary dictionaries and grammar rule sets for each supported language","Speech-to-text and text-to-speech models for each supported language (if audio features enabled)"],"input_types":["text (language pair selection, learner dialogue in target language)"],"output_types":["text (AI tutor responses in target language, feedback in learner's native language)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_proseable__cap_7","uri":"capability://automation.workflow.free.tier.access.with.unlimited.conversation.practice","name":"free-tier-access-with-unlimited-conversation-practice","description":"Provides free access to core conversational practice features without subscription paywall, removing financial barriers to language learning. The free tier includes unlimited dialogue sessions, real-time feedback, and proficiency assessment without usage limits or time restrictions. Monetization likely relies on optional premium features (advanced analytics, structured curriculum, human tutor integration) rather than restricting core practice access.","intents":["Practice language learning without paying subscription fees, testing commitment before investing money","Access unlimited conversation practice without worrying about usage quotas or session limits","Learn languages affordably in regions with limited disposable income for language learning subscriptions"],"best_for":["Cost-conscious learners in developing regions with limited language learning budgets","Learners testing language learning commitment before investing in paid platforms","Intermediate learners seeking supplementary practice without subscription costs"],"limitations":["Free tier likely has reduced AI model quality or response latency compared to premium tier, affecting feedback quality","No structured curriculum or learning path guidance—free tier users must self-direct learning without scaffolding","Limited analytics or progress tracking compared to premium tier, reducing visibility into learning progress","Potential for ad-supported model or data collection to monetize free tier, raising privacy concerns"],"requires":["Internet connection for API access to LLM backend","Web browser or mobile app","No payment method required for free tier access"],"input_types":["text (learner dialogue in target language)"],"output_types":["text (AI tutor responses, real-time feedback)"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_proseable__cap_8","uri":"capability://memory.knowledge.session.based.conversation.history.and.context.retention","name":"session-based-conversation-history-and-context-retention","description":"Maintains conversation context within individual dialogue sessions, enabling the AI tutor to reference previous exchanges, track topic continuity, and provide coherent multi-turn responses. The system stores conversation history (learner messages, AI responses, feedback) within session scope and uses this history as context for subsequent LLM prompts, enabling natural dialogue flow. Context retention is session-scoped; conversations reset between sessions without persistent cross-session memory.","intents":["Have natural multi-turn conversations where the AI remembers what we discussed earlier in the session","Refer back to earlier parts of conversation without repeating context or starting over","Maintain topic continuity across multiple exchanges without the AI losing track of conversation thread"],"best_for":["Learners seeking natural dialogue experience with context awareness rather than isolated question-answer exchanges","Intermediate learners practicing complex conversational skills that require topic continuity and reference to previous exchanges"],"limitations":["Context retention limited to single session—closing conversation loses all history without persistent storage","No cross-session learner profile—AI tutor has no memory of previous conversations or learner proficiency history","Context window limitations: LLM context length (typically 2K-4K tokens) limits conversation length before context truncation","No conversation export or review capability—learners cannot review past conversations for self-assessment"],"requires":["Session management system to track conversation state and history","LLM with sufficient context window (minimum 2K tokens) to maintain 10-20 dialogue turns","Temporary storage for conversation history within session (in-memory or short-lived cache)"],"input_types":["text (learner messages in target language)"],"output_types":["text (AI tutor responses with context awareness)","metadata (conversation history, session ID)"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":45,"verified":false,"data_access_risk":"high","permissions":["Internet connection for real-time API calls to LLM backend","Web browser or mobile app with text input capability","Basic proficiency in target language (A2+ CEFR level recommended for meaningful dialogue)","Target language must have robust NLP tooling (dependency parsers, POS taggers) available","Learner must have intermediate grammar knowledge to understand rule-based explanations","API access to grammar checking service (LanguageTool, custom rule engine, or LLM-based analysis)","Microphone or audio input device with minimum SNR (signal-to-noise ratio) of 20dB","Speech-to-text API (Google Cloud Speech-to-Text, Azure Speech Services, or Whisper) with target language support","Phoneme inventory and pronunciation rules for target language","Quiet environment for accurate audio capture"],"failure_modes":["No persistent conversation memory across sessions—each dialogue starts fresh without learner history context","AI responses may not catch subtle cultural nuances or regional dialect variations that human tutors would naturally address","Lacks ability to correct deeply ingrained pronunciation patterns through repeated feedback loops over weeks","Cannot simulate real-world social pressure or emotional stakes that motivate language use in authentic contexts","Grammar rule explanations may be overly technical or use metalanguage unfamiliar to beginner learners","Cannot distinguish between intentional stylistic choices and genuine errors (e.g., poetic word order)","Feedback latency may exceed 500ms for complex error analysis, disrupting conversational flow","No tracking of error patterns across sessions—each feedback event is isolated without learner error history","Speech-to-text accuracy degrades significantly for non-native accents, creating circular feedback problems where mispronunciations aren't recognized as errors","Cannot assess suprasegmental features (stress, intonation, rhythm) that significantly impact intelligibility—only phoneme-level accuracy","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":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:32.438Z","last_scraped_at":"2026-04-05T13:23:42.551Z","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=proseable","compare_url":"https://unfragile.ai/compare?artifact=proseable"}},"signature":"YWpm3zieDpnh7OAJiplXun98Mq3neNMwZNY+uoIkpQ8jYuHRXONkrEAip/HLDr/DjbVG549vb1xdgdE9StO/CQ==","signedAt":"2026-06-20T13:36:04.458Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/proseable","artifact":"https://unfragile.ai/proseable","verify":"https://unfragile.ai/api/v1/verify?slug=proseable","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"}}