{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-smixs--agent-second-brain","slug":"smixs--agent-second-brain","name":"agent-second-brain","type":"agent","url":"https://t.me/aimastersme","page_url":"https://unfragile.ai/smixs--agent-second-brain","categories":["ai-agents"],"tags":["ai-agents","ai-automation","claude","ebbinghaus","knowledge-graph","llm","memory-system","obsidian","openai","openclaw","personal-assistant","productivity","python","rag","second-brain","telegram-bot","todoist","vault-health"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-smixs--agent-second-brain__cap_0","uri":"capability://data.processing.analysis.voice.note.to.structured.knowledge.ingestion","name":"voice-note-to-structured-knowledge ingestion","description":"Accepts voice notes via Telegram, transcribes them using OpenAI's Whisper API, then parses the transcription through Claude to extract entities, relationships, and semantic meaning. The system converts unstructured audio into structured knowledge graph nodes with metadata (source, timestamp, confidence scores). Integration with Telegram Bot API enables real-time voice message capture and processing through OpenClaw orchestration layer.","intents":["I want to capture fleeting thoughts via voice without typing","I need my voice notes automatically organized into a knowledge base","I want to avoid manual transcription and categorization overhead"],"best_for":["knowledge workers capturing ideas on-the-go","researchers building literature notes from voice","non-technical users who prefer speaking to typing"],"limitations":["Whisper transcription accuracy varies with audio quality and background noise","Real-time processing adds 3-8 second latency per voice note","Language support limited to Whisper's supported languages (no rare language support)","Voice notes >25MB may timeout or require chunking"],"requires":["Telegram account with bot token","OpenAI API key for Whisper transcription","Claude API key for semantic parsing","Python 3.9+"],"input_types":["audio (voice notes via Telegram)","metadata (timestamp, sender)"],"output_types":["structured JSON (entities, relationships, tags)","knowledge graph nodes","markdown notes"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-smixs--agent-second-brain__cap_1","uri":"capability://memory.knowledge.ebbinghaus.spaced.repetition.memory.decay.scoring","name":"ebbinghaus-spaced-repetition memory decay scoring","description":"Implements the Ebbinghaus forgetting curve algorithm to score knowledge items based on review frequency and time intervals. Each note tracks review history, calculates decay probability using exponential decay functions, and assigns a freshness score (0-100). The system prioritizes items approaching the forgetting threshold for review, enabling evidence-based spaced repetition without manual scheduling. Decay calculations run on-demand during vault health scoring cycles.","intents":["I want to review knowledge at optimal intervals to maximize retention","I need to identify which notes I'm forgetting and should review soon","I want a data-driven approach to learning, not arbitrary review schedules"],"best_for":["students and researchers optimizing long-term retention","professionals maintaining technical knowledge across domains","learners building durable mental models"],"limitations":["Ebbinghaus model assumes uniform learning conditions; doesn't account for difficulty variance","Requires consistent review history to produce accurate decay scores","Cold-start problem: new notes have no review history for 7+ days","Doesn't adapt decay curves per user (uses population-average parameters)"],"requires":["Review history tracking enabled","Timestamp metadata on all notes","Python 3.9+ for decay calculations"],"input_types":["note content","review timestamps (array)","review count"],"output_types":["decay score (0-100 float)","next review date (ISO 8601)","priority ranking"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-smixs--agent-second-brain__cap_10","uri":"capability://tool.use.integration.obsidian.vault.export.and.sync.compatibility","name":"obsidian vault export and sync compatibility","description":"Exports knowledge base to Obsidian-compatible markdown format with frontmatter metadata (tags, relationships, decay scores, review dates). Maintains bidirectional compatibility: notes created in agent-second-brain can be edited in Obsidian, and changes sync back. Uses standard markdown + YAML frontmatter, enabling interoperability with other tools. Supports Obsidian plugins like graph view, backlinks, and dataview.","intents":["I want to use Obsidian for deep editing while keeping agent-second-brain for capture","I need my knowledge base to work with existing Obsidian workflows","I want to leverage Obsidian plugins for visualization and analysis"],"best_for":["Obsidian power users wanting to integrate voice capture","teams using Obsidian as their knowledge base","individuals wanting flexibility in editing tools"],"limitations":["Sync is one-way (agent → Obsidian) or requires manual reconciliation","Obsidian-specific features (plugins, themes) don't sync back to agent","Frontmatter schema must be carefully designed to avoid conflicts","Large vaults (1000+ notes) may have slow sync performance"],"requires":["Obsidian vault (local or cloud-synced)","Python 3.9+","Markdown + YAML knowledge"],"input_types":["knowledge base (internal format)","Obsidian vault (markdown files)"],"output_types":["markdown files with YAML frontmatter","obsidian.json config (optional)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-smixs--agent-second-brain__cap_2","uri":"capability://memory.knowledge.knowledge.graph.construction.and.relationship.inference","name":"knowledge-graph construction and relationship inference","description":"Builds a directed graph of knowledge items by extracting entity mentions and relationships from notes using Claude's semantic understanding. Nodes represent concepts/entities; edges represent relationships (e.g., 'mentions', 'contradicts', 'builds-on'). The system infers implicit relationships by analyzing note content and cross-referencing existing nodes, enabling discovery of unexpected connections. Graph is stored as adjacency lists with edge metadata (relationship type, confidence, source note).","intents":["I want to see how my ideas connect and build on each other","I need to discover gaps or contradictions in my knowledge","I want to find related notes without manual tagging"],"best_for":["researchers synthesizing literature across domains","systems thinkers mapping complex domains","teams building shared knowledge bases"],"limitations":["Relationship inference is probabilistic; false positives increase with vault size","Graph visualization requires external tools (Obsidian, Cytoscape); not built-in","Relationship types are limited to predefined schema; custom relationships require code changes","Inference latency grows O(n²) with vault size for full graph updates"],"requires":["Claude API key for relationship extraction","Minimum 10-20 notes for meaningful graph structure","Python 3.9+"],"input_types":["note content (markdown or plain text)","entity list (extracted or manual)"],"output_types":["graph JSON (nodes + edges)","adjacency list","relationship matrix"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-smixs--agent-second-brain__cap_3","uri":"capability://data.processing.analysis.vault.health.scoring.and.diagnostic.reporting","name":"vault-health scoring and diagnostic reporting","description":"Calculates a composite health score (0-100) for the knowledge vault by analyzing multiple dimensions: note coverage (breadth of topics), depth (detail per topic), decay distribution (how many notes are at risk of being forgotten), graph connectivity (orphaned vs well-connected nodes), and consistency (contradictions or duplicate knowledge). Runs periodic scans and generates diagnostic reports highlighting weak areas. Score is weighted and configurable per user priorities.","intents":["I want to know if my knowledge base is balanced or has gaps","I need a dashboard showing which areas need more attention","I want to identify redundant or contradictory notes"],"best_for":["knowledge managers maintaining large vaults (100+ notes)","researchers ensuring comprehensive coverage of topics","teams auditing shared knowledge bases"],"limitations":["Health score is heuristic-based; doesn't measure actual learning outcomes","Weighting system is opinionated; may not match user's actual priorities","Requires sufficient vault size (50+ notes) for meaningful diagnostics","Scanning large vaults (1000+ notes) may take 5-10 minutes"],"requires":["Vault with 20+ notes minimum","Claude API key for consistency analysis","Python 3.9+"],"input_types":["note collection (all vault notes)","knowledge graph","review history"],"output_types":["health score (0-100 integer)","diagnostic report (markdown)","dimension breakdown (JSON)","recommendations (text)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-smixs--agent-second-brain__cap_4","uri":"capability://text.generation.language.daily.digest.report.generation.with.prioritized.summaries","name":"daily-digest report generation with prioritized summaries","description":"Generates a daily report summarizing vault activity, highlighting notes due for review (based on decay scores), new connections discovered in the knowledge graph, and vault health changes. Uses Claude to create natural-language summaries of key insights rather than raw data dumps. Reports are formatted as markdown and delivered via Telegram, with optional export to email or Obsidian. Scheduling uses cron-like patterns (configurable daily time).","intents":["I want a daily summary of what I should review and why","I need to stay aware of my vault's health without manual checking","I want to discover new insights from my notes automatically"],"best_for":["busy professionals wanting minimal-friction knowledge review","students tracking learning progress daily","researchers monitoring evolving knowledge bases"],"limitations":["Report generation adds 2-5 second latency; not real-time","Summaries are generic; don't account for user's specific goals","Telegram formatting limits rich formatting (no tables, limited styling)","Scheduling is server-side; requires persistent background process"],"requires":["Telegram bot token","Claude API key","Python 3.9+","Background task scheduler (APScheduler or similar)"],"input_types":["vault notes (all)","decay scores","graph updates","health metrics"],"output_types":["markdown report","telegram message","email (optional)","JSON metadata"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-smixs--agent-second-brain__cap_5","uri":"capability://tool.use.integration.todoist.task.creation.from.voice.notes.with.context.injection","name":"todoist task creation from voice notes with context injection","description":"Automatically creates tasks in Todoist from voice notes, extracting action items using Claude's semantic understanding. Each task includes context from the original note, related notes from the knowledge graph, and decay-based priority (high priority for notes approaching forgetting threshold). Tasks are tagged with source note ID and vault health indicators. Integration uses Todoist API with OAuth authentication. Bidirectional sync allows task completion to update note review history.","intents":["I want action items from my voice notes automatically added to my task list","I need tasks prioritized based on knowledge decay, not arbitrary urgency","I want context from my knowledge base available in my task manager"],"best_for":["GTD practitioners integrating knowledge capture with task management","teams coordinating on shared knowledge and action items","individuals avoiding context-switching between apps"],"limitations":["Action item extraction is probabilistic; may miss implicit tasks or create spurious tasks","Todoist API rate limits (450 requests/15 min); batching required for large vaults","Context injection increases task description length; may clutter Todoist UI","Bidirectional sync requires polling or webhooks; adds complexity"],"requires":["Todoist account with API token","Claude API key","Python 3.9+","OAuth setup for Todoist integration"],"input_types":["voice note transcription","note metadata","related notes (from graph)"],"output_types":["Todoist task (JSON)","task metadata (priority, tags, due date)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-smixs--agent-second-brain__cap_6","uri":"capability://memory.knowledge.persistent.memory.state.management.with.decay.tracking","name":"persistent-memory state management with decay tracking","description":"Maintains persistent state across sessions by storing note metadata, review history, decay scores, and graph structure in a local database (likely SQLite or JSON files). Each note record includes creation timestamp, review timestamps (array), decay score, last updated, and relationships. State is loaded on startup and persisted after each operation. Handles concurrent access via file locking or transaction management. Enables recovery from crashes and audit trails of knowledge evolution.","intents":["I want my knowledge base to persist across sessions without manual saving","I need to track when I reviewed each note for spaced repetition","I want an audit trail of how my knowledge has evolved"],"best_for":["individual knowledge workers building long-term vaults","researchers needing reproducible knowledge snapshots","teams auditing knowledge base changes"],"limitations":["Local storage limits scalability; vaults >10k notes may have performance issues","No built-in cloud sync; requires manual backup or external sync tools","Concurrent access from multiple devices requires custom sync logic","State corruption can occur if process crashes during write"],"requires":["Local file system with write permissions","Python 3.9+","SQLite or JSON file storage"],"input_types":["note objects","metadata updates","review events"],"output_types":["persisted state (database/JSON)","state snapshots","audit logs"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-smixs--agent-second-brain__cap_7","uri":"capability://tool.use.integration.openclaw.orchestration.for.multi.step.agent.workflows","name":"openclaw orchestration for multi-step agent workflows","description":"Uses OpenClaw (a framework for composing LLM agent workflows) to orchestrate multi-step processes: voice transcription → semantic parsing → entity extraction → graph updates → task creation → report generation. Each step is a declarative node in a DAG, with Claude as the reasoning engine. OpenClaw handles state passing between steps, error handling, and retry logic. Enables complex workflows without custom orchestration code.","intents":["I want to build complex multi-step workflows without writing orchestration code","I need reliable error handling and retry logic across LLM calls","I want to compose reusable workflow components"],"best_for":["developers building LLM agents with complex logic","teams needing declarative workflow composition","projects requiring robust error handling in agent pipelines"],"limitations":["OpenClaw is relatively new; documentation and community support are limited","DAG execution adds overhead; not suitable for latency-sensitive applications","Debugging multi-step workflows is harder than linear code","State passing between steps requires careful schema design"],"requires":["OpenClaw library (Python)","Claude API key","Python 3.9+","Understanding of DAG-based workflow composition"],"input_types":["workflow definition (DAG)","input data","step parameters"],"output_types":["workflow result","execution trace","error logs"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-smixs--agent-second-brain__cap_8","uri":"capability://data.processing.analysis.claude.powered.semantic.note.parsing.and.entity.extraction","name":"claude-powered semantic note parsing and entity extraction","description":"Uses Claude's language understanding to parse unstructured note content and extract entities (people, concepts, dates, locations), relationships, and semantic tags. Claude performs zero-shot extraction without training data, understanding context and domain-specific terminology. Extraction results include confidence scores and explanations. Supports custom extraction schemas via prompt engineering. Results feed into knowledge graph construction and task creation.","intents":["I want to automatically extract structured data from unstructured notes","I need semantic understanding of note content, not just keyword matching","I want custom extraction rules without writing code"],"best_for":["researchers processing large volumes of notes","teams building domain-specific knowledge bases","individuals wanting semantic organization without manual tagging"],"limitations":["Claude API calls add 1-3 second latency per note","Extraction quality varies with note clarity and domain specificity","Hallucination risk: Claude may invent entities not in the note","Cost scales with vault size (each note requires an API call)"],"requires":["Claude API key","Python 3.9+","Minimum 100 tokens per note for accurate extraction"],"input_types":["note text (markdown or plain text)","extraction schema (optional)"],"output_types":["entity list (JSON)","relationship list (JSON)","semantic tags (array)","confidence scores (0-1 floats)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-smixs--agent-second-brain__cap_9","uri":"capability://tool.use.integration.telegram.bot.interface.with.voice.message.handling","name":"telegram bot interface with voice message handling","description":"Implements a Telegram bot using the Telegram Bot API that listens for incoming voice messages, text messages, and commands. Voice messages are forwarded to Whisper for transcription; text is processed directly. Bot maintains conversation state per user (e.g., awaiting confirmation, in edit mode). Supports commands like /review, /health, /export. Uses polling or webhooks for message delivery. Handles rate limiting and error recovery gracefully.","intents":["I want to interact with my knowledge system via Telegram, my primary messaging app","I need a simple interface for voice input without opening another app","I want quick commands to check vault health or export notes"],"best_for":["mobile-first users who live in Telegram","teams using Telegram for communication","individuals wanting minimal UI complexity"],"limitations":["Telegram Bot API has rate limits (30 messages/second per user)","Voice message quality depends on Telegram's compression","No rich UI; limited to text and inline buttons","Polling-based bots add latency; webhooks require public URL"],"requires":["Telegram account","Bot token from BotFather","Python 3.9+","python-telegram-bot library or similar"],"input_types":["voice messages (OGG Opus format)","text messages","commands (/review, /health, etc.)"],"output_types":["text responses","markdown-formatted messages","inline buttons","file exports"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Telegram account with bot token","OpenAI API key for Whisper transcription","Claude API key for semantic parsing","Python 3.9+","Review history tracking enabled","Timestamp metadata on all notes","Python 3.9+ for decay calculations","Obsidian vault (local or cloud-synced)","Markdown + YAML knowledge","Claude API key for relationship extraction"],"failure_modes":["Whisper transcription accuracy varies with audio quality and background noise","Real-time processing adds 3-8 second latency per voice note","Language support limited to Whisper's supported languages (no rare language support)","Voice notes >25MB may timeout or require chunking","Ebbinghaus model assumes uniform learning conditions; doesn't account for difficulty variance","Requires consistent review history to produce accurate decay scores","Cold-start problem: new notes have no review history for 7+ days","Doesn't adapt decay curves per user (uses population-average parameters)","Sync is one-way (agent → Obsidian) or requires manual reconciliation","Obsidian-specific features (plugins, themes) don't sync back to agent","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.34236777128882884,"quality":0.47,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.6,"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":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T13:59:57.743Z","last_commit":"2026-03-03T08:56:09Z"},"community":{"stars":246,"forks":176,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=smixs--agent-second-brain","compare_url":"https://unfragile.ai/compare?artifact=smixs--agent-second-brain"}},"signature":"50mpx8JLSWOC/QH3eL6R5//8hHEemNn/kB67a7F2vGA7sDWuqtZf/5R6DLTyHXYDgoaSwLUJBozUoI1Btb4jCA==","signedAt":"2026-06-20T23:51:10.222Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/smixs--agent-second-brain","artifact":"https://unfragile.ai/smixs--agent-second-brain","verify":"https://unfragile.ai/api/v1/verify?slug=smixs--agent-second-brain","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"}}