{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-eugeniughelbur--obsidian-second-brain","slug":"eugeniughelbur--obsidian-second-brain","name":"obsidian-second-brain","type":"skill","url":"https://github.com/eugeniughelbur/obsidian-second-brain/releases/latest","page_url":"https://unfragile.ai/eugeniughelbur--obsidian-second-brain","categories":["app-builders"],"tags":["ai-agent","ai-agents","ai-automation","ai-research","ai-tools","anthropic","claude","claude-ai","claude-code","claude-code-skill","claude-skill","knowledge-management","llm-tools","note-taking","obsidian","obsidian-md","obsidian-plugin","obsidian-skill","personal-knowledge-management","second-brain"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-eugeniughelbur--obsidian-second-brain__cap_0","uri":"capability://memory.knowledge.vault.aware.semantic.search.and.retrieval","name":"vault-aware semantic search and retrieval","description":"Indexes the entire Obsidian vault as a searchable knowledge base, enabling Claude to retrieve relevant notes based on semantic similarity rather than keyword matching. Uses embeddings to understand context and relationships between notes, allowing the agent to surface connected information across the vault without explicit linking. Implements local indexing to avoid sending vault contents to external services.","intents":["Find all notes related to a research topic without manually searching","Retrieve context from my vault to inform Claude's responses","Discover connections between disparate notes in my knowledge base","Build a searchable index of my personal knowledge without cloud dependency"],"best_for":["researchers and academics managing large note collections","knowledge workers building personal wikis","developers using Obsidian as a project documentation hub"],"limitations":["Indexing latency increases with vault size (100k+ notes may require optimization)","Embedding quality depends on Claude's semantic understanding; domain-specific terminology may require custom fine-tuning","No incremental indexing — full re-index required on vault changes unless delta tracking is implemented"],"requires":["Obsidian 1.0+","Claude API key with embeddings support","Minimum 512MB free disk space for index storage","Python 3.9+ for the agent runtime"],"input_types":["markdown notes","plain text queries","structured metadata (tags, frontmatter)"],"output_types":["ranked list of relevant notes","note excerpts with relevance scores","structured metadata about retrieved documents"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-eugeniughelbur--obsidian-second-brain__cap_1","uri":"capability://automation.workflow.scheduled.autonomous.research.agents","name":"scheduled autonomous research agents","description":"Enables creation of background agents that run on a schedule (hourly, daily, weekly) to perform research tasks, synthesize information, and update notes without manual intervention. Agents execute Claude Code skill commands in sequence, reading from the vault, processing information, and writing results back to specified notes. Implements a scheduling system that persists agent configurations and execution history.","intents":["Automatically summarize new research papers and add them to my vault daily","Run weekly synthesis tasks that combine notes on a topic into a summary","Monitor specific topics and update my vault with new findings on a schedule","Create background processes that maintain and evolve my knowledge base"],"best_for":["researchers maintaining living literature reviews","knowledge workers who want passive knowledge accumulation","teams using Obsidian for collaborative research with automated synthesis"],"limitations":["Scheduling requires persistent background process — agent stops if Obsidian is closed","No built-in error recovery or retry logic for failed scheduled tasks","Limited to operations that can complete within Obsidian's execution timeout (typically 30-60 seconds)","No distributed execution — all agents run on a single machine"],"requires":["Obsidian running continuously or via scheduled launcher","Claude API key with sufficient quota for scheduled executions","Python 3.9+ with APScheduler or similar scheduling library","Write permissions to vault directory"],"input_types":["cron expressions or interval specifications","note paths and query parameters","agent configuration in YAML or JSON"],"output_types":["updated notes with synthesized content","execution logs and timestamps","structured agent state and history"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-eugeniughelbur--obsidian-second-brain__cap_10","uri":"capability://tool.use.integration.claude.code.skill.command.execution.and.orchestration","name":"claude code skill command execution and orchestration","description":"Provides a unified interface for executing 31+ Claude Code skill commands that manipulate vault content, including note creation, editing, searching, and analysis. Implements a command registry that maps natural language requests to specific commands, handles parameter binding, and manages execution context. Supports command chaining and conditional execution based on results.","intents":["Execute vault operations through natural language commands","Chain multiple commands together for complex workflows","Automate repetitive vault operations","Build custom workflows by combining built-in commands"],"best_for":["users automating vault operations without coding","teams building custom workflows on top of Obsidian","developers extending Obsidian with AI-driven automation"],"limitations":["Limited to 31 built-in commands; custom commands require code","Command execution is synchronous; no parallel execution","Error handling is basic; failures in command chains may not rollback previous steps","No built-in command versioning or backward compatibility guarantees"],"requires":["Obsidian 1.0+","Claude API key","Python 3.9+","Understanding of available commands and their parameters"],"input_types":["natural language command requests","command parameters and options","command chains in YAML or JSON"],"output_types":["command execution results","modified vault content","execution logs and error messages"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-eugeniughelbur--obsidian-second-brain__cap_11","uri":"capability://data.processing.analysis.vault.content.analysis.and.insights.generation","name":"vault content analysis and insights generation","description":"Analyzes vault content to identify patterns, trends, gaps, and insights. The agent can identify frequently discussed topics, track how concepts evolve across notes, identify knowledge gaps, and generate insights about the vault's content. Supports statistical analysis and visualization data generation for vault structure and content patterns.","intents":["Identify gaps in my knowledge base that should be filled","Discover patterns and trends in my notes","Understand how concepts relate and evolve across notes","Generate insights about my vault's content and structure"],"best_for":["researchers analyzing their research notes","knowledge workers understanding their knowledge base","teams analyzing shared vault content"],"limitations":["Analysis quality depends on vault size and content quality","No built-in statistical significance testing","Pattern detection may identify spurious correlations","Insights are descriptive; no predictive capabilities"],"requires":["Obsidian 1.0+","Claude API key","Python 3.9+","Vault with sufficient content for meaningful analysis (100+ notes recommended)"],"input_types":["analysis scope (entire vault, specific folders, tags)","analysis parameters (time range, topic focus)","insight type specifications"],"output_types":["analysis report with findings","visualization data (graphs, charts)","identified gaps and recommendations"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-eugeniughelbur--obsidian-second-brain__cap_12","uri":"capability://data.processing.analysis.multi.format.note.import.and.normalization","name":"multi-format note import and normalization","description":"Imports notes from external sources (markdown files, web content, PDFs, other note-taking apps) and normalizes them into Obsidian-compatible format with consistent metadata and structure. The agent parses various formats, extracts content and metadata, and generates Obsidian-compatible markdown with appropriate frontmatter, links, and tags. Supports batch import with deduplication.","intents":["Import notes from other note-taking apps into Obsidian","Convert web content into vault notes with proper formatting","Batch import markdown files with metadata extraction","Normalize imported notes to match vault conventions"],"best_for":["users migrating from other note-taking systems","researchers importing research papers and web content","teams consolidating notes from multiple sources"],"limitations":["Import quality depends on source format and metadata availability","No built-in deduplication; duplicate detection requires manual review","Complex formatting may not translate perfectly to markdown","Links and references may break during import if not properly mapped"],"requires":["Obsidian 1.0+","Claude API key","Python 3.9+","Source files or content in supported formats"],"input_types":["markdown files","HTML/web content","PDF files","JSON exports from other note-taking apps","CSV with note data"],"output_types":["normalized markdown notes","import report with statistics","mapping of original IDs to new note paths"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-eugeniughelbur--obsidian-second-brain__cap_13","uri":"capability://text.generation.language.vault.aware.writing.assistance.and.editing","name":"vault-aware writing assistance and editing","description":"Provides writing assistance and editing capabilities that are aware of vault content and style. When editing notes, the agent can suggest improvements, check consistency with vault conventions, identify redundancy with existing notes, and improve clarity while maintaining the user's voice. Supports style checking and tone analysis based on vault examples.","intents":["Get writing suggestions that match my vault's style and tone","Check if my note is consistent with related notes in the vault","Identify redundancy with existing notes","Improve clarity and readability of notes"],"best_for":["writers maintaining consistent voice across notes","teams maintaining consistent documentation style","researchers ensuring clarity in research notes"],"limitations":["Style suggestions are based on vault examples; limited vault may have limited style data","No built-in grammar checking beyond Claude's capabilities","Tone analysis is subjective and may not match user preferences","Suggestions require manual review and approval"],"requires":["Obsidian 1.0+","Claude API key","Python 3.9+","Vault with sufficient content for style analysis"],"input_types":["note content to edit","editing parameters (style, tone, clarity focus)","style guide or conventions"],"output_types":["editing suggestions with explanations","revised note content","style consistency report"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-eugeniughelbur--obsidian-second-brain__cap_2","uri":"capability://automation.workflow.multi.step.vault.transformation.pipelines","name":"multi-step vault transformation pipelines","description":"Chains multiple Claude Code skill commands together to perform complex transformations on vault content, such as bulk note reformatting, metadata extraction, or content reorganization. Implements a pipeline abstraction that passes output from one step as input to the next, with error handling and rollback capabilities. Supports conditional branching based on note properties or content analysis.","intents":["Migrate all notes from one folder structure to another with consistent formatting","Extract structured metadata from unstructured notes and populate frontmatter","Bulk-process notes to add tags, links, or cross-references based on content analysis","Reorganize vault structure based on semantic similarity or topic clustering"],"best_for":["vault maintainers performing large-scale reorganizations","teams standardizing note formats across a shared vault","users migrating from other note-taking systems to Obsidian"],"limitations":["No atomic transactions — partial pipeline failures may leave vault in inconsistent state","Performance degrades with vault size; processing 10k+ notes may require batching","No built-in dry-run mode; users must manually backup vault before running transformations","Limited to transformations that can be expressed as sequential Claude operations"],"requires":["Obsidian 1.0+","Claude API key with sufficient context window for batch operations","Python 3.9+ with pipeline orchestration support","Vault backup for safety"],"input_types":["note selection criteria (folder, tag, regex)","transformation specifications (format, metadata rules)","pipeline configuration in YAML or JSON"],"output_types":["transformed notes with updated content and metadata","transformation report with statistics","rollback instructions if needed"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-eugeniughelbur--obsidian-second-brain__cap_3","uri":"capability://text.generation.language.context.aware.note.generation.and.expansion","name":"context-aware note generation and expansion","description":"Generates new notes or expands existing ones based on vault context, using semantic search to pull relevant information and Claude to synthesize new content. When creating a note, the agent retrieves related notes from the vault, uses them as context, and generates content that integrates with existing knowledge. Supports templates and structured generation for consistent note formats.","intents":["Create a new note on a topic with automatic links to related notes in my vault","Expand a stub note with detailed content informed by related notes","Generate literature review notes that synthesize multiple source notes","Create index or hub notes that aggregate related content"],"best_for":["researchers building interconnected knowledge bases","students creating study materials from lecture notes","writers using Obsidian for long-form content with cross-references"],"limitations":["Generated content quality depends on quality and relevance of existing vault notes","No built-in fact-checking; generated content may contain hallucinations or inaccuracies","Context window limits how many related notes can be included (typically 10-50 notes depending on size)","Generated links may be incorrect if semantic search retrieves irrelevant notes"],"requires":["Obsidian 1.0+","Claude API key with sufficient context window (100k+ tokens recommended)","Python 3.9+","Existing vault with sufficient notes for semantic search to work effectively"],"input_types":["note title or topic","template specification","generation parameters (length, style, tone)"],"output_types":["new markdown note with frontmatter","list of generated links to related notes","metadata about generation (sources used, confidence scores)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-eugeniughelbur--obsidian-second-brain__cap_4","uri":"capability://memory.knowledge.intelligent.note.linking.and.backlink.management","name":"intelligent note linking and backlink management","description":"Analyzes note content to identify semantic relationships and automatically creates or suggests links between notes. Uses Claude to understand note semantics and determine when notes should be linked, then updates Obsidian's link graph. Supports bidirectional link creation and can detect and resolve duplicate or conflicting links.","intents":["Automatically link related notes without manually creating each link","Discover implicit connections between notes that should be explicitly linked","Maintain link consistency across the vault","Identify orphaned notes that should be linked to the knowledge graph"],"best_for":["users with large vaults who want to improve interconnectedness","researchers building knowledge graphs from notes","teams maintaining shared vaults with consistent linking standards"],"limitations":["Semantic linking may create false positives (links between unrelated notes)","No built-in deduplication of similar notes before linking","Link suggestions require manual review for accuracy","Performance scales poorly with vault size (O(n²) comparisons for full linking)"],"requires":["Obsidian 1.0+","Claude API key","Python 3.9+","Vault with at least 50+ notes for meaningful linking"],"input_types":["note selection criteria","linking rules and thresholds","link type specifications (semantic, topical, hierarchical)"],"output_types":["list of suggested links with confidence scores","updated notes with new links","link graph visualization data"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-eugeniughelbur--obsidian-second-brain__cap_5","uri":"capability://code.generation.editing.vault.aware.code.generation.and.documentation","name":"vault-aware code generation and documentation","description":"Generates code snippets, functions, or documentation based on patterns and examples found in the vault. When a user requests code generation, the agent searches the vault for relevant examples, coding patterns, or documentation, then uses those as context to generate code that matches the vault's conventions and style. Supports multiple programming languages and documentation formats.","intents":["Generate code that follows patterns documented in my vault","Create function implementations based on similar functions in my vault","Generate documentation that matches my vault's documentation style","Create boilerplate code that integrates with my existing codebase patterns"],"best_for":["developers using Obsidian to document code patterns and conventions","teams maintaining shared coding standards in a vault","developers building consistent codebases with documented patterns"],"limitations":["Generated code quality depends on quality of examples in vault","No compilation or execution validation of generated code","Limited to code patterns that are documented in the vault","May generate code that doesn't match actual codebase if documentation is outdated"],"requires":["Obsidian 1.0+","Claude API key","Python 3.9+","Vault with documented code examples and patterns"],"input_types":["code generation request","function signature or specification","programming language and style parameters"],"output_types":["generated code snippet","generated documentation","list of example sources used"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-eugeniughelbur--obsidian-second-brain__cap_6","uri":"capability://text.generation.language.research.synthesis.and.literature.review.automation","name":"research synthesis and literature review automation","description":"Automatically synthesizes information from multiple notes into coherent research summaries, literature reviews, or analysis documents. The agent retrieves relevant notes based on a research query, analyzes them for key findings and themes, and generates a synthesized document that integrates insights across sources. Supports citation tracking and source attribution.","intents":["Generate a literature review from notes on a research topic","Synthesize findings from multiple research notes into a summary","Create analysis documents that integrate insights from multiple sources","Track and attribute sources in synthesized content"],"best_for":["academic researchers managing literature reviews","analysts synthesizing information from multiple sources","students creating research papers from notes"],"limitations":["Synthesis quality depends on quality and completeness of source notes","No built-in fact-checking or conflict resolution between sources","Generated synthesis may miss nuances or important caveats from sources","Citation tracking requires consistent metadata in source notes"],"requires":["Obsidian 1.0+","Claude API key with large context window (100k+ tokens)","Python 3.9+","Vault with research notes containing consistent metadata"],"input_types":["research topic or query","source note selection criteria","synthesis parameters (length, depth, focus areas)"],"output_types":["synthesized research document","source attribution and citations","key findings and themes extracted"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-eugeniughelbur--obsidian-second-brain__cap_7","uri":"capability://data.processing.analysis.vault.metadata.extraction.and.structuring","name":"vault metadata extraction and structuring","description":"Analyzes unstructured note content to extract structured metadata (entities, relationships, properties) and populates note frontmatter with extracted data. Uses Claude to understand note content and identify relevant metadata, then updates YAML frontmatter with extracted information. Supports custom metadata schemas and validation rules.","intents":["Extract entities (people, places, concepts) from notes and add them as metadata","Populate frontmatter with structured data extracted from note content","Create consistent metadata across notes for better organization","Enable structured querying of notes based on extracted metadata"],"best_for":["vault maintainers standardizing metadata across notes","researchers building structured knowledge bases","teams using Obsidian with dataview or other metadata-dependent plugins"],"limitations":["Extraction accuracy depends on note clarity and Claude's understanding","No built-in validation of extracted metadata against schemas","May extract incorrect or irrelevant metadata if note content is ambiguous","Requires manual review of extracted metadata for accuracy"],"requires":["Obsidian 1.0+","Claude API key","Python 3.9+","Dataview or similar plugin for metadata querying (optional but recommended)"],"input_types":["note selection criteria","metadata schema specification","extraction rules and patterns"],"output_types":["updated notes with populated frontmatter","extraction report with confidence scores","validation report for extracted metadata"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-eugeniughelbur--obsidian-second-brain__cap_8","uri":"capability://text.generation.language.query.driven.note.generation.and.expansion","name":"query-driven note generation and expansion","description":"Generates or expands notes in response to natural language queries, using vault context to ensure generated content is relevant and integrated. When a user asks a question, the agent searches the vault for relevant information, identifies gaps, and generates new content or expands existing notes to answer the question comprehensively. Supports follow-up queries and iterative refinement.","intents":["Ask a question and have the agent generate a note answering it with vault context","Expand notes to answer follow-up questions or provide additional detail","Generate missing content identified by gaps in vault coverage","Create comprehensive answers that integrate information from multiple notes"],"best_for":["researchers exploring topics and building knowledge incrementally","students using Obsidian for learning and note-taking","knowledge workers building comprehensive documentation"],"limitations":["Generated content may hallucinate information not in vault","No built-in fact-checking or validation of generated answers","Quality depends on existing vault coverage of the topic","May generate redundant content if similar notes already exist"],"requires":["Obsidian 1.0+","Claude API key with large context window","Python 3.9+","Vault with sufficient coverage of topics for meaningful context"],"input_types":["natural language query","note creation or expansion parameters","context scope (single note, multiple notes, entire vault)"],"output_types":["generated or expanded note","list of sources used","suggested follow-up questions"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-eugeniughelbur--obsidian-second-brain__cap_9","uri":"capability://automation.workflow.vault.aware.task.and.project.management","name":"vault-aware task and project management","description":"Integrates task and project management capabilities with vault content, enabling creation of tasks from notes, tracking project progress, and generating project summaries. The agent can create task notes with metadata, track dependencies between tasks, and generate project status reports by analyzing task notes. Supports integration with Obsidian's task syntax and metadata.","intents":["Create tasks from notes and track their progress","Generate project status reports from task notes","Identify task dependencies and critical path","Create project plans from research or planning notes"],"best_for":["researchers managing research projects in Obsidian","teams using Obsidian for project documentation","individuals managing personal projects and tasks"],"limitations":["No real-time synchronization with external project management tools","Task tracking requires consistent metadata and formatting","No built-in resource allocation or capacity planning","Limited to task management within Obsidian; no external integrations"],"requires":["Obsidian 1.0+","Claude API key","Python 3.9+","Obsidian task plugin or native task syntax support"],"input_types":["project specification or planning notes","task creation parameters","project scope and timeline"],"output_types":["task notes with metadata","project status reports","dependency graphs and critical path analysis"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":36,"verified":false,"data_access_risk":"high","permissions":["Obsidian 1.0+","Claude API key with embeddings support","Minimum 512MB free disk space for index storage","Python 3.9+ for the agent runtime","Obsidian running continuously or via scheduled launcher","Claude API key with sufficient quota for scheduled executions","Python 3.9+ with APScheduler or similar scheduling library","Write permissions to vault directory","Claude API key","Python 3.9+"],"failure_modes":["Indexing latency increases with vault size (100k+ notes may require optimization)","Embedding quality depends on Claude's semantic understanding; domain-specific terminology may require custom fine-tuning","No incremental indexing — full re-index required on vault changes unless delta tracking is implemented","Scheduling requires persistent background process — agent stops if Obsidian is closed","No built-in error recovery or retry logic for failed scheduled tasks","Limited to operations that can complete within Obsidian's execution timeout (typically 30-60 seconds)","No distributed execution — all agents run on a single machine","Limited to 31 built-in commands; custom commands require code","Command execution is synchronous; no parallel execution","Error handling is basic; failures in command chains may not rollback previous steps","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.1966930135889647,"quality":0.5,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.15,"quality":0.25,"ecosystem":0.1,"match_graph":0.45,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:59:57.743Z","last_commit":"2026-05-02T19:41:02Z"},"community":{"stars":492,"forks":54,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=eugeniughelbur--obsidian-second-brain","compare_url":"https://unfragile.ai/compare?artifact=eugeniughelbur--obsidian-second-brain"}},"signature":"ajR3j2MGIPTCrlTxgZJX1o/5H9CN24dNxhGPtBodVxw8/0PsPHS1je3zqxVX0CWE/iyNBY7N98G1X162OzKnAg==","signedAt":"2026-06-23T05:30:57.211Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/eugeniughelbur--obsidian-second-brain","artifact":"https://unfragile.ai/eugeniughelbur--obsidian-second-brain","verify":"https://unfragile.ai/api/v1/verify?slug=eugeniughelbur--obsidian-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"}}