{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hn-46962641","slug":"rowboat-ai-coworker-that-turns-your-work-into-a-kn","name":"Rowboat – AI coworker that turns your work into a knowledge graph","type":"repo","url":"https://github.com/rowboatlabs/rowboat","page_url":"https://unfragile.ai/rowboat-ai-coworker-that-turns-your-work-into-a-kn","categories":["rag-knowledge"],"tags":["hackernews","show-hn"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hn-46962641__cap_0","uri":"capability://data.processing.analysis.work.activity.to.knowledge.graph.extraction","name":"work-activity-to-knowledge-graph extraction","description":"Automatically captures work activities (emails, messages, documents, code commits) and transforms them into a structured knowledge graph representation using LLM-based entity and relationship extraction. The system parses unstructured work data, identifies key entities (people, projects, tasks, decisions), and maps relationships between them, building a queryable graph structure that persists across sessions and grows with continuous work activity.","intents":["I want to automatically capture what I'm working on without manual note-taking","I need to understand how different projects, people, and decisions are connected across my work","I want to build institutional memory that survives context switches and team changes","I need to query my work history to find related past decisions or similar problems"],"best_for":["knowledge workers managing multiple concurrent projects","teams building internal knowledge bases without manual curation","developers building AI agents that need semantic understanding of work context"],"limitations":["LLM-based extraction introduces hallucination risk — entities may be incorrectly inferred from ambiguous text","Graph quality depends on input data quality and LLM capability — noisy or sparse work logs produce incomplete graphs","No built-in deduplication strategy — same entity mentioned differently may create duplicate nodes","Extraction latency scales with work volume — processing large backlogs of activities may be slow"],"requires":["LLM API access (OpenAI, Anthropic, or local model)","Work data source integration (email, Slack, GitHub, calendar APIs)","Graph database or in-memory graph structure (likely Neo4j or similar)","Python 3.8+ or Node.js 16+"],"input_types":["email messages","chat/messaging platform exports","code commits and pull requests","calendar events","document text","task/issue tracker data"],"output_types":["knowledge graph (nodes and edges)","structured entity records (JSON)","relationship maps","graph query results"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46962641__cap_1","uri":"capability://search.retrieval.contextual.work.history.retrieval.and.search","name":"contextual work-history retrieval and search","description":"Enables semantic search and retrieval over the constructed knowledge graph to surface relevant past work, decisions, and context based on natural language queries or current task context. Uses graph traversal and embedding-based similarity to find related entities, past decisions, and similar problems solved previously, returning ranked results with relationship paths that explain why results are relevant.","intents":["I'm working on a similar problem — show me what we decided last time","Who was involved in this project and what were the key decisions?","Find all related work items, discussions, and decisions connected to this task","I need context on why we chose this approach — show me the decision history"],"best_for":["teams with high context-switching overhead","organizations with distributed decision-making across projects","developers onboarding to complex codebases needing historical context"],"limitations":["Search quality depends on graph completeness — missing or poorly extracted entities reduce recall","Relationship ranking may surface spurious connections in densely connected graphs","No temporal reasoning — cannot distinguish 'current decision' from 'outdated decision' without explicit timestamps","Requires sufficient work history to be useful — sparse graphs return limited results"],"requires":["Populated knowledge graph from work-activity extraction","Embedding model for semantic similarity (local or API-based)","Graph query engine (Cypher, SPARQL, or custom traversal)","LLM for query interpretation and result ranking"],"input_types":["natural language queries","current task/document context","entity names or IDs"],"output_types":["ranked entity results","relationship paths explaining connections","source references (original emails, commits, etc.)","summary of relevant decisions or context"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46962641__cap_2","uri":"capability://text.generation.language.automatic.work.context.summarization.for.task.switching","name":"automatic work-context summarization for task switching","description":"Generates concise summaries of relevant work context when switching between tasks or projects, using the knowledge graph to identify key entities, recent decisions, and involved stakeholders. The system traverses the graph to find all connected work items, extracts key facts and decisions, and synthesizes them into a brief summary that restores context without requiring manual review of past communications.","intents":["I'm switching back to a project I haven't touched in weeks — give me a quick context refresh","I need to brief someone new on this project — what are the key facts and decisions?","What's changed since I last worked on this task?","Summarize the current state of this project for a status update"],"best_for":["developers with fragmented attention across many projects","team leads needing to onboard new members quickly","remote teams where context is not shared through proximity"],"limitations":["Summaries may omit important nuance or context that requires reading original sources","LLM-based summarization can introduce factual errors or misrepresentations","No distinction between 'important' and 'incidental' facts — may over-summarize minor details","Temporal awareness is limited — cannot distinguish recent changes from historical context without explicit timestamps"],"requires":["Populated knowledge graph with temporal metadata","LLM for summary generation","Graph query engine to identify relevant subgraph"],"input_types":["task/project identifier","optional: time range or 'since last visit'"],"output_types":["text summary","key facts list","decision timeline","stakeholder list"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46962641__cap_3","uri":"capability://tool.use.integration.multi.source.work.data.integration.and.normalization","name":"multi-source work-data integration and normalization","description":"Integrates work data from multiple sources (email, Slack, GitHub, Jira, calendar, etc.) into a unified representation for knowledge graph construction. The system normalizes data from different schemas and formats, deduplicates entities across sources (e.g., recognizing the same person in email and Slack), and maps cross-source relationships (e.g., linking a GitHub commit to a Slack discussion).","intents":["I want to see all work related to this project across email, Slack, and GitHub in one place","Connect a GitHub commit to the Slack discussion that led to it","Deduplicate the same person across different tools (john@company.com vs @john in Slack)","Build a unified timeline of work activity across all my tools"],"best_for":["teams using multiple communication and project management tools","organizations needing unified work intelligence across fragmented tooling","developers building multi-source AI agents"],"limitations":["Each source integration requires separate API client and schema mapping — adding new sources requires development","Cross-source deduplication is heuristic-based (name matching, email domain, etc.) and prone to false positives/negatives","API rate limits and authentication complexity increase with number of sources","Data freshness varies by source — some integrations may be stale while others are real-time"],"requires":["API credentials for each integrated source","Source-specific adapters/connectors","Entity matching/deduplication logic","Unified schema for normalized work data"],"input_types":["email messages (IMAP, Gmail API)","Slack messages and threads (Slack API)","GitHub commits, PRs, issues (GitHub API)","Jira tickets and comments (Jira API)","Calendar events (Google Calendar, Outlook API)","Other structured work data (CSV, JSON)"],"output_types":["normalized work events","deduplicated entities","cross-source relationship mappings","unified activity timeline"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46962641__cap_4","uri":"capability://planning.reasoning.ai.assisted.task.decomposition.and.planning.from.work.context","name":"ai-assisted task decomposition and planning from work context","description":"Uses the knowledge graph and work history to suggest task decomposition, identify dependencies, and propose next steps based on similar past work and current project state. The system analyzes the graph to find related tasks, past decisions that constrain current work, and stakeholders who should be involved, then uses an LLM to synthesize a plan with estimated effort and risk factors.","intents":["I have a vague task — help me break it down into concrete steps based on similar past work","What dependencies or constraints should I be aware of for this task?","Who should I involve in this work based on past involvement in related projects?","Estimate effort and identify risks based on similar past work"],"best_for":["teams with repeating work patterns that can be learned from history","project managers planning work based on historical data","developers tackling unfamiliar tasks in familiar domains"],"limitations":["Plan quality depends on graph completeness and relevance of past work — sparse or dissimilar history produces poor suggestions","LLM-based planning can miss domain-specific constraints or risks not captured in the graph","No execution tracking — cannot validate whether suggested plans actually work","Effort estimates are heuristic-based and may not account for team skill variation"],"requires":["Populated knowledge graph with historical task data","LLM for plan synthesis","Graph similarity/matching to find related past work"],"input_types":["task description or title","project context","optional: constraints or requirements"],"output_types":["task decomposition (subtasks)","dependency graph","suggested stakeholders","effort estimates","risk assessment"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46962641__cap_5","uri":"capability://planning.reasoning.work.activity.monitoring.and.anomaly.detection","name":"work-activity monitoring and anomaly detection","description":"Continuously monitors incoming work data and detects anomalies or significant changes in work patterns using the knowledge graph as a baseline. The system identifies unusual activity (e.g., new stakeholders appearing in a project, sudden change in communication patterns, decisions that contradict past precedent) and alerts relevant parties, helping catch miscommunication or missed context early.","intents":["Alert me if a project's scope or stakeholders change unexpectedly","Detect if a decision contradicts past precedent or best practices","Identify when communication patterns suggest a problem (e.g., key stakeholder went silent)","Flag when new people join a project who should be onboarded to existing context"],"best_for":["teams managing complex projects with many stakeholders","organizations needing early warning of miscommunication or scope creep","distributed teams where context loss is a common problem"],"limitations":["Anomaly detection is statistical and prone to false positives in noisy work environments","Requires sufficient historical baseline to detect meaningful deviations — new projects have no baseline","Cannot distinguish 'intentional change' from 'anomaly' without explicit context","Alert fatigue risk if sensitivity is not carefully tuned"],"requires":["Populated knowledge graph with historical patterns","Continuous work data stream (real-time API access)","Statistical anomaly detection model","Alert/notification system"],"input_types":["continuous work activity stream","historical baseline from knowledge graph"],"output_types":["anomaly alerts","change summaries","suggested actions or escalations"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46962641__cap_6","uri":"capability://image.visual.knowledge.graph.visualization.and.exploration","name":"knowledge-graph visualization and exploration","description":"Provides interactive visualization of the work knowledge graph, allowing users to explore entities, relationships, and work patterns visually. The system renders the graph with customizable filtering (by project, person, time range, entity type) and supports multiple visualization modes (network graph, timeline, hierarchical tree) to help users understand work structure and find connections they might miss in text-based search.","intents":["I want to see how all my projects and people are connected visually","Show me the timeline of decisions and events for this project","Visualize the team structure and communication patterns for this work","Explore the graph to discover unexpected connections between work items"],"best_for":["visual learners who understand relationships better through graphs","team leads analyzing organizational structure and communication patterns","researchers studying work patterns and knowledge flow"],"limitations":["Large graphs become visually cluttered and hard to navigate — requires aggressive filtering","Graph layout algorithms (force-directed, hierarchical) may not reflect semantic importance","Interactive exploration requires client-side rendering which can be slow for large graphs","No built-in export to other visualization tools — locked into the visualization UI"],"requires":["Populated knowledge graph","Graph visualization library (D3.js, Cytoscape, or similar)","Web UI framework (React, Vue, etc.)","Backend API to serve graph data and filtering"],"input_types":["knowledge graph (nodes and edges)","filter parameters (project, person, time range, entity type)"],"output_types":["interactive graph visualization","filtered subgraph views","timeline visualizations","hierarchical tree views"],"categories":["image-visual","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["LLM API access (OpenAI, Anthropic, or local model)","Work data source integration (email, Slack, GitHub, calendar APIs)","Graph database or in-memory graph structure (likely Neo4j or similar)","Python 3.8+ or Node.js 16+","Populated knowledge graph from work-activity extraction","Embedding model for semantic similarity (local or API-based)","Graph query engine (Cypher, SPARQL, or custom traversal)","LLM for query interpretation and result ranking","Populated knowledge graph with temporal metadata","LLM for summary generation"],"failure_modes":["LLM-based extraction introduces hallucination risk — entities may be incorrectly inferred from ambiguous text","Graph quality depends on input data quality and LLM capability — noisy or sparse work logs produce incomplete graphs","No built-in deduplication strategy — same entity mentioned differently may create duplicate nodes","Extraction latency scales with work volume — processing large backlogs of activities may be slow","Search quality depends on graph completeness — missing or poorly extracted entities reduce recall","Relationship ranking may surface spurious connections in densely connected graphs","No temporal reasoning — cannot distinguish 'current decision' from 'outdated decision' without explicit timestamps","Requires sufficient work history to be useful — sparse graphs return limited results","Summaries may omit important nuance or context that requires reading original sources","LLM-based summarization can introduce factual errors or misrepresentations","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.24,"ecosystem":0.46,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.692Z","last_scraped_at":"2026-05-04T08:09:54.664Z","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=rowboat-ai-coworker-that-turns-your-work-into-a-kn","compare_url":"https://unfragile.ai/compare?artifact=rowboat-ai-coworker-that-turns-your-work-into-a-kn"}},"signature":"62s9JjJa+v76Ta/CZFldq9XclKlEc/iXTQ82lQCOPZQiUQOTgMEKnaqlNKuqJQ5Hn+yhRj4pmj+6xBpe55/RBw==","signedAt":"2026-06-20T08:43:27.672Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/rowboat-ai-coworker-that-turns-your-work-into-a-kn","artifact":"https://unfragile.ai/rowboat-ai-coworker-that-turns-your-work-into-a-kn","verify":"https://unfragile.ai/api/v1/verify?slug=rowboat-ai-coworker-that-turns-your-work-into-a-kn","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"}}