{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-knowns-dev--knowns","slug":"knowns-dev--knowns","name":"knowns","type":"agent","url":"http://knowns.sh","page_url":"https://unfragile.ai/knowns-dev--knowns","categories":["ai-agents"],"tags":["ai","ai-agents","ai-native","ai-workflow","cli","context-management","developer-tools","devex","knowledge-base","llm","memory-layer","open-source"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-knowns-dev--knowns__cap_0","uri":"capability://memory.knowledge.git.tracked.persistent.task.memory.with.reference.based.context.linking","name":"git-tracked persistent task memory with reference-based context linking","description":"Stores project tasks as markdown files in .knowns/tasks/ directory with Git-friendly format, enabling AI agents to maintain persistent memory across sessions. Tasks include acceptance criteria, implementation plans, and @doc/path/@task-N references that create a context graph. When an AI agent is assigned a task, it parses all embedded references, recursively follows links to documentation, and builds a complete context graph before implementation — solving the stateless AI problem where context must be re-explained each session.","intents":["Give AI agents persistent memory of project tasks and decisions between sessions","Create a Git-trackable task system that AI can reference and understand","Link tasks to architectural documentation so AI has full context when working","Avoid re-explaining project context to AI agents on every interaction"],"best_for":["Teams using AI agents (Claude, GPT, etc.) for development workflows","Solo developers building LLM-powered applications who need persistent context","Projects where architectural decisions and task context must be version-controlled"],"limitations":["No built-in conflict resolution for concurrent task edits — relies on Git merge strategies","Reference parsing is regex-based (@doc/path syntax) — no semantic validation of link targets","Task state is stored in markdown frontmatter — no transactional guarantees across multiple file updates","No automatic cleanup of orphaned references when documentation is deleted"],"requires":["Git repository initialized in project root","Node.js 18+ for CLI execution","Write permissions to create .knowns/ directory","AI agent with MCP support or ability to read --plain text output"],"input_types":["task title and description (text)","acceptance criteria (markdown)","implementation plan (markdown)","reference links (@doc/path, @task-N syntax)"],"output_types":["markdown task files with YAML frontmatter","Git-tracked .knowns/tasks/ directory structure","resolved context graph (task + all linked docs)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-knowns-dev--knowns__cap_1","uri":"capability://tool.use.integration.mcp.protocol.integration.for.ai.agent.context.resolution","name":"mcp protocol integration for ai agent context resolution","description":"Implements a Model Context Protocol (MCP) server that exposes the task and documentation system to AI agents via standardized protocol bindings. When an AI agent connects via MCP, it can query tasks, resolve references, and retrieve full context graphs without parsing markdown directly. The MCP server translates internal FileStore operations into MCP resource and tool endpoints, enabling seamless integration with Claude, GPT, and other MCP-compatible agents.","intents":["Connect AI agents to knowns via standard MCP protocol without custom parsing","Allow AI agents to query tasks and automatically resolve linked documentation","Enable AI agents to read project context in a structured, queryable format","Integrate knowns into existing AI agent workflows that support MCP"],"best_for":["AI agent developers building Claude or GPT-powered development tools","Teams standardizing on MCP for AI tool integration","Projects requiring programmatic AI access to task and documentation context"],"limitations":["MCP server requires separate process or integration — not embedded in CLI by default","No built-in authentication — assumes MCP server runs in trusted environment","Context resolution latency depends on FileStore I/O performance — no caching layer","MCP protocol version compatibility must be managed manually"],"requires":["Node.js 18+ runtime","MCP-compatible AI agent (Claude 3.5+, custom agent with MCP client)","knowns CLI installed and project initialized","Network connectivity between agent and MCP server"],"input_types":["MCP protocol requests (resource queries, tool calls)","task IDs and document paths","reference resolution queries"],"output_types":["MCP resource responses (task content, documentation)","resolved context graphs (task + linked docs in structured format)","tool call results (task updates, reference queries)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-knowns-dev--knowns__cap_10","uri":"capability://automation.workflow.cross.runtime.compatibility.with.typescript.javascript.execution","name":"cross-runtime compatibility with typescript/javascript execution","description":"Implements knowns as a TypeScript codebase that compiles to JavaScript and runs on Node.js, Deno, and browser runtimes. The build system uses Vite for bundling and supports multiple entry points (CLI, server, web UI). Core logic is runtime-agnostic, with platform-specific adapters for file I/O, HTTP, and other system operations. This enables the same codebase to run as a CLI tool, HTTP server, web application, and embedded library.","intents":["Run knowns as a CLI tool on developer machines","Deploy knowns as an HTTP server for team access","Embed knowns in web applications or Electron apps","Use knowns as a library in Node.js or browser-based projects"],"best_for":["Teams wanting a single codebase that works across multiple runtimes","Projects requiring both CLI and server deployment options","Developers building tools that embed knowns functionality"],"limitations":["File I/O is runtime-specific — requires platform adapters for each runtime","Browser runtime has limited file system access — requires IndexedDB or similar","Build system complexity increases with multiple entry points","Testing must cover all supported runtimes — increases test matrix"],"requires":["Node.js 18+ for CLI and server","Deno 1.30+ for Deno runtime (if using Deno)","Modern browser with ES2020+ support (for web UI)","TypeScript 5.0+ for compilation"],"input_types":["TypeScript source code","build configuration (Vite, tsconfig)","platform-specific adapters"],"output_types":["compiled JavaScript (Node.js, Deno, browser)","bundled CLI executable","web application bundle (React UI)"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-knowns-dev--knowns__cap_11","uri":"capability://automation.workflow.web.ui.with.react.based.kanban.board.and.documentation.browser","name":"web ui with react-based kanban board and documentation browser","description":"Provides a React-based web interface that renders the same task and documentation data as the CLI. The web UI includes a Kanban board for visual task management, a documentation browser for exploring linked docs, and a task detail view with full context. The UI communicates with the knowns server via HTTP API and WebSocket for real-time updates. All UI state is derived from the FileStore, ensuring consistency with CLI and other interfaces.","intents":["Provide a visual interface for non-technical stakeholders to view task status","Enable drag-and-drop task management through Kanban board","Browse and search documentation through a visual interface","View task details with full context (linked docs, references, history)"],"best_for":["Teams with mixed technical and non-technical members","Projects where visual task management is preferred over CLI","Organizations wanting a lightweight alternative to Jira/Linear UI"],"limitations":["Web UI requires separate server process — not embedded in CLI","No offline support — requires network connectivity to server","Drag-and-drop Kanban updates are optimistic — may conflict with concurrent CLI edits","UI is read-only for some operations (e.g., creating complex references) — CLI is required"],"requires":["knowns server running (knowns server command)","Modern browser with React 18+ support","Network connectivity to server","HTTP API and WebSocket support on server"],"input_types":["task and documentation data from HTTP API","user interactions (drag-drop, form submissions, clicks)","WebSocket updates from server"],"output_types":["rendered Kanban board (visual task layout)","documentation browser (tree view of docs)","task detail view (full context with linked docs)","HTTP API requests (task updates, state transitions)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-knowns-dev--knowns__cap_2","uri":"capability://memory.knowledge.recursive.context.graph.resolution.with.reference.parsing","name":"recursive context graph resolution with reference parsing","description":"Parses @doc/path and @task-N reference syntax embedded in task descriptions and documentation, then recursively resolves all linked documents to build a complete context graph. When an AI agent requests a task, the system traverses the reference tree, fetches all linked documentation, and returns a flattened context structure. This enables AI agents to understand not just the immediate task but all architectural decisions, patterns, and related work that inform implementation.","intents":["Automatically gather all relevant documentation when AI agent starts working on a task","Create a knowledge graph where tasks link to architectural decisions and patterns","Avoid AI agents missing critical context because documentation wasn't explicitly mentioned","Enable AI to understand task dependencies and related work through reference chains"],"best_for":["Complex projects with multiple interconnected tasks and documentation","Teams where architectural context is distributed across multiple documents","AI-driven development workflows where context completeness is critical"],"limitations":["Reference parsing is regex-based (@doc/path syntax) — no validation that targets exist until resolution time","Circular references are not detected — can cause infinite loops if A references B and B references A","No depth limiting — deeply nested reference chains can cause performance degradation","Reference syntax is rigid — no support for conditional or parameterized references"],"requires":["Tasks and documents stored in .knowns/ directory structure","References formatted as @doc/path or @task-N in markdown content","FileStore API for reading task and document files","No external dependencies — uses built-in file I/O"],"input_types":["task file with embedded @doc/path references","documentation files with @task-N or @doc/path references","reference query (task ID or document path)"],"output_types":["resolved context graph (task + all transitively linked docs)","flattened context structure (array of task + docs in dependency order)","reference metadata (which docs are linked, depth of resolution)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-knowns-dev--knowns__cap_3","uri":"capability://automation.workflow.cli.first.task.and.documentation.management.with.kanban.workflow","name":"cli-first task and documentation management with kanban workflow","description":"Provides a command-line interface (knowns/kn commands) for creating, updating, and organizing tasks and documentation with built-in Kanban board state management. Tasks move through predefined states (backlog, in-progress, review, done) tracked in markdown frontmatter. The CLI supports batch operations, filtering, and status transitions. A companion web UI (React-based) renders the same data as a visual Kanban board, with both interfaces operating on the shared .knowns/ file store.","intents":["Manage project tasks from the command line without leaving the terminal","Organize work using Kanban workflow (backlog → in-progress → review → done)","Create and link documentation alongside task management","View task status and progress through CLI or visual web interface"],"best_for":["Developers who prefer CLI-first workflows and terminal-based tools","Teams wanting lightweight task management without external SaaS","Projects where task state must be Git-trackable and human-readable"],"limitations":["No real-time collaboration — concurrent CLI edits can cause file conflicts","Kanban states are hardcoded (backlog, in-progress, review, done) — no custom workflow states","Web UI requires separate server process — not embedded in CLI","No built-in notifications or reminders for task state changes"],"requires":["Node.js 18+ for CLI execution","knowns CLI installed globally or via npx","Git repository initialized (for version tracking)","Write permissions to .knowns/ directory"],"input_types":["task title and description (text)","task state transitions (backlog → in-progress, etc.)","CLI flags for filtering and batch operations","markdown content for documentation"],"output_types":["markdown task files with YAML frontmatter (state, created, updated, etc.)","CLI output (task list, status, confirmation messages)","web UI rendering (Kanban board, task details, documentation browser)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-knowns-dev--knowns__cap_4","uri":"capability://automation.workflow.version.history.and.rollback.with.filestore.versioning","name":"version history and rollback with filestore versioning","description":"Maintains a version history of all task and documentation changes using a VersionStore layer that tracks file mutations over time. Each change is recorded with timestamp and metadata, enabling rollback to previous states. The versioning system operates transparently on top of the FileStore, capturing all mutations whether they come from CLI, web UI, or API calls. This enables audit trails and recovery from accidental deletions or edits.","intents":["Track who changed what and when in task and documentation files","Recover from accidental task deletions or documentation overwrites","Maintain an audit trail of project decisions and task evolution","Rollback to previous task states without manual Git operations"],"best_for":["Teams requiring audit trails for compliance or accountability","Projects where task history is important for understanding decisions","Developers who want version control without relying on Git commits"],"limitations":["Version history is stored separately from Git — not integrated with Git history","No garbage collection — version store grows unbounded over time","Rollback is file-level only — no cross-file transaction support","Version metadata (author, reason) must be manually provided — no automatic capture"],"requires":["VersionStore API initialized with FileStore","Disk space for version history storage","No external dependencies — uses built-in file I/O"],"input_types":["file mutations (create, update, delete operations)","version metadata (timestamp, optional author/reason)"],"output_types":["version history (list of changes with timestamps)","previous file state (for rollback operations)","diff information (what changed between versions)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-knowns-dev--knowns__cap_5","uri":"capability://memory.knowledge.markdown.based.documentation.system.with.structured.metadata","name":"markdown-based documentation system with structured metadata","description":"Stores project documentation as markdown files in .knowns/docs/ with YAML frontmatter for metadata (title, tags, created, updated). Documentation supports standard markdown syntax plus knowns-specific reference syntax (@doc/path, @task-N) for linking to other docs and tasks. The system treats documentation as first-class entities that can be queried, linked, and versioned alongside tasks. A documentation browser in the web UI enables visual navigation of the doc structure.","intents":["Store architectural decisions and patterns in a Git-trackable format","Link documentation to tasks so AI agents understand design context","Create a searchable knowledge base of project patterns and decisions","Maintain documentation alongside code in version control"],"best_for":["Teams documenting architecture and design decisions","Projects where documentation must be version-controlled with code","AI-driven workflows where documentation is a primary context source"],"limitations":["No built-in markdown validation — malformed markdown is stored as-is","Metadata is YAML frontmatter — no schema validation","No automatic documentation generation from code — must be manually maintained","Search is basic (filename/tag matching) — no full-text semantic search"],"requires":[".knowns/docs/ directory structure","Markdown files with YAML frontmatter","Reference syntax (@doc/path, @task-N) for linking","No external dependencies — uses built-in file I/O"],"input_types":["markdown content (text)","YAML frontmatter (title, tags, metadata)","reference links (@doc/path, @task-N syntax)"],"output_types":["markdown files with YAML frontmatter","parsed documentation metadata (title, tags, links)","rendered HTML (via web UI or --plain output)"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-knowns-dev--knowns__cap_6","uri":"capability://memory.knowledge.plain.text.context.export.for.ai.agent.consumption","name":"plain-text context export for ai agent consumption","description":"Exports task and documentation context as plain text (via --plain flag) in a format optimized for AI agent consumption. The export includes task details, acceptance criteria, implementation plans, and all recursively resolved linked documentation in a single text block. This enables AI agents without MCP support to access full context by reading a single text file or stdout. The export format is designed to be token-efficient while preserving all relevant information.","intents":["Provide AI agents with full project context without MCP protocol overhead","Export task context in a format that fits within AI token limits","Enable integration with AI agents that don't support MCP","Create shareable context snapshots for documentation or collaboration"],"best_for":["AI agent workflows that don't support MCP protocol","Quick context sharing without setting up MCP server","Debugging AI agent context by inspecting exported text","Creating context snapshots for documentation or handoff"],"limitations":["Plain text export is static — doesn't update when underlying docs change","No structured metadata in export — AI must parse text to extract task state","Export size can be large for deeply nested reference graphs — may exceed token limits","No compression or summarization — full context is always exported"],"requires":["knowns CLI installed","Task or document path to export","--plain flag support in CLI"],"input_types":["task ID or document path","--plain flag","optional filtering parameters"],"output_types":["plain text (stdout or file)","resolved context graph in text format","task details + all linked documentation as single text block"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-knowns-dev--knowns__cap_7","uri":"capability://automation.workflow.time.tracking.and.effort.estimation.with.task.metadata","name":"time tracking and effort estimation with task metadata","description":"Tracks time spent on tasks and estimates effort through YAML frontmatter metadata (estimated hours, actual hours, time logs). Tasks maintain a time log of work sessions with timestamps and duration. The system enables reporting on effort vs. estimate, identifying tasks that exceed estimates, and tracking team velocity. Time data is stored in markdown frontmatter, making it Git-trackable and queryable.","intents":["Track actual time spent on tasks for retrospectives and planning","Compare estimated vs. actual effort to improve estimation accuracy","Identify tasks that are taking longer than expected","Generate velocity reports for sprint planning"],"best_for":["Teams using time-based metrics for planning and retrospectives","Projects where effort estimation accuracy is important","Developers who want lightweight time tracking without external tools"],"limitations":["Time tracking is manual — no automatic timer or clock-in/out","No integration with calendar or scheduling systems","Time logs are stored in markdown — no structured time entry validation","No reporting UI — time data must be queried via CLI or exported"],"requires":["Task files with time metadata in YAML frontmatter","Manual time entry (estimated hours, actual hours, time logs)","No external dependencies — uses built-in file I/O"],"input_types":["estimated hours (number)","actual hours (number)","time log entries (timestamp, duration)"],"output_types":["task metadata with time information","time reports (effort vs. estimate, velocity)","time log history (all work sessions)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-knowns-dev--knowns__cap_8","uri":"capability://search.retrieval.search.and.filtering.with.tag.based.and.full.text.capabilities","name":"search and filtering with tag-based and full-text capabilities","description":"Provides search and filtering across tasks and documentation using tag-based queries and basic full-text matching. Tags are stored in YAML frontmatter and indexed for fast lookup. The CLI supports filtering by tag, status, and other metadata. Search results can be piped to other commands for batch operations. The system maintains a simple in-memory index of tags and metadata for fast filtering without external search infrastructure.","intents":["Find tasks and documentation by tag or keyword without external search tools","Filter tasks by status, priority, or other metadata for reporting","Batch operate on filtered task sets (e.g., update all 'urgent' tasks)","Discover related documentation through tag-based search"],"best_for":["Teams wanting lightweight search without Elasticsearch or similar","CLI-first workflows where search is integrated into command pipelines","Projects with moderate task/doc volume (hundreds, not millions)"],"limitations":["Full-text search is basic (substring matching) — no semantic or fuzzy search","No ranking or relevance scoring — results are unordered","Search index is in-memory — rebuilt on each query (no persistence)","No advanced query syntax (AND, OR, NOT) — only simple tag matching"],"requires":["Tasks and docs with tags in YAML frontmatter","CLI search commands (knowns search, kn search)","No external dependencies — uses built-in file I/O"],"input_types":["search query (tag name or keyword)","filter parameters (status, priority, etc.)","optional regex patterns"],"output_types":["filtered task/doc list (matching results)","search result metadata (title, tags, status)","piped output for batch operations"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-knowns-dev--knowns__cap_9","uri":"capability://tool.use.integration.http.api.and.websocket.protocol.for.programmatic.access","name":"http api and websocket protocol for programmatic access","description":"Exposes task and documentation data through a REST-like HTTP API and WebSocket protocol for real-time updates. The API supports CRUD operations on tasks and docs, query operations for filtering and search, and WebSocket subscriptions for live updates when data changes. The server layer (Node.js HTTP server) translates API requests to FileStore operations, enabling programmatic access without CLI. WebSocket connections enable real-time synchronization between multiple clients (CLI, web UI, external tools).","intents":["Build custom tools and integrations that read/write knowns data programmatically","Enable real-time synchronization between CLI, web UI, and external tools","Create dashboards or reporting tools that query task and documentation data","Integrate knowns with external systems (CI/CD, project management, etc.)"],"best_for":["Teams building custom integrations or dashboards on top of knowns","Projects requiring programmatic access to task and documentation data","Real-time collaboration scenarios where multiple clients need live updates"],"limitations":["No built-in authentication — assumes API runs in trusted environment","No rate limiting or throttling — can be overwhelmed by high-frequency requests","WebSocket connections are not persistent across server restarts","API schema is not formally documented (OpenAPI/Swagger) — must infer from code"],"requires":["Node.js 18+ for HTTP server","knowns server running (knowns server command)","Network connectivity to server (localhost or remote)","HTTP client library (curl, fetch, axios, etc.)"],"input_types":["HTTP requests (GET, POST, PUT, DELETE)","JSON request bodies (task/doc data)","WebSocket connection requests"],"output_types":["JSON responses (task/doc data, query results)","HTTP status codes (200, 404, 500, etc.)","WebSocket messages (data updates, notifications)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Git repository initialized in project root","Node.js 18+ for CLI execution","Write permissions to create .knowns/ directory","AI agent with MCP support or ability to read --plain text output","Node.js 18+ runtime","MCP-compatible AI agent (Claude 3.5+, custom agent with MCP client)","knowns CLI installed and project initialized","Network connectivity between agent and MCP server","Node.js 18+ for CLI and server","Deno 1.30+ for Deno runtime (if using Deno)"],"failure_modes":["No built-in conflict resolution for concurrent task edits — relies on Git merge strategies","Reference parsing is regex-based (@doc/path syntax) — no semantic validation of link targets","Task state is stored in markdown frontmatter — no transactional guarantees across multiple file updates","No automatic cleanup of orphaned references when documentation is deleted","MCP server requires separate process or integration — not embedded in CLI by default","No built-in authentication — assumes MCP server runs in trusted environment","Context resolution latency depends on FileStore I/O performance — no caching layer","MCP protocol version compatibility must be managed manually","File I/O is runtime-specific — requires platform adapters for each runtime","Browser runtime has limited file system access — requires IndexedDB or similar","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.27275836268870207,"quality":0.49,"ecosystem":0.6000000000000001,"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":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:59:55.150Z","last_commit":"2026-04-30T13:42:47Z"},"community":{"stars":184,"forks":39,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=knowns-dev--knowns","compare_url":"https://unfragile.ai/compare?artifact=knowns-dev--knowns"}},"signature":"mJ9845wYAWf/fvX/spGr88k9fPg/vltlBO2DBhTCaGZ6WJoOC5cy2+wLXNiAHhxS03Gde5mpIvghH+AJuuhhBw==","signedAt":"2026-06-22T07:53:22.303Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/knowns-dev--knowns","artifact":"https://unfragile.ai/knowns-dev--knowns","verify":"https://unfragile.ai/api/v1/verify?slug=knowns-dev--knowns","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"}}