{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-momotech--linkwork","slug":"momotech--linkwork","name":"LinkWork","type":"repo","url":"https://github.com/momotech/LinkWork/blob/master/README_zh-CN.md","page_url":"https://unfragile.ai/momotech--linkwork","categories":["automation","code-review-security"],"tags":["agent-framework","ai-agents","ai-automation","ai-platform","ai-workers","ai-workforce","enterprise-ai","harness","harness-engineering","kubernetes","multi-agent"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-momotech--linkwork__cap_0","uri":"capability://automation.workflow.containerized.role.based.ai.worker.deployment","name":"containerized-role-based-ai-worker-deployment","description":"Deploys AI agents as isolated, immutable container images following the 'One Role, One Image' paradigm, where skills, MCP configurations, and security policies are baked into the container during build-time rather than injected at runtime. This approach eliminates environment drift by treating the runtime filesystem as read-only and implements fail-fast validation during image construction to prevent broken capabilities from reaching production. The linkwork-server orchestrates role lifecycle management, scheduling, and approval workflows across Kubernetes clusters using the Volcano scheduler for workload distribution.","intents":["Deploy AI agents at enterprise scale with guaranteed reproducible behavior across environments","Ensure AI worker configurations are immutable and version-controlled as container images","Prevent runtime environment drift that causes unpredictable AI behavior in production","Manage multiple specialized AI roles (e.g., code reviewer, documentation writer) as independent containerized services"],"best_for":["Enterprise teams managing multi-agent AI workforces at scale","Organizations requiring strict reproducibility and auditability of AI behavior","DevOps teams already operating Kubernetes clusters with container orchestration expertise"],"limitations":["Requires Kubernetes cluster with Volcano scheduler — not suitable for serverless or edge deployments","Build-time solidification means skill updates require full container rebuild and redeployment cycle","Container image size grows with each added skill/dependency, increasing storage and pull overhead","No dynamic skill injection at runtime — all capabilities must be declared at image build time"],"requires":["Kubernetes 1.20+ cluster","Volcano scheduler installed and configured","Docker or container runtime compatible with OCI spec","linkwork-server Java service running with role configuration manifests"],"input_types":["YAML role definitions (skills, MCP tools, security policies)","Dockerfile specifications for custom role images","Task requests with role assignment and parameters"],"output_types":["Container image artifacts (OCI format)","Kubernetes Pod/Job resources","Task execution logs and audit trails","Role status and health metrics"],"categories":["automation-workflow","container-orchestration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-momotech--linkwork__cap_1","uri":"capability://memory.knowledge.declarative.skill.system.with.versioning","name":"declarative-skill-system-with-versioning","description":"Implements a declarative skill marketplace where AI capabilities are defined as versioned, composable modules that can be pinned to specific versions and shared across teams. Skills are registered in a central marketplace accessible via the linkwork-web dashboard, with dependency resolution and compatibility checking performed during the build phase. The linkwork-agent-sdk (Python) provides the runtime interface for agents to discover and invoke registered skills, while the skill definitions themselves are stored as declarative YAML/JSON specifications that map natural language intents to executable code entities.","intents":["Define reusable AI capabilities once and share them across multiple agent roles without code duplication","Pin agent roles to specific skill versions to ensure reproducible behavior across deployments","Discover available skills through a centralized marketplace and understand their dependencies","Compose complex agent behaviors by combining multiple skills with version constraints"],"best_for":["Teams building multiple AI agents with overlapping capabilities","Organizations needing skill governance and version control across teams","Enterprises requiring skill dependency tracking and compatibility validation"],"limitations":["Skill definitions must be declarative — complex conditional logic requires wrapper skills","No runtime skill swapping — version changes require container rebuild","Dependency resolution happens at build-time only, not runtime, limiting dynamic composition","Skill marketplace requires centralized registry management and access control setup"],"requires":["linkwork-agent-sdk Python 3.8+","YAML/JSON skill definition files with proper schema","linkwork-server running with skill marketplace enabled","linkwork-web dashboard for skill discovery and management"],"input_types":["YAML/JSON skill definitions (name, version, parameters, dependencies)","Skill implementation code (Python functions with @skill decorator)","Dependency specifications (skill name, version constraints)"],"output_types":["Skill registry entries with metadata","Dependency resolution graphs","Skill marketplace UI with search and filtering","Version compatibility reports"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-momotech--linkwork__cap_10","uri":"capability://automation.workflow.dashboard.ui.for.task.management.and.skill.discovery","name":"dashboard-ui-for-task-management-and-skill-discovery","description":"Provides a web-based dashboard (linkwork-web, TypeScript/Vue) for managing agent tasks, discovering available skills, monitoring execution, and configuring roles. The dashboard displays task queues, execution status, real-time logs, and metrics. The skill marketplace section enables browsing available skills with descriptions, versions, dependencies, and usage examples. Role management UI allows creating and editing agent roles, assigning skills and tools, and setting permissions. The dashboard integrates with the backend services through REST APIs and WebSocket connections for real-time updates.","intents":["Monitor and manage AI agent tasks from a centralized web interface","Discover available skills and understand their capabilities and dependencies","Create and configure new agent roles without writing code","View real-time execution logs and metrics for running tasks"],"best_for":["Non-technical operators managing agent tasks and monitoring execution","Teams discovering and reusing skills across the organization","Organizations needing a centralized control plane for AI workforce management"],"limitations":["Web UI adds operational overhead — requires separate service deployment and maintenance","Complex role configurations may be difficult to express through UI — YAML editing may be needed","Dashboard performance may degrade with large numbers of concurrent tasks or skills","UI customization requires frontend development skills"],"requires":["linkwork-web service running (Node.js 14+, Vue 3)","Backend API services (linkwork-server, linkwork-executor, linkwork-mcp-gateway)","WebSocket connectivity between dashboard and backend","Modern web browser with JavaScript support"],"input_types":["Task creation requests (role, parameters, priority)","Role configuration updates","Skill discovery queries","Permission and access control updates"],"output_types":["Task status and execution logs","Skill marketplace listings with metadata","Role configuration UI","Real-time metrics and monitoring dashboards"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-momotech--linkwork__cap_11","uri":"capability://automation.workflow.kubernetes.native.scheduling.with.volcano","name":"kubernetes-native-scheduling-with-volcano","description":"Integrates with Kubernetes and the Volcano scheduler to manage agent workload scheduling across clusters. Agent tasks are submitted as Kubernetes Jobs or Pods with resource requests/limits, and Volcano handles scheduling based on resource availability, priority, and fairness. The system supports gang scheduling (ensuring all pods of a task are scheduled together), queue-based prioritization, and preemption policies. Agents run as containerized workloads in the Kubernetes cluster, with automatic scaling based on task queue depth and resource availability. The linkwork-server manages the Kubernetes API interactions and task-to-pod mapping.","intents":["Scale AI agent execution across Kubernetes clusters based on task demand","Prioritize high-importance tasks over low-importance ones in shared clusters","Ensure fair resource allocation across multiple teams or projects","Automatically clean up completed agent tasks and reclaim resources"],"best_for":["Organizations already running Kubernetes clusters and wanting to leverage them for AI agents","Teams needing multi-tenant resource sharing and fair scheduling","Enterprises requiring auto-scaling and dynamic resource allocation"],"limitations":["Requires Kubernetes expertise to set up and maintain cluster and Volcano scheduler","Kubernetes overhead adds complexity compared to simpler deployment models","Resource requests/limits must be tuned per agent role — incorrect values cause scheduling issues","Volcano scheduler adds scheduling latency — tasks may wait in queue before execution"],"requires":["Kubernetes 1.20+ cluster","Volcano scheduler installed and configured","linkwork-server with Kubernetes API client","Container images for agent roles","Resource quotas and namespace configuration"],"input_types":["Task requests with resource requirements (CPU, memory, GPU)","Priority specifications","Scheduling constraints (node selectors, affinity rules)"],"output_types":["Kubernetes Pod/Job resources","Scheduling decisions and queue status","Resource utilization metrics","Task execution status"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-momotech--linkwork__cap_12","uri":"capability://code.generation.editing.agent.sdk.with.skill.invocation.and.llm.integration","name":"agent-sdk-with-skill-invocation-and-llm-integration","description":"Provides the linkwork-agent-sdk (Python) that agents use to invoke skills, call tools through the MCP gateway, and interact with LLMs. The SDK provides decorators for defining skills (@skill), context managers for workstation access, and utilities for structured output parsing. Agents use the SDK to discover available skills at runtime, invoke them with parameters, and handle results. The SDK handles LLM integration, including prompt construction, function calling, and response parsing. It also manages context passing between skill invocations and maintains execution state within a workstation.","intents":["Write agent code that invokes skills and tools without worrying about underlying infrastructure","Define custom skills as Python functions with automatic registration and discovery","Integrate with LLMs for decision-making and planning within agent workflows","Access workstation filesystem and environment within skill implementations"],"best_for":["Python developers writing custom agent implementations","Teams building specialized agent roles with domain-specific skills","Organizations extending LinkWork with custom capabilities"],"limitations":["Python-only SDK — no support for other languages without wrappers","SDK abstractions add overhead compared to direct API calls","Skill definitions must follow SDK conventions — incompatible code requires refactoring","Limited documentation and examples for advanced use cases"],"requires":["Python 3.8+","linkwork-agent-sdk package installed","Access to linkwork-server and linkwork-mcp-gateway services","LLM provider credentials (OpenAI, Anthropic, etc.)"],"input_types":["Skill function definitions with @skill decorator","LLM provider configuration","Task parameters and context"],"output_types":["Skill execution results","LLM responses and function calls","Structured output from agents","Workstation state and artifacts"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-momotech--linkwork__cap_2","uri":"capability://tool.use.integration.mcp.tool.gateway.with.auth.and.metering","name":"mcp-tool-gateway-with-auth-and-metering","description":"Provides a Model Context Protocol (MCP) gateway (linkwork-mcp-gateway in Go) that acts as a proxy between AI agents and external tools, handling MCP discovery, authentication, and usage metering. The gateway implements a schema-based function registry that validates tool invocations against declared schemas before execution, supports multiple authentication methods (API keys, OAuth, mTLS), and tracks tool usage metrics for billing and audit purposes. Agents interact with tools through a unified interface regardless of the underlying tool implementation, with the gateway handling protocol translation and error handling.","intents":["Connect AI agents to external APIs and tools through a standardized MCP interface","Control which tools each agent role can access through declarative permission policies","Track and meter tool usage for cost allocation and audit compliance","Validate tool invocations against schemas before execution to prevent malformed requests"],"best_for":["Enterprises integrating AI agents with multiple external APIs and SaaS tools","Teams requiring fine-grained tool access control and usage tracking","Organizations needing audit trails and cost allocation for tool usage"],"limitations":["MCP gateway adds ~50-100ms latency per tool invocation due to proxy overhead","Tool schema validation requires explicit schema definitions — tools without schemas cannot be validated","No built-in caching of tool responses — each invocation hits the gateway","Authentication credential management requires secure secret storage (Vault, K8s Secrets)"],"requires":["linkwork-mcp-gateway Go service running and accessible to agents","MCP-compatible tool implementations or adapters","Tool schema definitions in JSON Schema format","Authentication credentials for each tool (API keys, OAuth tokens, certificates)"],"input_types":["Tool invocation requests with function name and parameters","MCP tool definitions with schemas and authentication methods","Access control policies mapping roles to allowed tools"],"output_types":["Tool execution results (JSON, text, or binary)","Usage metrics and audit logs","Tool discovery responses with available functions and schemas","Error responses with validation details"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-momotech--linkwork__cap_3","uri":"capability://safety.moderation.policy.driven.command.execution.with.approval.workflows","name":"policy-driven-command-execution-with-approval-workflows","description":"Implements deep command analysis and policy enforcement through the linkwork-executor (Go service) that intercepts all command executions before they run, analyzing them against declarative security policies. High-risk operations (e.g., destructive commands, external network calls) trigger human-in-the-loop approval workflows where designated approvers review and authorize execution. The executor maintains an audit trail of all commands, approvals, and execution results, with policies defined declaratively in YAML and evaluated at runtime before command execution. Policies can enforce constraints on command patterns, resource usage, network access, and file operations.","intents":["Prevent AI agents from executing dangerous commands without human review and approval","Enforce organizational security policies on what commands agents can run","Maintain audit trails of all agent actions for compliance and forensics","Define risk levels for different command categories and route high-risk operations to approvers"],"best_for":["Enterprises with strict security and compliance requirements (SOC2, HIPAA, PCI-DSS)","Organizations where AI agents have access to production systems and need human oversight","Teams requiring detailed audit trails and approval workflows for agent actions"],"limitations":["Approval workflows add latency — high-risk operations may wait minutes to hours for approval","Policy evaluation overhead adds ~20-50ms per command execution","Policies must be manually maintained and updated as new risk patterns emerge","No automatic policy learning — policies require explicit definition by security teams"],"requires":["linkwork-executor Go service running with policy engine enabled","Declarative security policy definitions in YAML format","Approval workflow configuration with designated approvers","Audit log storage (database or object storage) for compliance retention"],"input_types":["Command strings to be executed","Security policy definitions (command patterns, risk levels, constraints)","Approval workflow configurations (approvers, escalation rules)","Context about the executing agent (role, permissions, history)"],"output_types":["Policy evaluation results (allow/deny/require-approval)","Approval requests with command details and risk assessment","Audit logs with command, approver, timestamp, and result","Policy violation alerts and security events"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-momotech--linkwork__cap_4","uri":"capability://automation.workflow.harness.engineering.build.time.validation","name":"harness-engineering-build-time-validation","description":"Implements a build-time validation and solidification system (Harness Engineering) that checks skill injection, dependency resolution, and security policy compatibility during container image construction. If any skill, MCP configuration, or policy fails validation during the build phase, the image is not created, preventing broken capabilities from reaching production. This fail-fast mechanism catches configuration errors early in the CI/CD pipeline rather than at runtime, with detailed error reporting that guides developers to fix issues. The build process is declarative, driven by role definition files that specify skills, tools, and policies to be baked into the image.","intents":["Catch configuration errors and missing dependencies before deploying AI agents to production","Validate that all declared skills and MCP tools are compatible and properly configured","Ensure security policies are syntactically correct and enforceable before runtime","Provide clear error messages that guide developers to fix configuration issues"],"best_for":["Teams using CI/CD pipelines and wanting early validation of agent configurations","Organizations requiring high confidence in agent deployments before production release","DevOps teams managing multiple agent roles and needing consistent validation"],"limitations":["Build-time validation cannot catch runtime errors or edge cases in skill execution","Validation rules must be explicitly defined — no automatic detection of all possible issues","Build failures require full image rebuild after fixes, adding deployment cycle time","Complex dependency graphs may require significant build-time computation"],"requires":["Role definition files with complete skill and tool declarations","Skill implementations available during build (not at runtime)","Build environment with access to skill registries and MCP tool definitions","Container build system (Docker, Buildah) with LinkWork build plugins"],"input_types":["Role definition YAML files (skills, MCP tools, policies)","Skill implementation code and metadata","Security policy definitions","Dependency specifications"],"output_types":["Build success/failure status","Validation error reports with specific issues and remediation guidance","Container image artifacts (only on successful validation)","Build logs with detailed validation steps"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-momotech--linkwork__cap_5","uri":"capability://automation.workflow.workstation.model.for.agent.context.management","name":"workstation-model-for-agent-context-management","description":"Implements a workstation model that provides each AI agent with a persistent, isolated execution context including filesystem, environment variables, and state management. The workstation acts as a virtual workspace where agents can create files, clone repositories, and maintain state across multiple task executions without interference from other agents. Workstations are containerized and ephemeral by default (cleaned up after task completion) but can be configured for persistence. The model enables agents to perform multi-step workflows that require maintaining state, such as cloning a repo, making changes, running tests, and committing results.","intents":["Provide agents with isolated filesystem and environment for executing multi-step workflows","Enable agents to maintain state across multiple task executions within a workstation","Prevent cross-contamination between concurrent agent executions","Support complex workflows requiring file operations, repository cloning, and build processes"],"best_for":["Agents performing code review, refactoring, or other file-based operations","Workflows requiring multi-step execution with state persistence","Teams needing strong isolation between concurrent agent tasks"],"limitations":["Workstation storage overhead — each active workstation requires disk space for filesystem","Ephemeral workstations lose state after task completion — persistence requires explicit configuration","Network isolation may prevent agents from accessing external resources without explicit configuration","Workstation cleanup and garbage collection requires manual or automated management"],"requires":["Container runtime with volume mount support","Persistent storage (local or network) for workstation filesystems","linkwork-executor service managing workstation lifecycle","Task definitions specifying workstation requirements and persistence policies"],"input_types":["Task requests with workstation configuration (size, persistence, network access)","Initial files or repositories to clone into workstation","Environment variable specifications"],"output_types":["Workstation filesystem state (files, directories)","Task execution results and logs","Artifacts generated during task execution","Workstation cleanup status"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-momotech--linkwork__cap_6","uri":"capability://automation.workflow.real.time.task.monitoring.and.streaming.logs","name":"real-time-task-monitoring-and-streaming-logs","description":"Provides real-time monitoring of agent task execution through WebSocket-based streaming of logs, metrics, and status updates to the linkwork-web dashboard. As agents execute tasks, logs are streamed in real-time rather than being buffered and returned at completion, enabling operators to monitor progress and intervene if needed. The system tracks task status (queued, running, completed, failed), execution metrics (duration, resource usage), and provides drill-down capabilities to view detailed logs for specific steps. Streaming is implemented through a pub-sub architecture where the executor publishes events that are subscribed to by the web dashboard.","intents":["Monitor AI agent task execution in real-time without waiting for completion","Detect and respond to long-running or stuck tasks by observing live logs","Understand agent behavior and debug issues by viewing detailed execution logs","Track task metrics and performance characteristics across multiple concurrent executions"],"best_for":["Operators managing multiple concurrent agent tasks and needing visibility","Teams debugging agent behavior and needing detailed execution logs","Organizations requiring real-time monitoring for SLA compliance"],"limitations":["WebSocket connections add overhead — high volume of concurrent tasks may require connection pooling","Log streaming can generate large volumes of data — filtering and sampling may be needed","Real-time updates require persistent connections, which may not work through all proxies/firewalls","Log retention requires storage infrastructure — unbounded log growth needs cleanup policies"],"requires":["linkwork-web dashboard with WebSocket support","linkwork-executor publishing task events","Network connectivity between executor and dashboard (WebSocket ports)","Log storage backend (database or object storage) for retention"],"input_types":["Task execution events from executor (started, log line, completed, failed)","Metric data (duration, resource usage, step timing)","Status updates and state transitions"],"output_types":["Real-time log streams in dashboard UI","Task status and progress indicators","Execution metrics and performance graphs","Drill-down views for detailed step-by-step logs"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-momotech--linkwork__cap_7","uri":"capability://automation.workflow.git.and.oss.based.deliverable.output","name":"git-and-oss-based-deliverable-output","description":"Enables AI agents to deliver results directly into production systems through Git commits or Object Storage Service (OSS) uploads rather than returning chat messages. Agents can clone repositories, make changes, run tests, and commit results back to Git with proper commit messages and metadata. For non-code artifacts, agents can upload results to OSS (S3, GCS, Aliyun OSS) with configurable paths and metadata. This approach treats agent outputs as first-class deliverables that integrate directly into CI/CD pipelines and production workflows, rather than as conversational responses that require manual integration.","intents":["Have AI agents commit code changes directly to Git repositories as part of automated workflows","Store agent-generated artifacts (reports, datasets, models) in object storage for downstream processing","Integrate agent outputs directly into CI/CD pipelines without manual handoff","Enable agents to participate in production delivery workflows as autonomous contributors"],"best_for":["Teams using AI agents for code generation, refactoring, or documentation","Organizations with mature CI/CD pipelines that can consume agent-generated artifacts","Workflows where agent outputs need to be version-controlled and auditable"],"limitations":["Git integration requires proper authentication (SSH keys, tokens) and branch management","OSS uploads require cloud credentials and network access to storage services","Commit quality depends on agent implementation — poor commits can pollute repository history","No built-in conflict resolution for concurrent agent commits to same files"],"requires":["Git repository access with appropriate credentials (SSH keys or tokens)","Git client installed in agent workstation","OSS service credentials (AWS S3, GCS, Aliyun OSS, etc.) if using object storage","Proper branch management and commit message conventions"],"input_types":["Repository URLs and branch specifications","File changes to commit (diffs, new files)","Commit message and metadata","OSS bucket paths and upload configurations"],"output_types":["Git commits with hashes and metadata","OSS object URLs and metadata","Commit status and validation results","Integration with CI/CD pipeline triggers"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-momotech--linkwork__cap_8","uri":"capability://safety.moderation.role.based.access.control.with.skill.permissions","name":"role-based-access-control-with-skill-permissions","description":"Implements fine-grained role-based access control (RBAC) where each AI agent role has explicitly declared permissions for which skills and tools it can access. Permissions are defined declaratively in role definitions and enforced at the gateway and executor layers. The system supports role hierarchies, permission inheritance, and dynamic permission updates without requiring agent restart. Permissions are checked before skill invocation and tool access, with violations logged and potentially triggering alerts. The linkwork-web dashboard provides UI for managing roles, permissions, and auditing access patterns.","intents":["Restrict which skills and tools each agent role can access based on job function","Prevent agents from accessing sensitive operations or data they don't need","Audit which agents accessed which tools and when for compliance purposes","Update agent permissions dynamically without redeploying agent containers"],"best_for":["Enterprises with strict access control requirements and compliance mandates","Organizations managing multiple agent roles with different permission levels","Teams needing detailed audit trails of agent access to sensitive tools"],"limitations":["Permission checks add latency to skill invocation (~5-10ms per check)","Permissions must be manually defined and maintained — no automatic least-privilege inference","Complex permission hierarchies can become difficult to manage and audit","No built-in attribute-based access control (ABAC) — only role-based"],"requires":["Role definitions with explicit skill and tool permissions","RBAC policy engine in linkwork-server or gateway","Audit log storage for permission check results","linkwork-web dashboard for permission management UI"],"input_types":["Role definitions with permission specifications","Skill and tool access requests from agents","Permission update requests"],"output_types":["Permission check results (allow/deny)","Audit logs with access attempts and results","Permission violation alerts","Role and permission management UI"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-momotech--linkwork__cap_9","uri":"capability://planning.reasoning.multi.provider.llm.orchestration.with.fallback","name":"multi-provider-llm-orchestration-with-fallback","description":"Provides LLM orchestration through the linkwork-agent-sdk that supports multiple LLM providers (OpenAI, Anthropic, local models via Ollama) with automatic fallback and retry logic. Agents can be configured to use a primary LLM provider with fallback to secondary providers if the primary fails or is rate-limited. The SDK abstracts provider-specific APIs (function calling, streaming, token counting) behind a unified interface, enabling agents to work with different LLM backends without code changes. Provider selection can be configured per-role or per-task, with metrics tracking which provider was used and performance characteristics.","intents":["Use multiple LLM providers to avoid vendor lock-in and ensure availability","Automatically fall back to alternative providers if primary provider fails or is rate-limited","Switch between LLM providers (e.g., GPT-4 for complex tasks, GPT-3.5 for simple tasks) based on task requirements","Track LLM usage and costs across multiple providers for billing and optimization"],"best_for":["Organizations wanting to avoid LLM provider lock-in","Teams requiring high availability and fallback capabilities","Enterprises needing cost optimization through provider selection"],"limitations":["Fallback logic adds latency when primary provider fails (retry timeout + fallback invocation)","Different LLM providers have different capabilities (function calling, streaming, context length) — abstraction may hide limitations","Provider-specific features (e.g., vision capabilities) may not be available across all providers","Cost tracking requires integration with provider billing APIs"],"requires":["linkwork-agent-sdk Python 3.8+","API keys for at least one LLM provider (OpenAI, Anthropic, etc.)","LLM provider configuration in role definitions","Network connectivity to LLM provider APIs"],"input_types":["LLM provider configurations (API keys, model names, rate limits)","Fallback provider specifications","Task requests with optional provider preferences"],"output_types":["LLM responses (text, function calls, structured output)","Provider usage metrics (which provider was used, latency, cost)","Fallback event logs"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":38,"verified":false,"data_access_risk":"high","permissions":["Kubernetes 1.20+ cluster","Volcano scheduler installed and configured","Docker or container runtime compatible with OCI spec","linkwork-server Java service running with role configuration manifests","linkwork-agent-sdk Python 3.8+","YAML/JSON skill definition files with proper schema","linkwork-server running with skill marketplace enabled","linkwork-web dashboard for skill discovery and management","linkwork-web service running (Node.js 14+, Vue 3)","Backend API services (linkwork-server, linkwork-executor, linkwork-mcp-gateway)"],"failure_modes":["Requires Kubernetes cluster with Volcano scheduler — not suitable for serverless or edge deployments","Build-time solidification means skill updates require full container rebuild and redeployment cycle","Container image size grows with each added skill/dependency, increasing storage and pull overhead","No dynamic skill injection at runtime — all capabilities must be declared at image build time","Skill definitions must be declarative — complex conditional logic requires wrapper skills","No runtime skill swapping — version changes require container rebuild","Dependency resolution happens at build-time only, not runtime, limiting dynamic composition","Skill marketplace requires centralized registry management and access control setup","Web UI adds operational overhead — requires separate service deployment and maintenance","Complex role configurations may be difficult to express through UI — YAML editing may be needed","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.19244674301178297,"quality":0.5,"ecosystem":0.7000000000000001,"match_graph":0.25,"freshness":0.75,"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":"2026-05-24T12:16:22.062Z","last_scraped_at":"2026-05-03T13:59:57.743Z","last_commit":"2026-03-31T07:58:36Z"},"community":{"stars":98,"forks":13,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=momotech--linkwork","compare_url":"https://unfragile.ai/compare?artifact=momotech--linkwork"}},"signature":"d61fA3a57aSnYhMx/uLNlCL2zpEWxObog62SqBHb9hmme31+DQPrMq6MRmwUsLlTChY+LA3MCHPzN6ftaJKVCA==","signedAt":"2026-06-20T17:31:07.520Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/momotech--linkwork","artifact":"https://unfragile.ai/momotech--linkwork","verify":"https://unfragile.ai/api/v1/verify?slug=momotech--linkwork","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"}}