{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-dagucloud--dagu","slug":"dagucloud--dagu","name":"dagu","type":"workflow","url":"https://dagu.sh","page_url":"https://unfragile.ai/dagucloud--dagu","categories":["automation"],"tags":["agent-workflow","ai-workflow","continuous-delivery","cron","dag","data-pipeline","devops","directed-acyclic-graph","durable-execution","durable-workflows","job-scheduler","task-automation","task-scheduler","workflow-engine","workflow-management","workflow-orchestration","workflow-scheduler"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-dagucloud--dagu__cap_0","uri":"capability://automation.workflow.declarative.dag.based.workflow.definition.via.yaml","name":"declarative dag-based workflow definition via yaml","description":"Dagu parses YAML files into directed acyclic graphs (DAGs) where each step is a node with dependencies explicitly declared. The engine validates the DAG structure at parse time, detects cycles, and builds an execution plan that respects task dependencies. This file-based approach eliminates the need for a UI or database schema — workflows are version-controllable text artifacts that can be audited, diffed, and reviewed like code.","intents":["Define multi-step workflows as declarative YAML files that can be version-controlled in Git","Ensure task dependencies are explicit and validated before execution","Run workflows on air-gapped systems without external orchestration services","Migrate existing shell scripts and cron jobs into structured, observable workflows"],"best_for":["DevOps teams managing CI/CD pipelines without cloud dependencies","AI agent orchestration teams needing sovereign infrastructure","Data engineers building ETL pipelines on-premises","Solo developers automating multi-step tasks on laptops or servers"],"limitations":["No built-in UI for visual workflow design — YAML editing required","DAG validation happens at parse time; runtime dependency injection not supported","No native support for dynamic step generation based on runtime data (fan-out patterns require workarounds)","YAML syntax errors require manual debugging; no schema validation IDE integration by default"],"requires":["YAML file with valid DAG structure (no cycles)","Dagu binary (single Go executable, no runtime dependencies)","Shell or scripting environment for step commands"],"input_types":["YAML configuration files","Environment variables","Command-line arguments"],"output_types":["Execution logs","Exit codes","Structured execution history (JSON/database)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dagucloud--dagu__cap_1","uri":"capability://automation.workflow.single.binary.distributed.execution.with.local.and.remote.task.scheduling","name":"single-binary distributed execution with local and remote task scheduling","description":"Dagu compiles to a single Go binary that can run standalone on a laptop or scale to a distributed cluster by spawning worker processes or connecting to remote nodes. The engine uses a local scheduler for single-machine execution and supports remote task execution via SSH or custom executors. This architecture eliminates the need for separate control planes, message brokers, or container orchestration — the same binary handles both local cron-like scheduling and distributed task dispatch.","intents":["Run workflows on a single machine without installing additional services or databases","Scale from laptop to multi-node cluster by adding remote executors without changing workflow definitions","Execute tasks on remote servers via SSH without deploying agents or sidecars","Avoid vendor lock-in by using a self-contained, portable binary"],"best_for":["Teams deploying to air-gapped or on-premises infrastructure","Startups avoiding cloud costs and complexity","DevOps engineers managing heterogeneous infrastructure (bare metal, VMs, Kubernetes)","Organizations requiring full control over execution environment"],"limitations":["No built-in high-availability or automatic failover — single scheduler instance is a potential bottleneck","Remote execution via SSH requires pre-configured SSH keys and network connectivity","No native Kubernetes integration — requires custom executors or sidecar patterns for K8s deployment","Scaling to hundreds of concurrent tasks may require tuning goroutine limits and file descriptor limits"],"requires":["Dagu binary (Go 1.16+ for compilation, or pre-built executable)","SSH access to remote nodes (for remote execution)","Unix-like OS (Linux, macOS) or Windows with WSL for shell command execution"],"input_types":["YAML workflow definitions","SSH credentials or key files","Environment variables for task configuration"],"output_types":["Task execution logs (stdout/stderr)","Exit codes and status updates","Execution history and metrics"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dagucloud--dagu__cap_10","uri":"capability://automation.workflow.workflow.dependency.management.and.task.ordering","name":"workflow dependency management and task ordering","description":"Dagu enforces task ordering through explicit dependency declarations in YAML — each task specifies which tasks it depends on, creating a directed acyclic graph (DAG) of execution order. The engine validates dependencies at parse time, detects cycles, and builds an execution plan that respects the DAG. This ensures tasks run in the correct order without race conditions, and enables parallel execution of independent tasks.","intents":["Ensure tasks run in the correct order based on explicit dependencies","Parallelize independent tasks to reduce overall workflow execution time","Detect circular dependencies at parse time before execution","Visualize task dependencies and execution order in the web UI"],"best_for":["Complex workflows with many interdependent tasks","Data pipelines requiring strict ordering (e.g., extract before transform before load)","CI/CD pipelines with multiple parallel stages (build, test, deploy)","Teams needing to understand and visualize task dependencies"],"limitations":["Dependencies are static — cannot be determined dynamically based on runtime conditions","No support for soft dependencies or optional ordering — all dependencies are hard constraints","Parallel execution is limited by the number of available CPU cores and I/O bandwidth","No built-in load balancing or task scheduling across multiple workers"],"requires":["YAML workflow definition with explicit task dependencies (depends_on field)","Valid DAG structure (no cycles)","Understanding of task dependency semantics"],"input_types":["YAML task definitions with depends_on specifications","Task names and identifiers"],"output_types":["Execution plan (topological sort of tasks)","DAG visualization in web UI","Execution order and parallelization strategy"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dagucloud--dagu__cap_11","uri":"capability://automation.workflow.workflow.templating.and.reusable.step.definitions","name":"workflow templating and reusable step definitions","description":"Dagu supports defining reusable step templates that can be instantiated multiple times in a workflow with different parameters. Templates encapsulate common task patterns (e.g., 'run a Docker container', 'call an API', 'execute a script') and can be parameterized to avoid duplication. This enables DRY (Don't Repeat Yourself) workflow definitions where common patterns are defined once and reused across multiple workflows.","intents":["Define reusable task templates to avoid duplicating common patterns","Parameterize templates to adapt them to different use cases without modifying the template","Share task templates across multiple workflows in a team or organization","Reduce YAML boilerplate by extracting common task configurations"],"best_for":["Teams managing many similar workflows with common patterns","Organizations building a library of reusable workflow components","DevOps teams standardizing task execution patterns (e.g., Docker image deployment)","Data teams with repetitive ETL or data quality check tasks"],"limitations":["Template inheritance is limited — no multi-level template composition","No built-in template marketplace or discovery mechanism","Template parameters are string-based — no type validation or schema enforcement","Debugging template instantiation requires understanding both the template and the parameters"],"requires":["YAML workflow definition with template definitions","Template syntax and parameter placeholders","Understanding of template instantiation and parameter substitution"],"input_types":["Template definitions (YAML with parameter placeholders)","Template parameters (values to substitute into the template)","Workflow definitions that reference templates"],"output_types":["Instantiated task definitions (templates with parameters substituted)","Expanded YAML workflow definition","Execution logs showing which template was used"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dagucloud--dagu__cap_12","uri":"capability://automation.workflow.graceful.shutdown.and.signal.handling.for.long.running.workflows","name":"graceful shutdown and signal handling for long-running workflows","description":"Dagu implements signal handling (SIGTERM, SIGINT) to gracefully shut down running workflows and tasks. When a shutdown signal is received, the engine attempts to stop currently executing tasks cleanly (allowing them to finish or respond to signals) rather than forcefully killing them. This enables safe workflow interruption without data corruption or orphaned processes, and supports deployment scenarios where the Dagu daemon needs to be restarted or updated.","intents":["Gracefully stop running workflows without killing tasks abruptly","Allow tasks to clean up resources (close files, commit transactions) before shutdown","Support rolling updates or restarts of the Dagu daemon without losing workflow state","Implement safe deployment practices where the daemon can be stopped and restarted"],"best_for":["Production deployments where workflows must be stopped gracefully","Long-running data pipelines that need to checkpoint and resume","Teams using container orchestration (Docker, Kubernetes) with graceful shutdown requirements","Mission-critical workflows requiring data consistency during restarts"],"limitations":["Graceful shutdown depends on tasks responding to signals — tasks that ignore signals will be forcefully killed","No built-in timeout for graceful shutdown — tasks may hang indefinitely","Workflow state is not automatically persisted during shutdown — requires external state store for recovery","No built-in coordination with external systems (databases, message queues) during shutdown"],"requires":["Dagu binary with signal handling enabled","Tasks that implement signal handling (SIGTERM, SIGINT) for graceful shutdown","Container or process manager that sends signals (Docker, systemd, Kubernetes)"],"input_types":["OS signals (SIGTERM, SIGINT)","Shutdown timeout configuration","Task signal handling implementation"],"output_types":["Graceful shutdown logs","Task exit codes indicating shutdown","Workflow state at shutdown time"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dagucloud--dagu__cap_2","uri":"capability://automation.workflow.durable.execution.with.automatic.retry.and.failure.recovery","name":"durable execution with automatic retry and failure recovery","description":"Dagu tracks task execution state (pending, running, success, failure) and persists this state to enable automatic retries, resume-on-failure, and idempotent re-execution. When a task fails, the engine can automatically retry with exponential backoff or skip to the next step based on configured policies. Failed workflows can be resumed from the point of failure without re-executing completed steps, enabling long-running pipelines to recover from transient failures without manual intervention.","intents":["Automatically retry failed tasks with exponential backoff without manual intervention","Resume failed workflows from the last failed step instead of restarting from the beginning","Implement idempotent task execution to safely retry without side effects","Handle transient failures (network timeouts, temporary service unavailability) gracefully"],"best_for":["Data pipelines with long-running jobs that may experience transient failures","AI agent workflows requiring resilience to API rate limits or temporary service outages","CI/CD pipelines needing automatic recovery from flaky tests or network issues","Mission-critical workflows running on unreliable infrastructure"],"limitations":["Retry logic is task-level only — no workflow-level rollback or compensation logic","State persistence requires local filesystem or external storage — no built-in distributed consensus","Idempotency is the responsibility of the task implementation — engine does not enforce idempotent semantics","No circuit breaker pattern — repeated failures to the same service will continue retrying until max attempts"],"requires":["Dagu binary with state persistence enabled (local filesystem or external store)","Tasks implemented with idempotent semantics (same input produces same output)","Retry policy configuration in YAML (max retries, backoff strategy)"],"input_types":["YAML workflow definitions with retry policies","Task execution state (from previous runs)","Environment variables and task parameters"],"output_types":["Execution history with retry attempts","State snapshots for resume capability","Failure logs and recovery metadata"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dagucloud--dagu__cap_3","uri":"capability://automation.workflow.cron.like.scheduling.with.time.based.and.event.based.triggers","name":"cron-like scheduling with time-based and event-based triggers","description":"Dagu embeds a cron scheduler that interprets standard cron expressions (minute, hour, day, month, day-of-week) to trigger workflows on a schedule. The scheduler runs as part of the Dagu daemon and can trigger workflows based on wall-clock time or custom events. This eliminates the need for external cron daemons or scheduling services — the workflow engine itself handles scheduling, making it suitable for air-gapped environments where external services are unavailable.","intents":["Schedule workflows to run at specific times (hourly, daily, weekly) using standard cron syntax","Replace traditional cron jobs with observable, auditable workflow schedules","Trigger workflows based on time windows or custom events without external schedulers","Monitor scheduled workflow execution and failures from a single interface"],"best_for":["Teams replacing legacy cron jobs with observable workflows","Data pipelines requiring regular execution (daily ETL, hourly metrics collection)","Backup and maintenance tasks scheduled on a regular cadence","Organizations avoiding external scheduling services (Kubernetes CronJob, AWS EventBridge)"],"limitations":["Cron expressions are limited to standard 5-field format — no second-level precision","Scheduling is local to the Dagu instance — no distributed scheduling across multiple nodes","No built-in timezone support — schedules are relative to the system timezone","Event-based triggers require custom implementation — no native pub/sub integration"],"requires":["Dagu daemon running continuously (systemd service, Docker container, or manual process)","Valid cron expression in YAML workflow definition","System timezone configured correctly on the host"],"input_types":["Cron expression (5-field format: minute hour day month day-of-week)","YAML workflow definition with schedule section"],"output_types":["Scheduled execution timestamps","Trigger logs and execution history","Missed execution alerts (if daemon was down)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dagucloud--dagu__cap_4","uri":"capability://automation.workflow.web.ui.and.rest.api.for.workflow.monitoring.and.control","name":"web ui and rest api for workflow monitoring and control","description":"Dagu exposes a web dashboard and REST API that provide real-time visibility into workflow execution, task status, logs, and history. The UI displays DAG visualizations, execution timelines, and task output; the API enables programmatic workflow triggering, status queries, and log retrieval. This allows operators to monitor and control workflows without SSH access or command-line tools, and enables integration with external systems (Slack notifications, custom dashboards, alerting systems).","intents":["Monitor workflow execution status and task progress in real-time via web dashboard","Retrieve workflow logs and debug task failures through the web UI","Trigger workflows manually or programmatically via REST API","Integrate Dagu with external systems (Slack, PagerDuty, custom dashboards) via API webhooks"],"best_for":["Operations teams needing visibility into workflow execution without SSH access","DevOps engineers building custom dashboards or alerting systems","Teams integrating Dagu with existing monitoring and incident management tools","Non-technical stakeholders tracking data pipeline progress"],"limitations":["Web UI is read-only for most operations — workflow definitions cannot be edited through the UI","REST API does not support workflow definition updates — changes require YAML file edits","No built-in authentication or authorization — requires reverse proxy (nginx, Traefik) for security","Web UI performance may degrade with very large execution histories (millions of task runs)"],"requires":["Dagu binary with web server enabled (default port 8080)","Network access to the Dagu host (or reverse proxy for remote access)","HTTP client or browser for API/UI access"],"input_types":["HTTP requests (GET for status/logs, POST for triggering)","Query parameters for filtering and pagination","JSON payloads for workflow trigger parameters"],"output_types":["JSON responses (workflow status, task logs, execution history)","HTML web dashboard","Streaming logs via WebSocket or Server-Sent Events"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dagucloud--dagu__cap_5","uri":"capability://automation.workflow.task.level.environment.variable.and.parameter.injection","name":"task-level environment variable and parameter injection","description":"Dagu supports passing environment variables and parameters to individual tasks through YAML configuration, command-line arguments, or API calls. Variables can be defined globally (workflow-level), per-task, or dynamically from previous task outputs. The engine substitutes variables into task commands before execution, enabling parameterized workflows that adapt to different environments (dev, staging, prod) without modifying the YAML definition.","intents":["Pass environment-specific parameters (API keys, database URLs, feature flags) to tasks without hardcoding","Use output from one task as input to downstream tasks (variable chaining)","Run the same workflow definition in multiple environments (dev, staging, prod) with different parameters","Parameterize workflows to accept user input at trigger time"],"best_for":["Teams managing multiple environments (dev, staging, prod) with a single workflow definition","Data pipelines requiring dynamic input parameters at runtime","AI agent workflows needing to pass context or configuration between steps","CI/CD pipelines with environment-specific build or deployment parameters"],"limitations":["Variable substitution is string-based — no type coercion or validation","Output capture from tasks requires explicit parsing — no structured output format (JSON) by default","No built-in secret management — sensitive values (API keys, passwords) must be injected via environment or external secret store","Variable scope is limited to task-level and workflow-level — no conditional variable assignment based on task output"],"requires":["YAML workflow definition with variable placeholders (${VAR_NAME} syntax)","Environment variables set in the shell or Dagu configuration","Task commands that accept environment variables or command-line arguments"],"input_types":["YAML variable definitions","Environment variables from shell or .env files","Command-line arguments passed to dagu trigger","API request parameters"],"output_types":["Substituted task commands with variables resolved","Task output captured as variables for downstream tasks","Execution logs showing variable values"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dagucloud--dagu__cap_6","uri":"capability://automation.workflow.conditional.task.execution.and.branching.logic","name":"conditional task execution and branching logic","description":"Dagu supports conditional execution of tasks based on the exit code or output of previous tasks. Tasks can be marked as optional (continue on failure), skipped based on conditions, or executed only if upstream tasks succeed. This enables branching workflows where different paths are taken based on runtime conditions, without requiring explicit if-then-else constructs — the DAG structure itself encodes the branching logic through task dependencies and conditions.","intents":["Skip tasks based on conditions (e.g., only run deployment if tests pass)","Continue workflow execution even if optional tasks fail","Implement branching workflows with different execution paths based on upstream results","Handle error cases gracefully without stopping the entire workflow"],"best_for":["CI/CD pipelines with conditional deployment or testing steps","Data pipelines with optional data quality checks or transformations","AI agent workflows with fallback paths or error handling","Workflows requiring graceful degradation when optional services are unavailable"],"limitations":["Conditional logic is limited to exit codes and task success/failure — no complex boolean expressions","No support for dynamic branching based on task output — branching must be pre-defined in the DAG","Skipped tasks are not re-executed if conditions change — workflow must be re-triggered","No built-in support for loops or fan-out patterns — requires workarounds or custom scripts"],"requires":["YAML workflow definition with condition syntax (e.g., depends_on, condition fields)","Tasks that exit with meaningful exit codes (0 for success, non-zero for failure)","Understanding of DAG structure and task dependency semantics"],"input_types":["YAML condition definitions","Task exit codes from upstream tasks","Task output for condition evaluation"],"output_types":["Execution path taken (which tasks were executed vs skipped)","Conditional execution logs","Final workflow status (success/failure based on critical path)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dagucloud--dagu__cap_7","uri":"capability://automation.workflow.task.output.capture.and.inter.task.communication","name":"task output capture and inter-task communication","description":"Dagu captures stdout/stderr from task execution and makes it available to downstream tasks through variable substitution or API queries. Tasks can write structured output (JSON, key=value pairs) that is parsed and injected as environment variables for subsequent tasks. This enables data flow through the workflow — one task produces output that becomes input to the next task, without requiring external message queues or databases.","intents":["Pass data from one task to downstream tasks without external storage","Capture task output and make it available for debugging and auditing","Implement data pipelines where each task transforms and passes data to the next step","Extract values from task output (e.g., API response IDs) for use in subsequent tasks"],"best_for":["Data transformation pipelines where each step processes and passes data","AI agent workflows where one agent's output becomes another agent's input","ETL pipelines requiring data flow between steps without external storage","Debugging workflows by capturing and inspecting intermediate outputs"],"limitations":["Output capture is string-based — no built-in JSON parsing or type coercion","Large outputs (>1MB) may cause performance issues or memory exhaustion","No built-in compression or streaming for large data transfers","Output is stored in memory or local filesystem — no distributed caching or deduplication"],"requires":["Tasks that write output to stdout in a parseable format (JSON, key=value, plain text)","YAML configuration to specify which output to capture and how to parse it","Downstream tasks that accept captured output as environment variables"],"input_types":["Task stdout/stderr","Structured output formats (JSON, key=value pairs, CSV)","Task exit codes and status"],"output_types":["Captured output as environment variables for downstream tasks","Execution logs with full task output","Structured data extracted from task output"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dagucloud--dagu__cap_8","uri":"capability://automation.workflow.workflow.execution.history.and.audit.logging","name":"workflow execution history and audit logging","description":"Dagu maintains a persistent execution history of all workflow runs, including task status, exit codes, start/end times, and logs. This history is queryable via the REST API and web UI, enabling audit trails, performance analysis, and debugging. The engine stores execution metadata (who triggered the workflow, when, with what parameters) and task-level details (duration, resource usage if available), providing full observability into workflow behavior over time.","intents":["Audit who triggered which workflows and when for compliance and security","Debug workflow failures by reviewing execution history and task logs","Analyze workflow performance and identify bottlenecks (slow tasks, frequent failures)","Detect patterns in workflow execution (e.g., recurring failures at specific times)"],"best_for":["Organizations requiring audit trails for compliance (SOC 2, HIPAA, PCI-DSS)","DevOps teams debugging production workflow failures","Data engineers optimizing pipeline performance","Teams tracking SLA compliance for critical workflows"],"limitations":["History storage is local filesystem or external database — no built-in distributed storage","No automatic log rotation or retention policies — manual cleanup required","Query capabilities are limited to basic filtering (workflow name, date range, status)","No built-in integration with centralized logging systems (ELK, Splunk, Datadog)"],"requires":["Dagu binary with history persistence enabled","Local filesystem or external database for storing execution history","Sufficient disk space for long-term history retention"],"input_types":["Workflow execution events (start, task completion, failure)","Task logs and exit codes","Trigger metadata (user, timestamp, parameters)"],"output_types":["Execution history records (JSON/database format)","Task logs and stdout/stderr","Performance metrics (task duration, total workflow time)","Audit trail with trigger information"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dagucloud--dagu__cap_9","uri":"capability://automation.workflow.custom.executor.plugins.for.task.execution","name":"custom executor plugins for task execution","description":"Dagu supports custom executors that define how tasks are executed — beyond the default shell command execution. Executors can be implemented as external programs or plugins that receive task definitions and execute them in custom environments (Docker containers, Kubernetes pods, remote services, custom runtimes). This enables Dagu to orchestrate tasks across heterogeneous infrastructure without modifying the core engine or workflow definitions.","intents":["Execute tasks in Docker containers without modifying workflow definitions","Run tasks on Kubernetes pods for cloud-native deployments","Execute tasks on remote services (Lambda, Cloud Functions) via custom executors","Support custom runtimes or execution environments (Java, .NET, custom languages)"],"best_for":["Teams deploying Dagu to Kubernetes or containerized environments","Organizations using multiple execution environments (bare metal, VMs, containers, serverless)","DevOps engineers building custom orchestration layers on top of Dagu","Teams integrating Dagu with existing infrastructure (Jenkins, GitLab CI, custom platforms)"],"limitations":["Custom executors require implementation and maintenance — no built-in Kubernetes executor","Executor interface is not standardized — each executor has its own configuration format","No built-in executor discovery or plugin marketplace — executors must be manually installed","Debugging custom executors requires understanding both Dagu and the executor implementation"],"requires":["Custom executor implementation (external program or plugin)","Executor configuration in YAML workflow definition","Understanding of executor interface and task definition format"],"input_types":["Task definitions (command, environment variables, parameters)","Executor configuration (image, resource limits, etc.)","Task input/output specifications"],"output_types":["Task execution results (exit code, stdout/stderr)","Executor-specific metadata (container ID, pod name, etc.)","Task logs from the executor"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":38,"verified":false,"data_access_risk":"high","permissions":["YAML file with valid DAG structure (no cycles)","Dagu binary (single Go executable, no runtime dependencies)","Shell or scripting environment for step commands","Dagu binary (Go 1.16+ for compilation, or pre-built executable)","SSH access to remote nodes (for remote execution)","Unix-like OS (Linux, macOS) or Windows with WSL for shell command execution","YAML workflow definition with explicit task dependencies (depends_on field)","Valid DAG structure (no cycles)","Understanding of task dependency semantics","YAML workflow definition with template definitions"],"failure_modes":["No built-in UI for visual workflow design — YAML editing required","DAG validation happens at parse time; runtime dependency injection not supported","No native support for dynamic step generation based on runtime data (fan-out patterns require workarounds)","YAML syntax errors require manual debugging; no schema validation IDE integration by default","No built-in high-availability or automatic failover — single scheduler instance is a potential bottleneck","Remote execution via SSH requires pre-configured SSH keys and network connectivity","No native Kubernetes integration — requires custom executors or sidecar patterns for K8s deployment","Scaling to hundreds of concurrent tasks may require tuning goroutine limits and file descriptor limits","Dependencies are static — cannot be determined dynamically based on runtime conditions","No support for soft dependencies or optional ordering — all dependencies are hard constraints","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.2935847408614679,"quality":0.5,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.2,"quality":0.25,"ecosystem":0.1,"match_graph":0.4,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-05-03T13:59:55.148Z","last_commit":"2026-05-03T11:25:45Z"},"community":{"stars":3353,"forks":260,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=dagucloud--dagu","compare_url":"https://unfragile.ai/compare?artifact=dagucloud--dagu"}},"signature":"YGkoYXuEKkJP0BRCIAwRNG14zss47DLcNNFYv8iJFAeyZ8dF+95hJuE/QrlbQfvD18GJTUwoFhCYkljgWaLkAA==","signedAt":"2026-06-21T15:51:16.660Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/dagucloud--dagu","artifact":"https://unfragile.ai/dagucloud--dagu","verify":"https://unfragile.ai/api/v1/verify?slug=dagucloud--dagu","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"}}