{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"trigger-dev","slug":"trigger-dev","name":"Trigger.dev","type":"framework","url":"https://github.com/triggerdotdev/trigger.dev","page_url":"https://unfragile.ai/trigger-dev","categories":["automation"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"trigger-dev__cap_0","uri":"capability://code.generation.editing.typescript.task.definition.with.type.safe.scheduling","name":"typescript task definition with type-safe scheduling","description":"Enables developers to define long-running background tasks as TypeScript functions with built-in type safety, automatic serialization, and declarative scheduling (cron, delays, intervals). Uses a decorator-based or function-wrapper pattern that captures task metadata at definition time and integrates with the SDK's task registry, allowing IDE autocomplete and compile-time validation of task parameters and return types.","intents":["Define a background job that processes AI model outputs without blocking the main request","Create a scheduled task that runs every hour to batch-process user data","Build a type-safe task queue where parameters are validated at compile time"],"best_for":["TypeScript/Node.js developers building AI agents and batch processors","Teams wanting compile-time safety for async task definitions","Developers migrating from untyped job queues like Bull or RabbitMQ"],"limitations":["TypeScript-only — no Python, Go, or other language SDKs","Task definitions must be serializable (no closures or non-JSON-serializable state)","Circular dependencies in task parameters can cause serialization failures"],"requires":["Node.js 18+","TypeScript 4.7+","Trigger.dev SDK installed (@trigger.dev/sdk)","API key for Trigger.dev cloud or self-hosted instance"],"input_types":["TypeScript function signatures","JSON-serializable parameters","Cron expressions (string format)","ISO 8601 delay specifications"],"output_types":["Task metadata (name, id, retry policy)","Scheduled execution records","Type definitions for task invocation"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trigger-dev__cap_1","uri":"capability://automation.workflow.distributed.task.execution.with.automatic.retry.and.exponential.backoff","name":"distributed task execution with automatic retry and exponential backoff","description":"Executes tasks across distributed workers with built-in retry logic, exponential backoff, and configurable failure policies. The Run Engine (internal-packages/run-engine) implements a state machine that tracks task execution attempts, persists retry state to the database, and coordinates dequeue/execution flow across multiple worker instances using distributed locking and queue management systems to prevent duplicate execution.","intents":["Automatically retry a failed API call to an LLM provider with exponential backoff","Ensure a task runs at least once even if a worker crashes mid-execution","Configure different retry policies for different task types (e.g., 3 retries for API calls, 10 for batch jobs)"],"best_for":["Teams building resilient AI workflows that call external APIs","Batch processing systems requiring fault tolerance","Developers needing fine-grained control over retry behavior per task"],"limitations":["Retry state is stored in database — high-frequency retries (>100/sec) may cause database contention","Exponential backoff is capped at a maximum interval (default 1 hour) — very long-running tasks may not retry optimally","No built-in circuit breaker pattern — cascading failures to downstream services are not automatically prevented"],"requires":["PostgreSQL or compatible database (Prisma ORM)","Redis for distributed locking and queue coordination","Worker instances with network access to task execution environment"],"input_types":["Task execution context (task ID, run ID, attempt number)","Retry policy configuration (max attempts, backoff multiplier, max delay)","Error objects from failed task execution"],"output_types":["Execution attempt records with timestamps and error logs","Retry scheduling events","Final success/failure status after all retries exhausted"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trigger-dev__cap_10","uri":"capability://automation.workflow.ttl.based.automatic.run.expiration.and.cleanup","name":"ttl-based automatic run expiration and cleanup","description":"Automatically expires and cleans up old task execution records based on configurable TTL (time-to-live) policies. Implemented via the ttlSystem in the Run Engine, which periodically scans for expired runs and deletes their associated records (logs, snapshots, attempts) from the database. Prevents unbounded database growth while maintaining configurable retention periods for compliance or debugging.","intents":["Automatically delete task execution logs older than 30 days to manage database size","Retain execution records for 1 year for compliance auditing while deleting older records","Configure different retention periods for different task types (short-lived vs long-running)"],"best_for":["Teams managing large-scale task execution with high volume","Organizations with data retention compliance requirements","Developers wanting automatic cleanup without manual intervention"],"limitations":["TTL cleanup is batch-based and may cause database load spikes during cleanup windows","Expired runs cannot be recovered — ensure retention period is sufficient for debugging needs","TTL is global per environment — per-task-type retention requires custom logic"],"requires":["PostgreSQL database with sufficient disk space for retention period","Background cleanup process running periodically","TTL configuration per environment"],"input_types":["TTL duration (days, hours, seconds)","Cleanup schedule (cron expression)","Retention policy per run status (success, failure, timeout)"],"output_types":["Cleanup execution records","Deleted run counts and freed disk space","Cleanup errors or warnings"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trigger-dev__cap_11","uri":"capability://code.generation.editing.sdk.based.task.invocation.with.type.safe.parameters","name":"sdk-based task invocation with type-safe parameters","description":"Provides a type-safe SDK for invoking tasks from application code, with automatic parameter serialization and validation. The SDK generates type definitions from task definitions, enabling IDE autocomplete and compile-time type checking when invoking tasks. Supports both immediate invocation and scheduled invocation with delay/cron parameters.","intents":["Invoke a task from a Next.js API route with full type safety for parameters","Schedule a task to run in 5 minutes with IDE autocomplete for all parameters","Get back a task handle/promise that can be used to poll for execution status"],"best_for":["TypeScript/Node.js application developers invoking background tasks","Teams wanting compile-time safety for task invocation","Developers building AI applications that spawn background jobs"],"limitations":["SDK is TypeScript-only — non-TypeScript applications must use REST API","Type generation requires task definitions to be in the same codebase or imported","Circular task dependencies (task A invokes task B which invokes task A) are not prevented at compile time"],"requires":["Trigger.dev SDK installed (@trigger.dev/sdk)","API key for Trigger.dev instance","Task definitions imported or available in the application codebase"],"input_types":["Task name (string or imported task reference)","Task parameters (typed)","Scheduling options (delay, cron, timezone)"],"output_types":["Task handle/run ID for status polling","Immediate execution result (if awaited)","Scheduled execution confirmation"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trigger-dev__cap_12","uri":"capability://automation.workflow.lifecycle.hooks.for.task.initialization.and.cleanup","name":"lifecycle hooks for task initialization and cleanup","description":"Supports lifecycle hooks (onStart, onSuccess, onFailure, onCompletion) that execute at specific points in a task's execution lifecycle. Hooks are defined at task definition time and executed by the Run Engine with access to task context and execution results. Enables common patterns like resource cleanup, notification sending, and metrics recording without cluttering task code.","intents":["Send a Slack notification when a task fails after all retries are exhausted","Clean up temporary files or database connections when a task completes","Record task execution metrics to a monitoring system on success or failure"],"best_for":["Teams wanting to separate cross-cutting concerns (logging, notifications) from task logic","Developers implementing common patterns (cleanup, notifications) across many tasks","Organizations with standardized task execution workflows"],"limitations":["Hooks are executed synchronously — long-running hooks block task completion","Hook failures do not prevent task completion — errors are logged but not propagated","Hooks have access to task context but not to external state — complex cleanup logic must be in task code"],"requires":["Task definitions with lifecycle hook implementations","Access to external services from hook code (e.g., Slack API, monitoring service)"],"input_types":["Hook type (onStart, onSuccess, onFailure, onCompletion)","Task context (task name, parameters, execution result)","Error objects (for failure hooks)"],"output_types":["Hook execution logs","Side effects (notifications, metrics, cleanup actions)","Hook error records"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trigger-dev__cap_13","uri":"capability://code.generation.editing.build.extensions.for.custom.task.bundling.and.compilation","name":"build extensions for custom task bundling and compilation","description":"Supports build extensions that customize how tasks are bundled and compiled before deployment. Extensions can modify the build process (e.g., add custom webpack loaders, tree-shake dependencies, inline assets) and are executed during the build phase before workers are deployed. Enables optimization for specific use cases (e.g., minimizing bundle size for Lambda, including native modules for GPU tasks).","intents":["Optimize task bundle size by tree-shaking unused dependencies before deployment","Include native modules (e.g., CUDA libraries) in the worker image for GPU-accelerated tasks","Inline large static assets into the bundle to avoid external file dependencies"],"best_for":["Teams optimizing task deployment for size or performance constraints","Developers building specialized task types (GPU, native code)","Organizations with custom build requirements"],"limitations":["Build extensions require understanding the build system (Turborepo, webpack) — complex extensions are difficult to debug","Build time increases with extension complexity — slow extensions can delay task deployment","Extensions are environment-specific — different extensions for Docker vs Kubernetes require careful configuration"],"requires":["Build extension implementation (custom code)","Understanding of the Trigger.dev build system","Configuration to enable/disable extensions per environment"],"input_types":["Build configuration (webpack config, bundler options)","Task source code","Environment-specific build parameters"],"output_types":["Optimized task bundle","Build logs and warnings","Deployment-ready artifacts"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trigger-dev__cap_14","uri":"capability://data.processing.analysis.clickhouse.based.analytics.and.query.performance.monitoring","name":"clickhouse-based analytics and query performance monitoring","description":"Trigger.dev uses ClickHouse as an analytics database to store and query task execution metrics, enabling high-performance analytics on large volumes of task data. The system exports task execution events (duration, status, retry count, etc.) to ClickHouse, allowing users to query execution patterns, identify performance bottlenecks, and generate reports. ClickHouse's columnar storage and compression enable efficient queries over billions of task runs.","intents":["Query task execution metrics (average duration, success rate, retry rate) across time periods","Identify slow tasks or tasks with high failure rates","Generate reports on task performance and resource utilization"],"best_for":["High-volume task systems processing millions of runs per day","Teams requiring detailed analytics on task execution patterns","Organizations with compliance requirements for audit trails"],"limitations":["ClickHouse is append-only — updates and deletes are expensive, limiting real-time metric corrections","Analytics queries have latency (seconds to minutes) — not suitable for real-time dashboards","ClickHouse requires separate infrastructure and operational overhead","Data export to ClickHouse adds latency (~100-500ms per task run) and network overhead"],"requires":["ClickHouse instance (self-hosted or managed service)","Network connectivity from Trigger.dev coordinator to ClickHouse","ClickHouse schema for task execution events"],"input_types":["Task execution events (duration, status, retry count, etc.)","SQL queries for analytics"],"output_types":["Query results (JSON or CSV format)","Aggregated metrics (average duration, success rate, etc.)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trigger-dev__cap_2","uri":"capability://automation.workflow.checkpoint.and.resume.execution.for.long.running.tasks","name":"checkpoint and resume execution for long-running tasks","description":"Allows tasks to pause execution at checkpoints, persist their state to the database, and resume from that exact point without re-executing prior steps. Implemented via the checkpointSystem in the Run Engine, which serializes task execution context (variables, call stack state) into execution snapshots stored in the database, enabling tasks to survive worker crashes or be paused indefinitely and resumed later.","intents":["Pause an AI model training job, save progress, and resume it days later on a different worker","Break a long-running batch process into resumable chunks to avoid timeouts","Implement human-in-the-loop workflows where a task waits for manual approval before continuing"],"best_for":["AI/ML teams running long-duration training or inference jobs","Batch processing systems with strict timeout constraints","Workflows requiring human approval or external input mid-execution"],"limitations":["Checkpoint serialization overhead — each checkpoint adds ~50-200ms depending on state size","State must be JSON-serializable — complex objects (e.g., file handles, database connections) cannot be checkpointed","Resuming from checkpoint requires the same task code version — breaking changes to task logic may cause resume failures"],"requires":["PostgreSQL database with sufficient storage for execution snapshots","Task code must explicitly call checkpoint/waitpoint APIs","Worker instances must have access to persisted snapshot data"],"input_types":["Task execution state (variables, intermediate results)","Checkpoint markers (explicit pause points in task code)","Waitpoint conditions (external event triggers for resume)"],"output_types":["Execution snapshots (serialized state blobs)","Checkpoint metadata (timestamp, sequence number)","Resume events with restored execution context"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trigger-dev__cap_3","uri":"capability://automation.workflow.waitpoint.system.for.external.event.coordination","name":"waitpoint system for external event coordination","description":"Enables tasks to pause execution and wait for external events (webhooks, API callbacks, manual triggers) before resuming. The waitpointSystem in the Run Engine registers wait conditions, stores them in the database, and resumes task execution when matching events arrive. Integrates with the checkpoint system to persist task state across the wait period, supporting both timeout-based expiration and event-driven resumption.","intents":["Pause a task while waiting for a webhook callback from a third-party service","Implement a workflow that waits for a user to approve a decision before proceeding","Create a task that waits for an external API to complete processing before continuing"],"best_for":["Developers building event-driven workflows with external dependencies","Teams implementing approval-based processes","AI systems that need to wait for external tool execution results"],"limitations":["Waitpoint timeout is configurable but adds database polling overhead if events are delayed","Event matching is based on simple key-value conditions — complex event correlation requires custom logic","No built-in deduplication — duplicate webhook events may trigger multiple resumptions"],"requires":["PostgreSQL database for storing wait conditions","Webhook endpoint or event delivery mechanism to trigger resumption","Task code must explicitly define waitpoint conditions"],"input_types":["Waitpoint condition definitions (event type, matching criteria)","External event payloads (webhook data, API responses)","Timeout specifications (ISO 8601 duration or seconds)"],"output_types":["Wait condition records in database","Event matching results","Resumed task execution with event data injected into context"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trigger-dev__cap_4","uri":"capability://automation.workflow.real.time.task.execution.monitoring.and.logging","name":"real-time task execution monitoring and logging","description":"Provides real-time visibility into task execution via a web dashboard that streams execution logs, status updates, and performance metrics. Integrates OpenTelemetry for distributed tracing, uses WebSocket-based realtime updates to push execution events to connected clients, and stores execution history in ClickHouse for analytics and historical querying. The webapp (apps/webapp) implements the UI layer with Remix, while the backend streams execution data via the realtime system.","intents":["Watch a long-running AI model inference task execute in real-time with live logs","Debug a failed task by reviewing its complete execution trace and error logs","Analyze task performance metrics (duration, retry count, resource usage) across historical runs"],"best_for":["DevOps teams monitoring production task execution","Developers debugging complex multi-step workflows","Teams analyzing task performance and failure patterns"],"limitations":["Real-time updates require WebSocket connection — high-latency networks may see delayed log streaming","ClickHouse analytics queries have eventual consistency — very recent executions may not appear in analytics immediately","OpenTelemetry instrumentation adds ~5-10% overhead to task execution time"],"requires":["Web browser with WebSocket support","ClickHouse instance for analytics storage","Redis for realtime event distribution","OpenTelemetry SDK integrated into task code (optional but recommended)"],"input_types":["Task execution events (start, checkpoint, completion)","Log messages from task code","OpenTelemetry spans and metrics","Error objects and stack traces"],"output_types":["Real-time log streams (text)","Execution status timeline (structured events)","Performance metrics (duration, memory, CPU)","Historical analytics queries (aggregated data)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trigger-dev__cap_5","uri":"capability://automation.workflow.cron.based.and.delayed.task.scheduling","name":"cron-based and delayed task scheduling","description":"Supports declarative scheduling of tasks via cron expressions and delay specifications, with automatic timezone handling and daylight saving time awareness. The scheduler integrates with the delayedRunSystem in the Run Engine, which stores scheduled runs in the database and uses a background process to enqueue them at the appropriate time. Supports both one-time delays (e.g., 'run in 5 minutes') and recurring schedules (e.g., 'every Monday at 9am').","intents":["Schedule a batch processing job to run every night at 2am in the user's timezone","Delay a task execution by a specific duration (e.g., retry after 30 seconds)","Create a recurring task that runs on a specific day/time with timezone awareness"],"best_for":["Teams building scheduled batch jobs and maintenance tasks","Developers needing timezone-aware scheduling for global applications","AI systems that need to run inference jobs on a fixed schedule"],"limitations":["Cron expressions are limited to standard 5-field format — complex schedules (e.g., 'every 2 weeks') require custom logic","Timezone changes (DST transitions) may cause missed or duplicate executions if not handled carefully","Scheduling granularity is limited to seconds — sub-second scheduling is not supported"],"requires":["PostgreSQL database for storing scheduled run records","Background scheduler process running continuously","Valid cron expression or ISO 8601 duration string"],"input_types":["Cron expressions (e.g., '0 2 * * *' for 2am daily)","ISO 8601 durations (e.g., 'PT5M' for 5 minutes)","Timezone identifiers (IANA timezone database)","Task parameters for scheduled execution"],"output_types":["Scheduled run records with next execution time","Enqueued task executions at scheduled time","Execution history with actual vs scheduled timestamps"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trigger-dev__cap_6","uri":"capability://automation.workflow.concurrency.control.and.rate.limiting.per.task","name":"concurrency control and rate limiting per task","description":"Allows developers to configure maximum concurrent executions per task and rate limits (e.g., 'max 5 concurrent, max 100 per minute'). Implemented via the concurrency management system in the Run Engine, which uses distributed locking (Redis-based) to coordinate concurrent execution limits across multiple worker instances. Prevents resource exhaustion and API rate limit violations by queuing excess executions.","intents":["Limit concurrent calls to an external API that has rate limits (e.g., OpenAI API max 100 requests/min)","Prevent database connection pool exhaustion by limiting concurrent database-heavy tasks","Ensure a resource-intensive AI inference task runs with at most 3 concurrent instances"],"best_for":["Teams integrating with rate-limited external APIs","Developers managing resource-constrained environments","AI systems calling expensive inference APIs"],"limitations":["Distributed locking adds ~10-50ms latency per concurrency check","Rate limiting is approximate — burst traffic may temporarily exceed configured limits due to lock contention","No built-in priority queue — all queued executions are processed FIFO regardless of importance"],"requires":["Redis instance for distributed locking","Multiple worker instances (concurrency control is most useful in distributed setups)","Task configuration with concurrency/rate limit parameters"],"input_types":["Max concurrent execution count (integer)","Rate limit specifications (executions per time window)","Time window duration (seconds, minutes, hours)"],"output_types":["Concurrency lock acquisitions/releases","Queued execution records","Rate limit violation events"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trigger-dev__cap_7","uri":"capability://automation.workflow.provider.based.worker.deployment.docker.kubernetes","name":"provider-based worker deployment (docker, kubernetes)","description":"Abstracts worker deployment across multiple execution environments via a provider architecture. Supports Docker (docker-provider) and Kubernetes (kubernetes-provider) out of the box, with a pluggable provider interface allowing custom deployment targets. Each provider handles worker lifecycle (startup, health checks, graceful shutdown) and integrates with the coordinator to receive task assignments and report execution status.","intents":["Deploy task workers as Docker containers in a development environment","Scale task execution across a Kubernetes cluster with automatic pod provisioning","Implement custom worker deployment to a proprietary infrastructure (e.g., Lambda, Fargate)"],"best_for":["Teams deploying Trigger.dev in containerized environments","Kubernetes-native organizations wanting native cluster integration","Developers building custom deployment providers for specialized infrastructure"],"limitations":["Docker provider is single-host only — no built-in clustering or load balancing","Kubernetes provider requires cluster admin access for pod creation and monitoring","Custom provider development requires understanding the provider interface and coordinator protocol"],"requires":["Docker daemon (for Docker provider) or Kubernetes cluster (for Kubernetes provider)","Provider configuration (image registry, resource limits, namespace)","Network connectivity between coordinator and worker instances"],"input_types":["Provider configuration (type, credentials, resource specs)","Worker image specifications (Docker image URI, Kubernetes pod spec)","Task assignment messages from coordinator"],"output_types":["Worker instance lifecycle events (started, ready, terminated)","Task execution status updates","Resource utilization metrics"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trigger-dev__cap_8","uri":"capability://automation.workflow.batch.triggering.and.waiting.for.multiple.task.executions","name":"batch triggering and waiting for multiple task executions","description":"Enables triggering multiple task instances in a batch and waiting for all (or a subset) to complete before proceeding. Implemented via batch trigger APIs that create multiple run records atomically and the waitpoint system that can wait for batch completion conditions. Useful for fan-out/fan-in patterns where a parent task spawns many child tasks and waits for results.","intents":["Trigger 100 parallel image processing tasks and wait for all to complete before aggregating results","Fan out to multiple LLM API calls in parallel and wait for all responses before combining them","Implement a map-reduce pattern where a parent task distributes work and collects results"],"best_for":["Teams implementing fan-out/fan-in workflows","Batch processing systems with parallel sub-tasks","AI systems that need to parallelize inference across multiple inputs"],"limitations":["Batch size is limited by database transaction size — very large batches (>10k tasks) may fail","Waiting for batch completion requires polling or event-based coordination — no built-in timeout for partial completion","Result aggregation must be implemented in task code — no built-in result collection mechanism"],"requires":["PostgreSQL database with sufficient transaction capacity","Task code that explicitly calls batch trigger and wait APIs","Sufficient worker capacity to execute all batch tasks concurrently"],"input_types":["Batch task specifications (task name, parameters for each instance)","Batch completion conditions (all complete, N complete, timeout)","Result aggregation logic (custom code)"],"output_types":["Batch execution records with individual task IDs","Batch completion events","Aggregated results from child tasks"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trigger-dev__cap_9","uri":"capability://automation.workflow.environment.based.configuration.and.secrets.management","name":"environment-based configuration and secrets management","description":"Supports multiple environments (dev, staging, prod) with environment-specific configuration and secrets. Environment configuration is stored in the database and injected into task execution context at runtime. Integrates with the web application's environment management UI for secure secret storage and rotation, with audit logging of configuration changes.","intents":["Use different API keys for development vs production without changing task code","Store sensitive credentials (database passwords, API keys) securely and rotate them without redeploying","Configure different task behavior per environment (e.g., batch size, retry policy)"],"best_for":["Teams managing multiple deployment environments","Organizations with security requirements for secrets management","Developers wanting environment-specific configuration without code changes"],"limitations":["Secrets are stored in database — encryption at rest is required for compliance (not built-in)","Environment switching requires redeploying or restarting workers — no hot-reload of configuration","Audit logging of configuration changes is basic — detailed compliance reporting requires external tools"],"requires":["PostgreSQL database for storing environment configuration","Web application access to manage environments and secrets","Task code that reads configuration from environment context"],"input_types":["Environment names (string identifiers)","Configuration key-value pairs","Secret values (encrypted storage)"],"output_types":["Environment-specific configuration injected into task context","Audit logs of configuration changes","Secret rotation events"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trigger-dev__headline","uri":"capability://automation.workflow.open.source.background.jobs.framework.for.typescript","name":"open-source background jobs framework for typescript","description":"Trigger.dev is an open-source framework designed for managing background jobs in TypeScript, providing features like retries, scheduling, and real-time logs specifically tailored for AI workloads and batch processing.","intents":["best background jobs framework","background jobs framework for AI workloads","open-source job scheduler for TypeScript","TypeScript background processing solution","real-time logging for background tasks"],"best_for":["AI workloads","batch processing"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Node.js 18+","TypeScript 4.7+","Trigger.dev SDK installed (@trigger.dev/sdk)","API key for Trigger.dev cloud or self-hosted instance","PostgreSQL or compatible database (Prisma ORM)","Redis for distributed locking and queue coordination","Worker instances with network access to task execution environment","PostgreSQL database with sufficient disk space for retention period","Background cleanup process running periodically","TTL configuration per environment"],"failure_modes":["TypeScript-only — no Python, Go, or other language SDKs","Task definitions must be serializable (no closures or non-JSON-serializable state)","Circular dependencies in task parameters can cause serialization failures","Retry state is stored in database — high-frequency retries (>100/sec) may cause database contention","Exponential backoff is capped at a maximum interval (default 1 hour) — very long-running tasks may not retry optimally","No built-in circuit breaker pattern — cascading failures to downstream services are not automatically prevented","TTL cleanup is batch-based and may cause database load spikes during cleanup windows","Expired runs cannot be recovered — ensure retention period is sufficient for debugging needs","TTL is global per environment — per-task-type retention requires custom logic","SDK is TypeScript-only — non-TypeScript applications must use REST API","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:05.297Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=trigger-dev","compare_url":"https://unfragile.ai/compare?artifact=trigger-dev"}},"signature":"xgnndN2j9S3q+zQ3nKtjlKQTK5wt3iJr0Jpz5ImBDeSYjlfalD1Fz4JbNUkW9Hwc/QCKbfeqAAOzMvgbO84fCQ==","signedAt":"2026-06-19T20:41:32.254Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/trigger-dev","artifact":"https://unfragile.ai/trigger-dev","verify":"https://unfragile.ai/api/v1/verify?slug=trigger-dev","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"}}