{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-prefect","slug":"pypi-prefect","name":"prefect","type":"workflow","url":"https://pypi.org/project/prefect/","page_url":"https://unfragile.ai/pypi-prefect","categories":["automation"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-prefect__cap_0","uri":"capability://automation.workflow.python.native.flow.and.task.definition.with.decorator.based.composition","name":"python-native flow and task definition with decorator-based composition","description":"Enables developers to define workflows as standard Python functions decorated with @flow and @task, converting imperative Python code into orchestrated DAGs without requiring domain-specific languages. The system uses Python's function introspection and async/await support to automatically capture task dependencies, parameter types, and return values, building an execution graph at definition time that can be serialized and deployed independently of the defining code.","intents":["Convert existing Python scripts into production workflows without rewriting business logic","Define complex multi-step data pipelines using familiar Python syntax and control flow","Create reusable task libraries that can be composed into different workflows"],"best_for":["Python developers migrating scripts to production workflows","Data engineers building ETL pipelines with existing Python codebases","Teams avoiding domain-specific workflow languages"],"limitations":["Requires Python 3.9+ for full async/await support","Dynamic control flow (if/for loops with runtime conditions) requires special handling via task mapping","Circular task dependencies are not detected until runtime"],"requires":["Python 3.9+","Prefect SDK installed via pip","Understanding of async/await patterns for advanced use cases"],"input_types":["Python function definitions","Primitive types (int, str, list, dict)","Custom serializable objects"],"output_types":["Execution DAG (directed acyclic graph)","Task state objects","Serialized flow definitions for deployment"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-prefect__cap_1","uri":"capability://automation.workflow.state.machine.based.task.and.flow.execution.with.automatic.retry.and.recovery","name":"state-machine-based task and flow execution with automatic retry and recovery","description":"Implements a deterministic state machine where each task and flow transitions through defined states (Pending → Running → Completed/Failed/Cancelled) with automatic persistence to a backend database. The execution engine tracks state transitions, captures timestamps and result metadata, and automatically applies retry logic with exponential backoff, timeout handling, and failure recovery based on configurable policies stored in the database as orchestration policies.","intents":["Automatically retry failed tasks with configurable backoff strategies","Resume workflows from failure points without re-executing completed tasks","Track execution history and debug failures through state transition logs","Implement timeout-based task cancellation and cleanup"],"best_for":["Production workflows requiring fault tolerance and automatic recovery","Teams needing detailed execution audit trails and debugging capabilities","Distributed systems where task failures are expected and must be handled gracefully"],"limitations":["State persistence adds ~50-200ms latency per state transition depending on database backend","Retry logic is task-level only; flow-level retries require manual implementation via parent task","State machine is not customizable; fixed set of states cannot be extended"],"requires":["Backend database (SQLite, PostgreSQL, or Prefect Cloud)","Network connectivity to state persistence layer","Proper exception handling in tasks to trigger retry logic"],"input_types":["Task execution context","Exception objects","Retry policy configuration"],"output_types":["State transition records","Execution metadata (duration, timestamps, result data)","Failure logs and stack traces"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-prefect__cap_10","uri":"capability://tool.use.integration.prefect.client.library.for.local.workflow.execution.and.server.interaction","name":"prefect client library for local workflow execution and server interaction","description":"Provides a Python client library that enables local workflow execution (without a server) and programmatic interaction with Prefect servers. The client handles flow and task execution, state management, and communication with the Prefect API. It supports both synchronous and asynchronous execution models and can be used in scripts, notebooks, or as a library. The client includes utilities for testing workflows locally before deployment and for querying server state from external applications.","intents":["Execute workflows locally for development and testing without a running server","Programmatically interact with Prefect servers from external applications","Test workflows in CI/CD pipelines before deployment","Query execution history and logs from custom scripts"],"best_for":["Developers testing workflows locally before deployment","CI/CD pipelines validating workflows before production deployment","External applications querying Prefect state and logs"],"limitations":["Local execution does not support distributed task execution; all tasks run in a single process","Local execution does not persist state to a database; state is lost if the process terminates","Client library requires Python 3.9+; no JavaScript/TypeScript client available","Async execution requires event loop management; blocking operations can deadlock"],"requires":["Python 3.9+","Prefect SDK installed","API key if connecting to Prefect Cloud"],"input_types":["Flow and task function definitions","Execution parameters","API credentials"],"output_types":["Execution results","State objects","Query results from server"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-prefect__cap_11","uri":"capability://automation.workflow.cli.command.interface.for.workflow.management.and.deployment","name":"cli command interface for workflow management and deployment","description":"Provides a comprehensive command-line interface for managing workflows, deployments, and server operations. The CLI supports commands for creating/updating deployments, running flows locally, querying execution history, managing blocks, and configuring Prefect settings. Commands are organized hierarchically (e.g., `prefect deployment create`, `prefect flow run`) and support both interactive and non-interactive modes. The CLI uses Typer for command definition and supports shell completion for common commands.","intents":["Deploy workflows from the command line without writing Python code","Run workflows locally for testing and debugging","Query execution history and logs from the terminal","Manage Prefect configuration and credentials via CLI"],"best_for":["DevOps engineers deploying workflows via CI/CD pipelines","Developers testing workflows locally from the terminal","Teams automating workflow management via shell scripts"],"limitations":["CLI output is text-based; complex queries require parsing or piping to external tools","Some advanced operations require API calls; CLI does not expose all API functionality","Shell completion is limited to common commands; custom commands are not auto-completed","Interactive mode requires terminal; non-interactive mode requires explicit flags"],"requires":["Prefect SDK installed","Python 3.9+","Shell environment (bash, zsh, PowerShell)"],"input_types":["Command-line arguments","Configuration files","Interactive prompts"],"output_types":["Text output","JSON output (with --json flag)","Exit codes"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-prefect__cap_12","uri":"capability://automation.workflow.react.based.web.dashboard.for.workflow.monitoring.and.management","name":"react-based web dashboard for workflow monitoring and management","description":"Provides a modern React-based web UI (v2) for monitoring workflow execution, managing deployments, and querying execution history. The dashboard displays real-time flow run status, task execution timelines, logs, and state transitions. It supports filtering and searching across flows, deployments, and runs, and provides interactive controls for pausing/resuming deployments and triggering manual flow runs. The UI communicates with the Prefect API and supports role-based access control.","intents":["Monitor workflow execution in real-time without accessing the terminal","Debug failed workflows by viewing logs and state transitions","Manage deployments and schedules via a graphical interface","Trigger manual workflow runs and view results"],"best_for":["Non-technical stakeholders monitoring workflow status","Operators debugging failed workflows","Teams requiring a centralized workflow management interface"],"limitations":["UI is read-only for some operations; complex configurations require API calls","Real-time updates rely on polling; latency can be 5-30 seconds","UI performance degrades with large numbers of flows/runs (>10,000)","Mobile support is limited; UI is optimized for desktop browsers"],"requires":["Prefect server or Prefect Cloud","Modern web browser (Chrome, Firefox, Safari, Edge)","Network connectivity to Prefect server"],"input_types":["User interactions (clicks, form submissions)","API responses from Prefect server"],"output_types":["Rendered UI components","API requests to Prefect server","Real-time updates via polling"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-prefect__cap_13","uri":"capability://automation.workflow.concurrency.management.and.task.rate.limiting","name":"concurrency management and task rate limiting","description":"Provides mechanisms to limit concurrent task execution and enforce rate limits on task runs. Concurrency limits are defined per-tag and are enforced globally across all workers, preventing more than a specified number of tagged tasks from running simultaneously. Rate limiting can be applied per-task or per-flow to control resource consumption. The system uses a distributed lock mechanism to enforce concurrency limits across multiple workers without requiring a centralized coordinator.","intents":["Prevent resource exhaustion by limiting concurrent task execution","Enforce rate limits on external API calls to avoid throttling","Prioritize critical tasks by allocating concurrency slots","Control database connection pool usage by limiting concurrent database tasks"],"best_for":["Workflows with resource-intensive tasks that cannot run in parallel","Integrations with rate-limited external APIs","Systems with limited database connections or other shared resources"],"limitations":["Concurrency limits are enforced globally; no per-worker limits","Lock acquisition adds latency (100-500ms) to task execution","Concurrency limits are not preemptive; tasks are queued but not cancelled","Rate limiting is task-level only; flow-level rate limiting requires manual implementation"],"requires":["Prefect server or Prefect Cloud","Task tags for concurrency limit application","Concurrency limit configuration via API or deployment settings"],"input_types":["Task tags","Concurrency limit values","Rate limit specifications"],"output_types":["Concurrency limit enforcement","Task queuing when limits are reached","Execution logs indicating concurrency limit waits"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-prefect__cap_2","uri":"capability://automation.workflow.distributed.task.execution.via.worker.pools.and.work.queues","name":"distributed task execution via worker pools and work queues","description":"Decouples task scheduling from execution by routing tasks to named work queues that are consumed by distributed workers running on heterogeneous infrastructure (local machines, Kubernetes, cloud VMs). Workers poll work queues via the Prefect API, pull task execution requests, execute them in isolated processes or containers, and report results back to the server, enabling horizontal scaling and infrastructure-agnostic task distribution without modifying workflow code.","intents":["Scale task execution across multiple machines without modifying workflow definitions","Route specific tasks to specialized infrastructure (GPU nodes, high-memory machines)","Run workflows on private infrastructure while managing them from Prefect Cloud","Isolate task execution environments to prevent resource contention"],"best_for":["Teams running compute-intensive workflows requiring horizontal scaling","Organizations with hybrid cloud/on-premises infrastructure","Workflows with heterogeneous resource requirements (some tasks need GPUs, others need high memory)"],"limitations":["Worker polling adds 1-5 second latency between task scheduling and execution start","Requires network connectivity between workers and Prefect server; no offline execution mode","Task serialization overhead for large data objects (>100MB) passed between tasks","Worker failure detection relies on heartbeat timeout (default 30 seconds)"],"requires":["Prefect server or Prefect Cloud account","Worker processes running on target infrastructure","Network connectivity from workers to Prefect API","Task code must be serializable (pickle-compatible or custom serialization)"],"input_types":["Task execution requests (serialized function calls)","Work queue names","Worker configuration (concurrency limits, tags)"],"output_types":["Task execution results","Worker heartbeat status","Execution logs from remote workers"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-prefect__cap_3","uri":"capability://automation.workflow.event.driven.workflow.triggering.and.automation.rules","name":"event-driven workflow triggering and automation rules","description":"Provides an event system where external systems (webhooks, cloud services, custom applications) emit events to Prefect, which are stored in a time-series database and matched against user-defined automation rules. Rules specify event filters (event type, source, attributes) and actions (trigger flow run, send notification, update deployment), enabling workflows to react to external state changes without polling or manual intervention. Events are queryable and can be used for debugging and audit purposes.","intents":["Trigger workflows automatically when external events occur (file uploads, database changes, API calls)","Create conditional automation rules that respond to multiple event types","Build event-driven data pipelines that react to real-time data sources","Debug workflow triggers by querying historical events and rule matches"],"best_for":["Event-driven architectures where workflows respond to external state changes","Real-time data pipelines triggered by upstream systems","Teams building reactive systems without custom webhook infrastructure"],"limitations":["Event matching is synchronous; complex rule evaluation can add 100-500ms latency","Events are stored with configurable retention (default 7 days); historical events are not queryable after expiration","Rule conditions are limited to event attribute matching; complex logic requires custom event handlers","Webhook delivery is not guaranteed; events can be lost if Prefect server is unavailable"],"requires":["Prefect server or Prefect Cloud","Event source integration (webhook, cloud provider SDK, custom client)","Automation rule definition via API or UI"],"input_types":["Event objects (type, source, timestamp, attributes)","Automation rule specifications (filters, actions)","Event query parameters"],"output_types":["Flow run triggers","Automation action results","Event query results with matching rules"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-prefect__cap_4","uri":"capability://automation.workflow.deployment.packaging.and.versioning.with.code.to.infrastructure.mapping","name":"deployment packaging and versioning with code-to-infrastructure mapping","description":"Packages flow code, dependencies, and configuration into versioned deployment artifacts that map to specific infrastructure (work pools, workers, environment variables). Deployments are created via CLI or API, stored in the Prefect database, and include metadata about code location, parameter defaults, schedule triggers, and execution environment. The deployment system supports multiple code storage backends (local filesystem, S3, GitHub) and can automatically pull code from version control before execution, enabling separation of flow definition from deployment configuration.","intents":["Package and version workflows for production deployment without manual infrastructure setup","Define default parameters and schedules for workflow execution","Store workflow code in version control and automatically pull it at execution time","Deploy the same workflow code to multiple environments with different configurations"],"best_for":["Teams deploying workflows to production with version control integration","Organizations requiring separation of code and infrastructure configuration","Workflows with environment-specific parameters (dev/staging/prod)"],"limitations":["Code storage backends require external configuration (S3 credentials, GitHub tokens)","Deployment updates require re-packaging; incremental updates are not supported","Parameter defaults are stored in the database; runtime parameter overrides require API calls","Code pulling from remote storage adds 5-30 second latency before task execution"],"requires":["Prefect server or Prefect Cloud","Code storage backend configured (local, S3, GitHub, etc.)","Deployment CLI or API access","Work pool configured for target infrastructure"],"input_types":["Flow function definition","Deployment configuration (name, work pool, schedule, parameters)","Code storage credentials"],"output_types":["Deployment object with metadata","Deployment version identifier","Code storage location reference"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-prefect__cap_5","uri":"capability://automation.workflow.scheduled.workflow.execution.with.cron.and.interval.based.triggers","name":"scheduled workflow execution with cron and interval-based triggers","description":"Enables workflows to run on fixed schedules defined via cron expressions or interval specifications (e.g., every 5 minutes, daily at 2 AM). Schedules are stored as deployment metadata and evaluated by a background scheduler service that creates flow run records at scheduled times. The scheduler supports timezone-aware scheduling, automatic daylight saving time handling, and can be paused/resumed without modifying deployment configuration. Schedule evaluation is deterministic and idempotent, preventing duplicate runs even if the scheduler restarts.","intents":["Run workflows on fixed schedules (daily, hourly, weekly) without external cron jobs","Define multiple schedules per deployment for different execution patterns","Pause and resume scheduled execution without modifying workflow code","Handle timezone-aware scheduling for globally distributed teams"],"best_for":["Batch data pipelines with fixed execution schedules","Periodic maintenance workflows (cleanup, reconciliation, reporting)","Teams avoiding external cron infrastructure"],"limitations":["Schedule evaluation is centralized in Prefect server; no distributed schedule evaluation","Cron expression parsing is limited to standard cron syntax; custom schedule logic requires event-driven triggers","Schedule drift can occur if scheduler service is unavailable; missed runs are not automatically backfilled","Timezone handling requires explicit configuration; UTC is the default"],"requires":["Prefect server or Prefect Cloud with scheduler service running","Deployment with schedule configuration","Valid cron expression or interval specification"],"input_types":["Cron expression (e.g., '0 2 * * *')","Interval specification (e.g., 'every 5 minutes')","Timezone identifier (e.g., 'America/New_York')"],"output_types":["Flow run records created at scheduled times","Schedule evaluation logs","Next scheduled run timestamp"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-prefect__cap_6","uri":"capability://automation.workflow.task.mapping.and.dynamic.parallelization.with.parameter.expansion","name":"task mapping and dynamic parallelization with parameter expansion","description":"Enables dynamic task parallelization by mapping a task over a collection of inputs, automatically creating multiple task runs with different parameters. Mapping is specified via the `.map()` method on task calls, which accepts iterables or upstream task results. The execution engine expands mapped tasks into individual runs that execute in parallel (subject to worker concurrency limits) and collects results into a list. Mapping supports nested structures and can be combined with conditional logic via task result filtering.","intents":["Process collections of items in parallel without explicit loop unrolling","Create dynamic task graphs based on runtime data (e.g., process each file in a directory)","Combine results from parallel task runs into downstream tasks","Scale task execution horizontally across multiple workers"],"best_for":["Batch processing workflows with variable-sized input collections","Embarrassingly parallel tasks (image processing, data transformation)","Workflows where task count is determined at runtime"],"limitations":["Mapped tasks cannot have conditional logic within the map; filtering requires separate tasks","Result collection is blocking; downstream tasks wait for all mapped runs to complete","Large mapped task counts (>10,000) can cause performance degradation in the scheduler","Nested mapping is supported but can create exponential task explosion"],"requires":["Task defined with @task decorator","Iterable input (list, tuple, generator, or upstream task result)","Sufficient worker concurrency to execute mapped tasks in parallel"],"input_types":["Iterable collections (lists, tuples, generators)","Upstream task results","Parameter combinations for multi-argument mapping"],"output_types":["List of task results (one per mapped input)","Task run records for each mapped execution"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-prefect__cap_7","uri":"capability://automation.workflow.logging.aggregation.and.structured.log.capture.from.distributed.tasks","name":"logging aggregation and structured log capture from distributed tasks","description":"Captures logs from task execution across distributed workers and aggregates them into a centralized logging system accessible via the Prefect UI and API. Logs are structured with metadata (task name, flow run ID, worker ID, timestamp) and can be filtered by flow run, task, or worker. The logging system supports multiple log levels and can be configured per-task or globally. Logs are stored in the Prefect database and are queryable, enabling debugging of distributed workflows without SSH access to worker machines.","intents":["Debug distributed task execution without accessing worker machines directly","Aggregate logs from multiple workers into a single searchable interface","Track task execution progress and identify bottlenecks via log analysis","Audit workflow execution by reviewing structured logs"],"best_for":["Distributed workflows running on multiple workers","Teams without direct access to worker infrastructure","Workflows requiring detailed execution audit trails"],"limitations":["Log storage is not unlimited; retention policies may discard old logs (configurable)","Log query performance degrades with large log volumes (>1M logs per flow run)","Custom log handlers must be compatible with Prefect's logging configuration","Log shipping from workers to server adds network overhead; high-volume logging can impact performance"],"requires":["Prefect server or Prefect Cloud","Workers configured with Prefect logging handlers","Network connectivity from workers to logging backend"],"input_types":["Log records from task execution (stdout, stderr, custom loggers)","Log level specifications","Log filter criteria"],"output_types":["Structured log records with metadata","Log query results","Log aggregation views in UI"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-prefect__cap_8","uri":"capability://tool.use.integration.block.system.for.credential.and.configuration.management","name":"block system for credential and configuration management","description":"Provides a pluggable block system where reusable configuration objects (credentials, connection strings, API keys) are stored encrypted in the Prefect database and can be referenced by name in task code. Blocks are defined as Python classes with type annotations and can be created via CLI, API, or UI. The block system supports custom block types via plugin registration, enabling integration with external systems (cloud providers, databases, APIs). Blocks are versioned and can be updated without modifying task code.","intents":["Store and manage credentials securely without embedding them in code","Reference external system configurations by name in task code","Share credentials across multiple workflows without duplication","Rotate credentials without modifying or redeploying workflow code"],"best_for":["Teams managing multiple credentials across many workflows","Organizations with security requirements for credential management","Workflows integrating with multiple external systems"],"limitations":["Blocks are stored in Prefect database; external credential stores (AWS Secrets Manager, HashiCorp Vault) require custom block implementations","Block updates are not automatically propagated to running tasks; in-flight tasks use cached block values","Block access control is limited to Prefect's role-based access control; fine-grained credential scoping requires custom implementation"],"requires":["Prefect server or Prefect Cloud","Block definition (built-in or custom)","Credentials or configuration values to store"],"input_types":["Block configuration (type, name, values)","Credential data (API keys, passwords, connection strings)","Custom block class definitions"],"output_types":["Block object with encrypted values","Block reference for use in task code","Block metadata (creation time, last updated)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-prefect__cap_9","uri":"capability://tool.use.integration.fastapi.based.rest.api.with.openapi.schema.generation","name":"fastapi-based rest api with openapi schema generation","description":"Exposes Prefect's core functionality via a FastAPI-based REST API with auto-generated OpenAPI/Swagger documentation. The API supports CRUD operations on flows, deployments, flow runs, tasks, and blocks, as well as querying execution history and logs. The API uses Pydantic models for request/response validation and supports both JSON and form-encoded request bodies. Authentication is handled via API keys or OAuth2 tokens, and authorization is enforced via role-based access control. The API is versioned and maintains backward compatibility across minor versions.","intents":["Programmatically create and manage workflows without using the CLI","Query execution history and logs from external systems","Integrate Prefect with custom dashboards and monitoring tools","Automate workflow deployment and configuration via infrastructure-as-code"],"best_for":["Teams building custom integrations with Prefect","Organizations automating workflow deployment via CI/CD","External systems needing to query Prefect state and logs"],"limitations":["API rate limiting is enforced; high-frequency polling can trigger rate limits","Some complex operations (multi-step deployments) require multiple API calls","API response payloads can be large for queries returning many results; pagination is required","Webhook delivery via API is not guaranteed; events can be lost if Prefect server is unavailable"],"requires":["Prefect server or Prefect Cloud","API key or OAuth2 token for authentication","HTTP client library (requests, httpx, curl)"],"input_types":["JSON request bodies","Query parameters","API keys or OAuth2 tokens"],"output_types":["JSON response bodies","OpenAPI schema","HTTP status codes"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","Prefect SDK installed via pip","Understanding of async/await patterns for advanced use cases","Backend database (SQLite, PostgreSQL, or Prefect Cloud)","Network connectivity to state persistence layer","Proper exception handling in tasks to trigger retry logic","Prefect SDK installed","API key if connecting to Prefect Cloud","Shell environment (bash, zsh, PowerShell)","Prefect server or Prefect Cloud"],"failure_modes":["Requires Python 3.9+ for full async/await support","Dynamic control flow (if/for loops with runtime conditions) requires special handling via task mapping","Circular task dependencies are not detected until runtime","State persistence adds ~50-200ms latency per state transition depending on database backend","Retry logic is task-level only; flow-level retries require manual implementation via parent task","State machine is not customizable; fixed set of states cannot be extended","Local execution does not support distributed task execution; all tasks run in a single process","Local execution does not persist state to a database; state is lost if the process terminates","Client library requires Python 3.9+; no JavaScript/TypeScript client available","Async execution requires event loop management; blocking operations can deadlock","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"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":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:22.334Z","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=pypi-prefect","compare_url":"https://unfragile.ai/compare?artifact=pypi-prefect"}},"signature":"R2QtmQRFnvLE8RmxTGzv+TehxYyJo6rc3tB2dt0afvfqKhZvDjEWc0kB3aCz+0F3TRRKX8r9ihvEU0gI7T+/Bw==","signedAt":"2026-06-21T22:49:27.800Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-prefect","artifact":"https://unfragile.ai/pypi-prefect","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-prefect","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"}}