{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"prefect","slug":"prefect","name":"Prefect","type":"framework","url":"https://github.com/PrefectHQ/prefect","page_url":"https://unfragile.ai/prefect","categories":["data-pipelines","automation"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"prefect__cap_0","uri":"capability://automation.workflow.decorator.based.flow.and.task.definition.with.automatic.state.tracking","name":"decorator-based flow and task definition with automatic state tracking","description":"Prefect uses Python decorators (@flow, @task) to transform standard functions into orchestrated units with built-in state management. The execution engine wraps decorated functions to automatically track execution state (Pending, Running, Completed, Failed, Cached) through a state machine, enabling recovery and observability without modifying core business logic. State transitions are persisted to the backend database and queryable via the Prefect Client.","intents":["Define data pipeline steps as simple Python functions without boilerplate orchestration code","Automatically capture execution state and history for debugging and auditing","Enable conditional branching and dynamic task dependencies based on upstream results"],"best_for":["Python developers building data pipelines who want minimal framework overhead","Teams migrating from Airflow seeking simpler task definition syntax"],"limitations":["Decorator-based approach couples pipeline logic to Prefect framework — refactoring to remove Prefect requires code changes","State machine adds ~50-100ms overhead per task execution for state serialization and persistence","Limited to Python — no native support for tasks in other languages without subprocess wrapping"],"requires":["Python 3.9+","Prefect installed via pip (prefect>=3.0)","For Prefect Cloud: valid API key and network connectivity"],"input_types":["Python function definitions","Function parameters (any serializable Python objects)"],"output_types":["Task/Flow objects with state metadata","Execution state records (Pending, Running, Completed, Failed, Cached)","Result objects with caching metadata"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prefect__cap_1","uri":"capability://automation.workflow.automatic.retry.and.failure.recovery.with.exponential.backoff","name":"automatic retry and failure recovery with exponential backoff","description":"Prefect's execution engine implements configurable retry logic at the task level using exponential backoff with jitter. When a task fails, the engine automatically re-executes it up to a specified retry count, with delays that grow exponentially (e.g., 1s, 2s, 4s, 8s). Retry policies are defined via @task decorators and stored in task metadata, allowing fine-grained control per task without modifying business logic.","intents":["Handle transient failures (network timeouts, rate limits) without manual intervention","Reduce operational burden of manual retry logic in data pipelines","Configure different retry strategies for different tasks (e.g., aggressive retries for API calls, no retries for database writes)"],"best_for":["Data engineers building resilient ETL pipelines with external API dependencies","Teams operating in unreliable network environments (cloud, multi-region)"],"limitations":["Retries are task-level only — no built-in flow-level rollback or saga pattern for distributed transactions","Exponential backoff can cause long delays for tasks with high retry counts (e.g., 10 retries = ~17 minutes total wait)","No automatic dead-letter queue or poison pill detection — failed tasks after max retries are marked Failed without secondary handling"],"requires":["Python 3.9+","Task decorated with @task(retries=N, retry_delay_seconds=M)","Idempotent task logic (retries assume task can be safely re-executed)"],"input_types":["Task definition with retry parameters","Task execution context"],"output_types":["Task state (Completed or Failed after max retries)","Execution logs with retry attempt metadata"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prefect__cap_10","uri":"capability://tool.use.integration.rest.api.and.python.client.for.programmatic.flow.management.and.monitoring","name":"rest api and python client for programmatic flow management and monitoring","description":"Prefect exposes a REST API (FastAPI-based) for all operations: creating flows, submitting runs, querying logs, managing blocks, and configuring automations. The Python client (PrefectClient) wraps the REST API and provides a Pythonic interface for SDK users. The client handles authentication (API key-based), connection pooling, and automatic retries. Both API and client support async operations for high-throughput scenarios.","intents":["Programmatically submit flow runs from external systems (e.g., data lake triggers)","Query flow run history and logs for monitoring and debugging","Manage deployments and automations via CI/CD pipelines"],"best_for":["Teams integrating Prefect with external systems (data lakes, BI tools)","Organizations automating deployment and configuration management"],"limitations":["API rate limiting is enforced server-side — high-frequency submissions (>100 req/s) may be throttled","Client authentication is API key-based — no OAuth or SAML support; requires key rotation for security","Async client requires event loop management — not suitable for synchronous scripts without additional setup"],"requires":["Prefect Server or Cloud","API key for authentication","Python 3.9+ for PrefectClient, any HTTP client for REST API"],"input_types":["Flow run parameters (JSON-serializable)","Query filters (run state, time range, tags)"],"output_types":["Flow run objects with metadata","Execution logs and state history","Deployment and automation configurations"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prefect__cap_11","uri":"capability://safety.moderation.multi.tenant.server.architecture.with.role.based.access.control.and.audit.logging","name":"multi-tenant server architecture with role-based access control and audit logging","description":"Prefect Server (self-hosted or Cloud) implements multi-tenancy with separate workspaces per tenant, role-based access control (RBAC) for flows/deployments/blocks, and audit logging of all API operations. The server uses FastAPI with SQLAlchemy ORM for database abstraction, supporting PostgreSQL and SQLite backends. Authentication is API key-based with scoped permissions (e.g., 'read flows', 'create deployments'). All operations are logged to the audit log with user, timestamp, and action metadata.","intents":["Isolate flows and data across teams or environments using workspaces","Control who can create, modify, or execute flows via RBAC","Audit all changes to flows and configurations for compliance"],"best_for":["Enterprise organizations with multi-team deployments","Regulated industries requiring audit trails and access control"],"limitations":["RBAC is coarse-grained (workspace-level) — no fine-grained permissions per flow or task","Audit logging is stored in the same database as operational data — no separate audit store for compliance","Multi-tenancy is logical (workspace-based) not physical — data is not isolated at the database level"],"requires":["Prefect Server (self-hosted) or Prefect Cloud","PostgreSQL or SQLite database","API keys with appropriate scopes for each user/service"],"input_types":["User credentials (API key)","Role definitions (read, write, admin)","API operations (create flow, submit run, etc.)"],"output_types":["Workspace isolation","RBAC enforcement on API operations","Audit log entries with user and action metadata"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prefect__cap_12","uri":"capability://code.generation.editing.mcp.model.context.protocol.server.integration.for.ai.assisted.workflow.generation","name":"mcp (model context protocol) server integration for ai-assisted workflow generation","description":"Prefect provides an MCP server that exposes Prefect operations (create flows, submit runs, query logs) as tools for AI models. The MCP server implements the Model Context Protocol, allowing Claude or other AI assistants to interact with Prefect via natural language. Users can ask the AI to 'create a flow that processes S3 files' and the AI generates Prefect code and submits it via MCP tools. The MCP server handles authentication and translates AI requests to Prefect API calls.","intents":["Generate Prefect flows from natural language descriptions","Allow non-technical users to create and manage workflows via AI assistants","Automate workflow creation and modification based on AI reasoning"],"best_for":["Teams using Claude or other AI assistants for workflow automation","Non-technical users who want to create data pipelines without coding"],"limitations":["AI-generated code may not follow best practices — requires human review before production deployment","MCP server requires Claude or compatible AI model — not usable with other LLMs without custom integration","No built-in validation of generated flows — AI may create syntactically valid but semantically incorrect code"],"requires":["Prefect Server or Cloud","Claude or compatible AI model with MCP support","MCP server running and configured with Prefect API key"],"input_types":["Natural language descriptions of workflows","AI model requests (via MCP protocol)"],"output_types":["Generated Prefect flow code","Submitted flow runs","Execution logs and results"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prefect__cap_13","uri":"capability://automation.workflow.context.based.variable.injection.and.flow.parameter.passing","name":"context-based variable injection and flow parameter passing","description":"Prefect uses context variables (via Python's contextvars module) to inject runtime information into flows and tasks without explicit parameter passing. The context includes flow run ID, task run ID, logger, and custom variables. Parameters can be passed to flows at submission time and accessed via the context or function arguments. The system supports parameter validation via Pydantic models, enabling type-safe parameter handling.","intents":["Access flow/task metadata (run ID, logger) without explicit parameter passing","Pass parameters to flows at submission time without modifying flow signatures","Validate parameters using Pydantic models for type safety"],"best_for":["Developers building complex flows with many parameters","Teams using Pydantic for data validation"],"limitations":["Context variables are thread-local — not suitable for async code without careful handling","Parameter validation is optional — no enforcement of parameter types at submission time","Context is flow-specific — cannot be accessed outside of flow/task execution"],"requires":["Python 3.9+","Flow code with context variable access (via get_run_context())","For parameter validation: Pydantic models"],"input_types":["Flow parameters (JSON-serializable or Pydantic models)","Context variables (flow run ID, task run ID, logger)"],"output_types":["Injected context variables in flow/task code","Validated parameters"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prefect__cap_2","uri":"capability://memory.knowledge.task.result.caching.with.configurable.ttl.and.cache.key.generation","name":"task result caching with configurable ttl and cache key generation","description":"Prefect provides task-level result caching that stores task outputs in a configurable cache backend (local filesystem, S3, or custom). Cache keys are generated from task name, version, and input parameters, allowing downstream tasks to skip execution if a cached result exists within the TTL. The cache is queryable and can be manually invalidated via the CLI or API.","intents":["Skip expensive computations (ML inference, data transformations) when inputs haven't changed","Reduce pipeline execution time by reusing intermediate results across runs","Enable deterministic re-runs by replaying cached results without re-executing upstream tasks"],"best_for":["Data science teams with long-running feature engineering pipelines","ETL teams processing large datasets where intermediate transformations are expensive"],"limitations":["Cache key generation is deterministic but opaque — custom cache key logic requires subclassing or manual implementation","No distributed cache invalidation — cache coherency issues in multi-worker deployments require external coordination","Cache storage is separate from result storage — requires dual writes and potential inconsistency if one fails"],"requires":["Python 3.9+","@task decorator with cache_key_fn or cache_expiration parameters","Cache backend configured (local filesystem by default, S3 requires credentials)"],"input_types":["Task outputs (any serializable Python object)","Cache key parameters (task name, version, input hash)"],"output_types":["Cached result object with metadata (cache hit/miss, TTL remaining)","Cache invalidation confirmation"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prefect__cap_3","uri":"capability://automation.workflow.scheduled.flow.execution.with.cron.and.interval.based.triggers","name":"scheduled flow execution with cron and interval-based triggers","description":"Prefect's deployment system supports scheduling flows via cron expressions or fixed intervals (e.g., every 6 hours). Schedules are defined in deployment configuration and managed by the Prefect Server, which uses a background scheduler service to emit flow run events at scheduled times. Workers poll for scheduled runs and execute them in their configured work pools, with full observability into scheduled vs. ad-hoc runs.","intents":["Automatically trigger data pipelines on a schedule without external cron infrastructure","Define complex schedules (e.g., business days only, with timezone awareness) declaratively","Monitor scheduled run history and troubleshoot missed or delayed executions"],"best_for":["Data teams running daily/hourly ETL jobs without dedicated scheduling infrastructure","Organizations consolidating scheduling logic into a single platform (vs. cron + Airflow)"],"limitations":["Scheduling is server-side — requires Prefect Server or Cloud to be running; local-only deployments cannot use scheduling","Schedule precision is limited by worker polling interval (default 30s) — sub-second scheduling not supported","No built-in support for dynamic scheduling based on external events; requires custom automation rules"],"requires":["Prefect Server running (self-hosted or Prefect Cloud)","Deployment created with schedule configuration (cron or interval)","Worker(s) running and assigned to the deployment's work pool"],"input_types":["Cron expression (e.g., '0 9 * * MON-FRI') or interval (e.g., 3600 seconds)","Timezone specification (e.g., 'America/New_York')"],"output_types":["Scheduled flow run records with execution timestamp","Flow run logs and state history"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prefect__cap_4","uri":"capability://automation.workflow.worker.based.distributed.task.execution.with.work.pools.and.concurrency.limits","name":"worker-based distributed task execution with work pools and concurrency limits","description":"Prefect's worker system enables distributed task execution across multiple machines or containers. Deployments are assigned to work pools (e.g., 'kubernetes', 'docker', 'local'), and workers poll the Prefect Server for flow runs assigned to their pool. Workers execute tasks with configurable concurrency limits (e.g., max 5 concurrent tasks per worker), and the server enforces global concurrency limits per work pool. Task execution can be containerized (Docker) or run directly on the worker machine.","intents":["Scale pipeline execution across multiple machines without managing job queues manually","Isolate task execution environments (e.g., run ML training in GPU containers, data processing on CPU workers)","Enforce resource limits and prevent worker overload via concurrency controls"],"best_for":["Teams running large-scale data pipelines requiring distributed execution","Organizations with heterogeneous compute resources (GPUs, high-memory nodes) needing task routing"],"limitations":["Worker polling adds latency (default 30s) between flow submission and execution start","No built-in load balancing across workers — work pools use simple FIFO assignment; requires external load balancer for advanced routing","Concurrency limits are enforced at work pool level, not globally across all pools — requires manual coordination for cross-pool limits"],"requires":["Prefect Server or Cloud","Worker process running (prefect worker start --pool <pool-name>)","Work pool configured in Prefect Server","For containerized execution: Docker daemon or Kubernetes cluster"],"input_types":["Flow run submission with work pool assignment","Concurrency limit configuration (tasks per worker, global limits)"],"output_types":["Flow run execution on assigned worker","Task execution logs with worker metadata","Concurrency metrics (active tasks, queue depth)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prefect__cap_5","uri":"capability://automation.workflow.event.driven.flow.triggering.with.custom.automation.rules","name":"event-driven flow triggering with custom automation rules","description":"Prefect's event system allows flows to be triggered by external events (e.g., S3 object creation, database updates) via custom automation rules. Events are emitted by integrations or custom code and stored in the Prefect Server's event log. Automation rules match events against patterns (e.g., 'event.resource.id matches \"s3://my-bucket/*\"') and trigger flow runs with parameterized inputs. Rules are evaluated server-side and can be created/updated via API or UI.","intents":["Trigger data pipelines in response to external events without polling","Build reactive data workflows that respond to upstream system changes","Parameterize flow runs based on event metadata (e.g., pass S3 bucket name to flow)"],"best_for":["Data teams building event-driven ETL (e.g., trigger on file upload)","Organizations consolidating event-driven orchestration into a single platform"],"limitations":["Event matching is pattern-based (string regex) — no complex boolean logic or nested conditions without custom rule code","Event delivery is at-least-once but not guaranteed — events may be lost if Prefect Server is down during emission","No built-in event deduplication — duplicate events trigger duplicate flow runs; requires idempotent flow logic"],"requires":["Prefect Server or Cloud","Event source integration (S3, webhook, custom emitter)","Automation rule created via API or UI with event pattern and flow trigger"],"input_types":["Event objects with resource ID, type, and metadata","Automation rule pattern (regex or custom matcher)"],"output_types":["Flow run triggered by event","Event log with rule match metadata","Flow run parameters derived from event"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prefect__cap_6","uri":"capability://data.processing.analysis.centralized.logging.and.observability.with.structured.log.aggregation","name":"centralized logging and observability with structured log aggregation","description":"Prefect's logging system captures all task and flow execution logs and aggregates them in the Prefect Server database. Logs are structured (JSON-formatted with metadata like task name, run ID, timestamp) and queryable via the API and UI. The logging system integrates with Python's standard logging module, allowing custom loggers to emit logs that are automatically captured and stored. Logs can be streamed to external systems (e.g., Datadog, Splunk) via integrations.","intents":["Centralize logs from distributed task execution without setting up ELK or Splunk","Query and filter logs by task, run, or time range for debugging","Stream logs to external observability platforms for long-term retention and analysis"],"best_for":["Teams running distributed pipelines needing centralized log visibility","Organizations consolidating observability into Prefect without external log aggregation"],"limitations":["Log storage is in Prefect Server database — no built-in log rotation or archival; requires manual cleanup for long-running systems","Log querying is limited to basic filters (task name, run ID, timestamp) — no full-text search or complex aggregations without external tools","Log streaming to external systems is integration-dependent — not all observability platforms have native Prefect support"],"requires":["Prefect Server or Cloud","Python logging module configured in flow/task code","For external streaming: integration installed and configured (e.g., prefect-datadog)"],"input_types":["Python log records (via logging.Logger)","Structured log metadata (task name, run ID, level)"],"output_types":["Aggregated log records in Prefect Server","Queryable log API with filtering","Log streams to external systems"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prefect__cap_7","uri":"capability://automation.workflow.deployment.versioning.and.code.packaging.with.automatic.dependency.management","name":"deployment versioning and code packaging with automatic dependency management","description":"Prefect's deployment system packages flow code and dependencies into versioned deployments that can be deployed to workers. Deployments are created via CLI (prefect deploy) or API and include flow code, Python dependencies (via pyproject.toml or requirements.txt), and configuration metadata. The system supports multiple deployment strategies: push deployments (code sent to worker at runtime) and pull deployments (worker pulls code from Git or S3). Deployments are versioned and can be rolled back via API.","intents":["Package and version flow code with dependencies for reproducible deployments","Deploy flow updates without restarting workers","Support multiple deployment strategies (push, pull from Git/S3) for different environments"],"best_for":["Data teams managing multiple flow versions and needing rollback capability","Organizations with CI/CD pipelines integrating Prefect deployments"],"limitations":["Dependency management is Python-only — no support for system-level dependencies or non-Python code without custom Docker images","Pull deployments require external storage (Git, S3) — adds complexity for teams without existing Git infrastructure","Deployment versioning is manual — no automatic version bumping or semantic versioning support"],"requires":["Prefect CLI installed (prefect>=3.0)","Flow code in a Python file or module","For pull deployments: Git repository or S3 bucket with code access"],"input_types":["Flow Python code","Dependencies (pyproject.toml or requirements.txt)","Deployment configuration (YAML or Python)"],"output_types":["Versioned deployment object in Prefect Server","Deployment metadata (code location, dependencies, configuration)","Deployment history with rollback capability"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prefect__cap_8","uri":"capability://tool.use.integration.block.based.configuration.management.for.credentials.and.external.system.connections","name":"block-based configuration management for credentials and external system connections","description":"Prefect's block system provides a declarative way to manage credentials and connections to external systems (databases, cloud storage, APIs) without hardcoding secrets. Blocks are configuration objects stored in the Prefect Server database and encrypted at rest. They can be referenced in flow code via block.load() and are injected at runtime. Blocks support templating and can be created via CLI, API, or UI. Common blocks include DatabaseBlock, S3Block, GCSBlock, and custom blocks can be defined by users.","intents":["Manage credentials securely without hardcoding secrets in flow code","Reuse connection configurations across multiple flows","Rotate credentials without redeploying flows"],"best_for":["Teams managing multiple external system connections across flows","Organizations with strict credential management policies"],"limitations":["Blocks are stored in Prefect Server database — encryption is at-rest only; requires TLS for transport security","Block loading adds ~50-100ms latency per block reference — not suitable for high-frequency credential lookups","No built-in credential rotation — requires manual updates or external secret management integration"],"requires":["Prefect Server or Cloud","Block defined and saved in Prefect Server","Flow code with block.load() call"],"input_types":["Block configuration (credentials, connection parameters)","Block type (DatabaseBlock, S3Block, etc.)"],"output_types":["Block object with connection parameters","Authenticated client for external system (e.g., boto3 S3 client)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prefect__cap_9","uri":"capability://planning.reasoning.flow.run.state.machine.with.conditional.branching.and.dynamic.task.dependencies","name":"flow run state machine with conditional branching and dynamic task dependencies","description":"Prefect's execution engine implements a state machine for flow runs with states (Scheduled, Pending, Running, Completed, Failed, Cancelled) and transitions. Tasks can conditionally execute based on upstream task states via if/else logic or the .result() method, enabling dynamic DAGs. The engine evaluates task dependencies at runtime, allowing downstream tasks to branch based on upstream results without pre-defining the full DAG structure.","intents":["Build dynamic pipelines that branch based on intermediate results","Skip tasks conditionally without defining multiple DAGs","Handle error cases with conditional retry or fallback logic"],"best_for":["Data teams building adaptive pipelines that respond to data characteristics","Organizations with complex conditional logic in workflows"],"limitations":["Dynamic dependencies are evaluated at runtime — cannot be visualized in the DAG UI until execution","Conditional logic is Python code — no declarative condition syntax; requires careful error handling","State transitions are not atomic — if a task fails during state update, the state may be inconsistent"],"requires":["Python 3.9+","Flow code with conditional logic (if/else or .result() calls)","Understanding of Prefect state machine (Scheduled, Pending, Running, etc.)"],"input_types":["Task results (any serializable Python object)","Conditional expressions (Python boolean logic)"],"output_types":["Flow run state (Completed, Failed, Cancelled)","Task execution graph with dynamic dependencies","Execution logs with branch information"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prefect__headline","uri":"capability://automation.workflow.workflow.orchestration.framework.for.data.and.ml.pipelines","name":"workflow orchestration framework for data and ml pipelines","description":"Prefect is a Python-native workflow orchestration framework that enables users to build, schedule, and monitor resilient data and ML pipelines with features like automatic retries and observability.","intents":["best workflow orchestration framework","workflow orchestration for data pipelines","Python-native orchestration tool for ML","how to schedule data workflows","data pipeline management solutions"],"best_for":["data engineers","ML practitioners"],"limitations":[],"requires":[],"input_types":["Python scripts"],"output_types":["orchestrated workflows","pipeline monitoring"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","Prefect installed via pip (prefect>=3.0)","For Prefect Cloud: valid API key and network connectivity","Task decorated with @task(retries=N, retry_delay_seconds=M)","Idempotent task logic (retries assume task can be safely re-executed)","Prefect Server or Cloud","API key for authentication","Python 3.9+ for PrefectClient, any HTTP client for REST API","Prefect Server (self-hosted) or Prefect Cloud","PostgreSQL or SQLite database"],"failure_modes":["Decorator-based approach couples pipeline logic to Prefect framework — refactoring to remove Prefect requires code changes","State machine adds ~50-100ms overhead per task execution for state serialization and persistence","Limited to Python — no native support for tasks in other languages without subprocess wrapping","Retries are task-level only — no built-in flow-level rollback or saga pattern for distributed transactions","Exponential backoff can cause long delays for tasks with high retry counts (e.g., 10 retries = ~17 minutes total wait)","No automatic dead-letter queue or poison pill detection — failed tasks after max retries are marked Failed without secondary handling","API rate limiting is enforced server-side — high-frequency submissions (>100 req/s) may be throttled","Client authentication is API key-based — no OAuth or SAML support; requires key rotation for security","Async client requires event loop management — not suitable for synchronous scripts without additional setup","RBAC is coarse-grained (workspace-level) — no fine-grained permissions per flow or task","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.49999999999999994,"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":"2026-06-17T09:51:05.295Z","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=prefect","compare_url":"https://unfragile.ai/compare?artifact=prefect"}},"signature":"zl2vLzQKc0er9ijbyiukL3fmcOWrcW0NlgcFLeJoQHVu1IQRy987oDDV1LveW4N7PA+7V+Z4IpDP+IeAB/8gDw==","signedAt":"2026-06-22T14:56:19.378Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/prefect","artifact":"https://unfragile.ai/prefect","verify":"https://unfragile.ai/api/v1/verify?slug=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"}}