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Implements token refresh logic and expiration handling to maintain authenticated sessions across CLI invocations.","intents":["I need to authenticate my CLI with Anyscale using my API credentials securely","I want to switch between multiple Anyscale workspaces or organizations from the CLI","I need to set up CI/CD pipelines with service account credentials that don't require interactive login"],"best_for":["Individual developers setting up local development environments","CI/CD pipeline engineers configuring automated deployments","Teams managing multiple Anyscale workspaces with different access levels"],"limitations":["Credential storage relies on OS keychain availability; headless servers may require environment variable fallback","No built-in credential rotation; manual token refresh required before expiration","Multi-factor authentication not supported through CLI (web-based login required)"],"requires":["Python 3.8+","Anyscale account with API key generation capability","OS keychain support (macOS Keychain, Windows Credential Manager, or Linux secret-service)"],"input_types":["API keys (string format)","OAuth tokens","Service account credentials (JSON)"],"output_types":["Authentication status confirmation","Active workspace/organization information","Token expiration warnings"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-anyscale__cap_4","uri":"capability://tool.use.integration.workspace.and.organization.management","name":"workspace and organization management","description":"Manages Anyscale workspace and organization contexts through CLI commands that list, switch, and configure active workspaces. Maintains context state (current workspace, organization, default cluster) in local configuration files and syncs with Anyscale backend to validate permissions. Supports role-based access control enforcement at the CLI level before API calls are made.","intents":["I need to list all Anyscale workspaces I have access to and switch between them","I want to set a default workspace so I don't have to specify it in every CLI command","I need to understand what permissions I have in each workspace before attempting operations"],"best_for":["Users managing multiple Anyscale workspaces across different projects or organizations","Teams with role-based access control requirements","Organizations with strict workspace isolation policies"],"limitations":["Workspace switching requires re-authentication if credentials differ between workspaces","No support for cross-workspace resource sharing or federation","Permission checks are advisory; actual authorization enforced server-side"],"requires":["Python 3.8+","Anyscale account with access to multiple workspaces","Valid API credentials for each workspace"],"input_types":["Workspace identifiers (name or ID)","Organization context","CLI flags for workspace selection"],"output_types":["List of accessible workspaces with metadata","Current workspace context information","Permission summary for active workspace"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-anyscale__cap_5","uri":"capability://data.processing.analysis.cli.output.formatting.and.structured.data.export","name":"cli output formatting and structured data export","description":"Formats CLI command output in multiple formats (human-readable tables, JSON, YAML) and supports structured data export for programmatic consumption. Implements output filtering, sorting, and column selection through CLI flags that transform API responses into desired formats. Enables piping output to other tools (jq, grep, awk) for advanced data processing.","intents":["I want to export cluster information as JSON for parsing in scripts or other tools","I need to filter and sort cluster lists by specific criteria without writing custom scripts","I want to pipe CLI output to other command-line tools for further processing"],"best_for":["DevOps engineers building automation scripts around Anyscale CLI","Data engineers integrating Anyscale operations into larger pipelines","Users preferring structured data output for programmatic consumption"],"limitations":["Output formatting applied client-side; large result sets may be slow to format","Custom output templates not supported; limited to predefined formats","Streaming output (for large result sets) not available; full results buffered before formatting"],"requires":["Python 3.8+","Basic knowledge of JSON/YAML for structured output parsing"],"input_types":["CLI command output","Format specification flags (--json, --yaml, --table)"],"output_types":["JSON-formatted structured data","YAML-formatted structured data","Human-readable table format","CSV export (if supported)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-anyscale__cap_6","uri":"capability://code.generation.editing.local.development.environment.setup.and.initialization","name":"local development environment setup and initialization","description":"Initializes local development environments for Ray projects with Anyscale integration through CLI commands that scaffold project structure, install dependencies, and configure local Ray runtime. Supports project templates for common use cases (ML training, data processing, analytics) and generates boilerplate code for cluster interaction. Uses Python package management (pip, poetry) to install Ray and Anyscale SDKs with compatible versions.","intents":["I want to quickly start a new Ray project with Anyscale integration without manual setup","I need to ensure my local Ray environment is compatible with my Anyscale cluster configuration","I want to generate example code showing how to interact with Anyscale clusters from Python"],"best_for":["New users getting started with Ray and Anyscale","Teams standardizing project structure across multiple Ray projects","Developers prototyping distributed applications locally before cluster deployment"],"limitations":["Project templates limited to predefined use cases; custom templates not supported","Local Ray runtime may differ from cluster runtime (version mismatches possible)","Dependency resolution may conflict with existing Python environments"],"requires":["Python 3.8+","pip or poetry for package management","Write permissions in target project directory"],"input_types":["Project name and type (template selection)","CLI flags for customization (Python version, dependencies)"],"output_types":["Scaffolded project directory structure","Generated Python files with boilerplate code","Configuration files (pyproject.toml, requirements.txt)","README with setup instructions"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-anyscale__cap_7","uri":"capability://data.processing.analysis.cluster.diagnostics.and.health.monitoring","name":"cluster diagnostics and health monitoring","description":"Provides CLI commands to diagnose cluster health, resource utilization, and runtime issues through queries to Anyscale's monitoring backend. Collects metrics (CPU, memory, network, Ray-specific metrics like task queue depth) and displays them in human-readable format or exports as structured data. Implements health checks that validate cluster connectivity, node availability, and Ray runtime status.","intents":["I want to check if my Ray cluster is healthy and all nodes are available","I need to understand current resource utilization to decide if I should scale the cluster","I want to diagnose why a job is slow or failing by examining cluster metrics"],"best_for":["DevOps engineers monitoring production Ray clusters","Data scientists debugging performance issues in distributed jobs","Teams implementing cluster health checks in monitoring systems"],"limitations":["Metrics have 30-60 second latency; real-time monitoring not available","Historical metrics retention limited to 7 days; long-term trend analysis requires external storage","Custom metric collection not supported; limited to Anyscale-provided metrics"],"requires":["Python 3.8+","Active Ray cluster on Anyscale","Monitoring permissions in workspace"],"input_types":["Cluster identifier","Time range for metrics (optional)","Metric type filters"],"output_types":["Cluster health status (healthy/degraded/unhealthy)","Resource utilization metrics (CPU, memory, disk)","Ray-specific metrics (task queue depth, actor count)","Node status information","JSON export of metrics"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","Anyscale account with valid API credentials","Network connectivity to Anyscale control plane","Active Ray cluster running on Anyscale","Job code compatible with Ray's serialization (pickle/cloudpickle)","Anyscale API credentials with configuration management permissions","YAML syntax knowledge for cluster specs","Anyscale account with API key generation capability","OS keychain support (macOS Keychain, Windows Credential Manager, or Linux secret-service)","Anyscale account with access to multiple workspaces"],"failure_modes":["Cluster configuration limited to Anyscale's predefined instance types and regions","No support for hybrid on-premise + cloud deployments","Cluster state changes are asynchronous; polling required for completion confirmation","Job logs streamed asynchronously; real-time streaming may have 1-5 second latency","No built-in job dependency management (DAG scheduling requires external orchestrators)","Result objects limited to serializable Python types; large outputs require external storage","Configuration templates stored in Anyscale backend; no local-only option for air-gapped environments","Limited template inheritance; complex multi-level overrides not supported","Schema validation errors may be cryptic without detailed error messages","Credential storage relies on OS keychain availability; headless servers may require environment variable fallback","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.26,"ecosystem":0.3,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:10.823Z","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-anyscale","compare_url":"https://unfragile.ai/compare?artifact=pypi-anyscale"}},"signature":"7lQYlUBMUVnBzum04Kmo/+N/0zwbftOwhakwfJhMIuCz0g6Gzp4vEkRwKLw4B9WALXhs9uYxa27WfEU0Us3VDw==","signedAt":"2026-06-18T21:01:56.641Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-anyscale","artifact":"https://unfragile.ai/pypi-anyscale","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-anyscale","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"}}