Terradev

Terradev is a cross-cloud compute control plane for AI workloads, not just a provisioning wrapper. Combines quoting, provisioning, topology optimization, training orchestration, inference tuning, and cost analytics in one CLI, with an accelerated idempotent runtime underneath. Model agnostic. Dataset agnostic. GPU agnostic. Provider agnostic. The only thing Terradev is not agnostic about is correctness: it enforces topology, idempotency, and sequencing. 17 providers are available BYOAPI. Amazon Web Services, Microsoft Azure, Baseten, Crusoe Cloud, DigitalOcean, E2E Networks, Gcore, Google Cloud Platform, Hugging Face, Hyperstack, InferX, Latitude.sh, RunPod, SiliconFlow, TensorDock, Vast.ai, YottaLabs Go to one of them and get a token or secrets package. These are stored locally in your secrets vault that can be provisioned through the secrets command. *DISCLAIMER* Terradev is an Apache 2.0 open source tool, is BYOAPI, does not charge any markup, and does not have relationships with any providers. Implementers are responsible for what they put in payloads, Terradev doesn't log request content, only public metadata. Usage in production systems is at the implementer's risk.

该来源不提供完整文件导出（国内平台多为平台内托管），仅存元数据与原链

接入信息

传输形态: http
鉴权方式: 鉴权未知
端点: https://terradev--wolfendentheo.run.tools

鉴权方式未标注，请核对官方文档后再接入——不要直接使用以下片段

{
  "mcpServers": {
    "Terradev": {
      "url": "https://terradev--wolfendentheo.run.tools"
    }
  }
}

能力清单

工具	说明
provision_gpu	Provision GPU instances for optimal parallel efficiency
preflight_report	Generate full preflight validation report with pass/warn/fail per check. Covers GPU drivers, CUDA, NCCL, RDMA, network, disk, and Docker.
preflight_gpu_check	GPU-specific preflight validation: NVIDIA drivers, CUDA version, GPU count, NCCL, NVLink topology, NCU stall-signature profiling, and adversarial config verification (V1-V3).
preflight_network_check	Network-specific preflight validation: RDMA availability, InfiniBand status, inter-node bandwidth, latency matrix, firewall rules.
local_scan	Scan local machine and network for available GPU devices. Returns total VRAM pool for local-first provisioning.
status	View all instances and costs with state optimization
manage_instance	Manage GPU instances (stop/start/terminate)
analytics	Get cost analytics
optimize	Find cheaper alternatives for running instances
setup_provider	Get setup instructions for a provider
configure_provider	Configure provider credentials
train	Launch distributed training on provisioned GPU nodes. Supports torchrun, deepspeed, accelerate, and megatron. Use from_provision='latest' to auto-resolve node IPs from your last provision command.
preflight	Pre-training validation: GPU availability, NCCL, RDMA, drivers across all nodes.
stage	Compress, chunk, checksum, and position datasets near compute. Supports local paths, S3/GCS URIs, HTTP URLs, and HuggingFace dataset names. Returns staging plan with agent recommendations.
up	CLI-native provisioning with manifest cache and drift detection. Use --fix-drift to detect and auto-fix drifted infrastructure.
rollback	Explicit versioned rollback. Format: job@version (e.g., llama3@v3).
manifests	List cached manifests and versions for jobs.
run_workflow	Run a declarative YAML workflow that chains multiple Terradev commands (provision → preflight → train → monitor → checkpoint). Returns step-by-step execution status with cost estimates and confirmation gates for expensive operations.
active_context	Get current Terradev state: running training jobs, active instances, spend-to-date, alerts. Call this on session start to resume context from previous sessions.
governance_request_consent	Request user consent for data movement across cloud regions. GDPR/SOC2 compliant consent tracking with audit trail.
governance_record_consent	Record a consent response (granted or denied) for a pending consent request.
governance_evaluate_opa	Evaluate OPA (Open Policy Agent) policies for data access. Checks region restrictions, classification rules, and compliance requirements.
governance_move_data	Move data with full governance audit trail. Requires prior consent and OPA policy approval. Tracks integrity, encryption, and compliance.
governance_movement_history	Get data movement audit log. Filter by user, dataset, or time range.
governance_compliance_report	Generate comprehensive compliance report: consent stats, policy evaluations, data movements, violations. For GDPR/SOC2/HIPAA audits.
inferx_deploy	Deploy model to InferX serverless platform
inferx_status	Check InferX endpoint status
inferx_list	List deployed InferX models
inferx_optimize	Get cost analysis for inference endpoints
inferx_configure	Configure InferX serverless platform credentials.
inferx_delete	Delete an InferX model deployment.
inferx_usage	Get InferX account usage statistics: requests, cost, GPU hours, latency.
inferx_quote	Get InferX pricing quotes for a GPU type.
infer_route	Semantic-aware inference routing. Analyzes query content across 6 signal dimensions (modality, complexity, domain, language, safety, keywords), applies NUMA-aware endpoint scoring, and selects the optimal inference endpoint. Uses DAG parallel execution for signal extraction.
infer_route_disagg	Disaggregated Prefill/Decode routing (DistServe architecture). Splits LLM inference into compute-bound prefill phase (routed to FLOPS-optimized GPUs like H100 SXM) and memory-bound decode phase (routed to bandwidth-optimized GPUs like MI300X). Tracks KV cache handoffs between endpoint pairs.
infer_failover	Run health checks and auto-failover for inference endpoints. If a primary endpoint is unhealthy and has a backup configured, traffic automatically shifts to the backup provider.
gpu_topology	GPU NUMA topology report with intra-GPU XCD (Accelerated Compute Die) awareness. Models MI300X (8 XCDs, 192GB HBM3), MI300A (6 XCDs, 128GB), H200 (unified 141GB HBM3e), H100 (80GB). Reports PCIe locality (PIX/PXB/PHB/SYS), GPU-NIC pairing, SR-IOV VF status, and generates XCD-aware NCCL/AITER environment variables.
vllm_start	Start a vLLM inference server on a remote instance via SSH/systemd. Supports Multi-LoRA, Sleep Mode, KV Offloading, Speculative Decoding.
vllm_stop	Stop a vLLM server on a remote instance.
vllm_inference	Test inference against a running vLLM endpoint (completions or chat).
vllm_info	Get vLLM server info: loaded models, config, and health status.
vllm_sleep	Put a vLLM server to sleep. Level 1: offload to CPU (fast wake). Level 2: discard weights (minimal RAM).
vllm_wake	Wake a sleeping vLLM server. For Level 2 sleep, also reloads weights and resets prefix cache.
sglang	Complete SGLang optimization stack with workload-specific auto-tuning for 7 workload types: agentic chat, batch inference, low latency, MoE models, PD disaggregated, structured output, and RAG.
sglang_start	[DEPRECATED] Use sglang action='start' instead. Start an SGLang inference server on a remote instance.
sglang_stop	[DEPRECATED] Use sglang action='stop' instead. Stop an SGLang server on a remote instance.
sglang_inference	[DEPRECATED] Use sglang action='inference' instead. Test inference against a running SGLang endpoint.
ollama_list	List models available on an Ollama server.
ollama_pull	Pull a model to an Ollama server on a remote instance.
ollama_generate	Generate text using an Ollama model (non-chat completions).
ollama_chat	Chat with an Ollama model using the chat/completions API.
ollama_model_info	Get detailed information about an Ollama model (parameters, template, license).
vllm_auto_optimize	Automatically optimize vLLM configuration by analyzing workload patterns. Selects optimal settings for the 6 critical knobs based on live endpoint metrics or sample request files.
vllm_analyze	Analyze a running vLLM server's workload and return specific optimization recommendations with before/after comparisons.
vllm_benchmark	Benchmark a vLLM endpoint with concurrent requests. Returns throughput (req/s), success rate, and total latency.
ollama_ps	List currently running Ollama models.
langfuse_configure	Configure Langfuse credentials (public key, secret key, host URL).
langfuse_test	Test Langfuse connectivity and list accessible projects.
langfuse_traces	List recent LLM traces from Langfuse.
langfuse_trace	Get a single Langfuse trace with all observations/spans.
langfuse_scores	List evaluation scores from Langfuse, optionally filtered by trace or score name.
langfuse_score	Create an evaluation score for a Langfuse trace (e.g. quality, accuracy, relevance).
langfuse_datasets	List Langfuse datasets for evaluation and fine-tuning.
langfuse_export_training_data	Export Langfuse traces as instruction/response pairs for LoRA fine-tuning. Filters by quality score.
langfuse_quality	Get aggregated quality metrics from Langfuse scores for drift detection.
langfuse_otel_env	Print OTEL environment variables for instrumenting LLM apps to send traces to Langfuse.
langfuse_k8s	Generate Kubernetes deployment manifest for self-hosted Langfuse.
k8s_create	Create Kubernetes cluster with GPU nodes for optimal multi-cloud deployment
k8s_list	List Kubernetes clusters
k8s_info	Get information about a specific cluster
k8s_destroy	Destroy a Kubernetes cluster
helm_generate	Generate Helm charts from workload specifications.
kserve_generate_yaml	Generate a GPU-aware KServe InferenceService YAML manifest with NUMA pinning, resource limits derived from model size and VRAM, and topology hints.
kserve_list	List KServe InferenceServices in a Kubernetes namespace.
kserve_status	Get detailed status of a KServe InferenceService including readiness, traffic split, and URL.
k8s_gpu_operator_install	Install NVIDIA GPU Operator on a Kubernetes cluster. Configures driver containers, device plugin, DCGM exporter, and GPU Feature Discovery.
k8s_device_plugin	Configure Kubernetes GPU device plugin settings: time-slicing, MIG strategy, and resource naming.
k8s_mig_configure	Configure Multi-Instance GPU (MIG) partitioning on A100/H100 GPUs. Splits a single GPU into isolated instances for multi-tenant workloads.
k8s_time_slicing	Configure GPU time-slicing for Kubernetes. Allows multiple pods to share a single GPU with configurable oversubscription.
hf_space_deploy	Deploy model to HuggingFace Spaces
hf_space_status	Get HuggingFace Space deployment status.
ray_status	Get Ray cluster status including node count, resources, memory, and running jobs.
ray_start	Start a Ray cluster (head node or worker). For distributed ML training and inference.
ray_stop	Stop the Ray cluster on the current node.
ray_submit_job	Submit a job script to the Ray cluster for distributed execution.
ray_list_jobs	List all running Ray jobs and tasks.
ray_wide_ep_deploy	Generate a Ray Serve LLM Wide-EP (Expert Parallel) deployment for MoE models. Returns Python script and config for distributed MoE serving with EPLB and DeepEP.
ray_disagg_pd_deploy	Generate a Ray Serve LLM disaggregated Prefill/Decode deployment. Splits inference into compute-bound prefill and memory-bound decode phases with KV cache transfer via NIXL.
ray_parallelism_strategy	Compute optimal TP/DP/EP parallelism strategy for a given MoE model and GPU count. Returns recommended configuration with rationale.
wandb_list_projects	List all Weights & Biases projects for the configured entity.
wandb_list_runs	List runs in a W&B project with status, metrics summary, and config.
wandb_run_details	Get detailed info, metrics, and artifacts for a specific W&B run.
mlflow_list_experiments	List MLflow experiments on the configured tracking server.
mlflow_log_run	Log a Terradev training run to MLflow with auto-injected GPU type, provider, cost/hr, and duration as params.
mlflow_register_model	Register a trained model in the MLflow model registry with Terradev provenance tags.
dvc_status	Get DVC repository status: tracked files, remotes, and changes since last commit.
dvc_diff	Show DVC diff between two revisions (e.g. training checkpoints). Shows added, modified, deleted files.
dvc_stage_checkpoint	Atomic checkpoint staging: DVC add + push + git commit in one operation. Promotes a training checkpoint to versioned storage.
dvc_push	Push DVC-tracked data to the configured remote storage.
hf_list_models	Search and browse HuggingFace Hub models. Filter by author, task, library. Returns model ID, downloads, likes, and tags.
hf_list_datasets	Search and browse HuggingFace Hub datasets. Filter by author and search query.
hf_model_info	Get detailed model info: architecture, size, downloads, license, tags, pipeline_tag, and model card.
hf_create_endpoint	Create a HuggingFace Inference Endpoint (paid GPU endpoint). Supports custom GPU types, regions, and scaling.
hf_list_endpoints	List all active HuggingFace Inference Endpoints with status, URL, and cost.
hf_endpoint_info	Get detailed info about a specific HuggingFace Inference Endpoint: status, URL, scaling config, cost.
hf_delete_endpoint	Delete a HuggingFace Inference Endpoint.
hf_endpoint_infer	Run inference on a HuggingFace Inference Endpoint. Supports text generation, embeddings, and custom inputs.
hf_smart_template	Auto-generate an optimized deployment template for any HuggingFace model. Analyzes model size, architecture, and quantization to select optimal hardware and generate ready-to-deploy configs.
hf_hardware_recommend	Get hardware recommendation with cost breakdown for any HuggingFace model. Returns optimal GPU type, estimated cost, and performance score.
hf_hardware_compare	Compare all hardware options for a HuggingFace model. Returns side-by-side cost, performance, and compatibility analysis.
langchain_create_workflow	Create a LangChain workflow.
langchain_create_sglang_pipeline	Create an SGLang model-serving pipeline via LangChain. Connects LangChain agents to SGLang inference endpoints.
langgraph_create_workflow	Create a LangGraph stateful workflow with monitoring. Supports agent graphs, tool calling, and state persistence.
langgraph_orchestrator_worker	Create an orchestrator-worker pattern workflow in LangGraph. The orchestrator delegates tasks to specialized worker agents.
langgraph_evaluation_workflow	Create an evaluator-optimizer workflow in LangGraph. Generates outputs, evaluates quality, and iteratively improves.
langgraph_workflow_status	Get the status and metrics of a LangGraph workflow execution.
wandb_create_dashboard	Create a custom W&B dashboard with GPU metrics, training loss, and cost panels.
wandb_create_terradev_dashboard	Auto-create a Terradev-specific W&B dashboard with GPU utilization, cost tracking, training metrics, and infrastructure panels.
wandb_create_report	Create a W&B report with custom sections, charts, and narrative text.
wandb_create_terradev_report	Auto-generate a Terradev infrastructure report: GPU costs, provider comparison, training efficiency, and recommendations.
wandb_setup_alerts	Set up custom W&B alerts for GPU metrics: cost thresholds, utilization drops, training anomalies.
wandb_create_terradev_alerts	Auto-create standard Terradev alerts: GPU cost > budget, utilization < 50%, training loss spike, straggler detection.
wandb_dashboard_status	Get comprehensive W&B monitoring overview: dashboards, reports, alerts, active runs.
phoenix_test	Test connection to Arize Phoenix server. Returns collector endpoint and project count.
phoenix_projects	List Phoenix projects (trace namespaces).
phoenix_spans	List recent spans for a Phoenix project. Supports SpanQuery DSL filters like "span_kind == 'RETRIEVER'" or "status_code == 'ERROR'".
phoenix_trace	View full execution tree for a specific trace ID. Shows span hierarchy, latencies, and token counts.
phoenix_otel_env	Generate OpenTelemetry environment variables for instrumenting serving pods with Phoenix tracing.
phoenix_snippet	Generate Python instrumentation snippet for adding Phoenix tracing to LLM applications.
phoenix_k8s	Generate Kubernetes deployment manifest for self-hosted Arize Phoenix server.
guardrails_test	Test connection to NeMo Guardrails server.
guardrails_chat	Send a message through NeMo Guardrails and return the safety-filtered response. Applies topical, jailbreak, PII, and factcheck rails.
guardrails_generate_config	Generate default Colang 2.x guardrails configuration files (topical, jailbreak, PII, factcheck rails).
guardrails_k8s	Generate Kubernetes deployment manifest for NeMo Guardrails server (standalone or sidecar mode).
qdrant_test	Test connection to Qdrant vector database. Returns cluster info and collection count.
qdrant_collections	List all Qdrant vector collections with their point counts and configurations.
qdrant_create_collection	Create a Qdrant vector collection. Auto-configures vector dimensions from embedding model name.
qdrant_info	Get detailed info and stats for a Qdrant collection.
qdrant_count	Count points (vectors) in a Qdrant collection.
qdrant_k8s	Generate Kubernetes StatefulSet manifest for self-hosted Qdrant vector database.
deepeval_run	Run a DeepEval test suite from a Python test file.
deepeval_metrics	List available DeepEval metrics for LLM evaluation.
deepeval_evaluate	Evaluate a single LLM output with a DeepEval metric (AnswerRelevancy, Faithfulness, Hallucination, etc.).
deepeval_init	Generate a starter DeepEval test file.
egress_cheapest_route	Find the cheapest egress route between cloud providers/regions for model weights or dataset transfer. Supports multi-hop routing.
egress_optimize_staging	Optimize dataset or model staging across regions by finding the cheapest transfer plan. Integrates with the dataset stager for parallel uploads.
orchestrator_start	Start the model orchestrator for multi-model GPU sharing with eviction policies.
orchestrator_register	Register a model with the orchestrator.
orchestrator_load	Load a model into GPU memory.
orchestrator_evict	Evict a model from GPU memory.
orchestrator_status	Get orchestrator and model status including GPU memory utilization.
orchestrator_infer	Test inference with a model via the orchestrator.
warm_pool_start	Start the warm pool manager for intelligent model pre-warming. 5 strategies: traffic_based, time_based, priority_based, cost_optimized, latency_optimized.
warm_pool_status	Get warm pool status: hit rate, cold starts, memory saved, cost saved.
price_intel	GPU price intelligence with quantitative analytics. Computes delta (rate of change), gamma (acceleration), and annualized realized volatility on GPU spot/on-demand prices across 21+ providers. Identifies cheapest time windows and provider arbitrage opportunities.
cost_analyze	Deep cost analysis of current GPU infrastructure: per-provider breakdown, utilization efficiency, waste identification, and optimization potential.
cost_optimize_recommend	Generate actionable cost optimization recommendations: spot migration, GPU right-sizing, provider arbitrage, idle shutdown, and density packing.
cost_simulate	Simulate cost optimization scenarios with ROI projections. Compare current vs optimized infrastructure costs.
price_trends	Get GPU price trend analysis with delta (rate of change), gamma (acceleration), and annualized volatility. Identifies cheapest time windows.
price_spot_risk	Spot instance risk assessment per provider. Returns interruption probability, mean time to interruption, and recommended mitigation.
moe_deploy	Deploy Mixture-of-Experts models with production-ready cluster templates. Auto-applies vLLM cost optimizations (KV cache offloading for up to 9x throughput, MTP speculative decoding for up to 2.8x speed, sleep mode for 18-200x faster restarts). Supports GLM-5, Qwen 3.5, Mistral Large 3, DeepSeek V4, Llama 5. Configures NVLink topology, tensor parallelism, FP8 quantization, vLLM/SGLang backends, and GPU-aware HPA autoscaling.
train_status	List all training jobs and their state (created, running, completed, failed).
train_monitor	Real-time GPU monitoring for training jobs. Shows utilization, memory, temperature, power, and cost.
checkpoint_list	List all checkpoints for a training job.
checkpoint_save	Manually trigger a checkpoint save for a running training job.
train_stop	Stop a running training job. Kills training processes on all nodes in parallel.
train_resume	Resume a training job from its latest checkpoint. Rebuilds config with topology revalidation.
checkpoint_restore	Restore a specific checkpoint for a training job.
checkpoint_promote	Promote a checkpoint to a final model path for serving.
checkpoint_delete	Delete a checkpoint.
lora_list	List LoRA adapters loaded on a running vLLM endpoint. Shows base models and hot-loaded fine-tuned adapters.
lora_add	Hot-load a LoRA adapter onto a running vLLM endpoint. The adapter becomes immediately available as a model name for inference requests. Uses vLLM's fused_moe_lora kernel for 454% higher output tokens/sec on MoE models.
lora_remove	Hot-unload a LoRA adapter from a running vLLM endpoint. Frees GPU memory for other adapters.
training_config_generate	Generate a complete training configuration from a declarative spec. Auto-detects framework, sets optimal parallelism, and configures distributed training.
training_launch_distributed	Full distributed training launch with framework auto-detection, topology validation, and monitoring. Combines preflight + train + monitor in one operation.
train_snapshot	Get complete training monitoring snapshot: GPU metrics (utilization, memory, temp, power), training metrics (loss, grad_norm, lr, throughput), straggler detection, and cost estimate.
train_detect_stragglers	Detect straggler nodes in distributed training. Identifies GPUs with significantly lower utilization that slow the whole job.
agent_agentic_serving_configure	Configure agentic inference serving settings.
agent_agentic_serving_helm_values	Print Helm values for agentic inference deployment.
agent_agentic_serving_k8s	Print K8s deployment manifests for agentic inference.
agent_agentic_serving_launch_args	Print engine launch arguments for copy-paste.
agent_agentic_serving_lmcache_env	Print LMCache environment variables.
agent_agentic_serving_show_config	Show current agentic serving configuration.
agent_cost	Show real-time cost breakdown for a fleet by tier.
agent_deploy	Provision a heterogeneous agent fleet across all tiers simultaneously.
agent_langchain_create_langgraph	Create a LangGraph workflow.
agent_langchain_create_pipeline	Create an SGLang pipeline.
agent_langchain_create_workflow	Create a LangChain workflow.
agent_langchain_test	Test connection to LangChain service.
agent_langgraph_create_workflow	Create a LangGraph workflow.
agent_langgraph_deploy	Deploy a workflow.
agent_langgraph_status	Get workflow status.
agent_langgraph_test	Test connection to LangGraph service.
agent_letta_chat	Send a message to a Letta agent.
agent_letta_create	Create a new stateful Letta agent.
agent_letta_delete	Delete a Letta agent.
agent_letta_list	List Letta agents.
agent_letta_remember	Teach a Letta agent a durable fact.
agent_letta_status	Show the state of a Letta agent.
agent_mem0_add	Store a memory in Mem0 for an agent or user.
agent_mem0_configure	Configure Mem0 credentials and defaults.
agent_mem0_delete	Delete a memory by ID.
agent_mem0_forget	Delete all memories matching an entity scope.
agent_mem0_get	Get a single memory by ID.
agent_mem0_list	List memories for an entity scope.
agent_mem0_search	Search agent/user memories.
agent_mem0_test	Test connection to Mem0.
agent_mem0_update	Update a memory by ID.
agent_list	List all known agent fleets.
agent_plan	Plan a heterogeneous agent fleet without provisioning.
agent_scale	Scale a single fleet tier up or down without affecting other tiers.
agent_skill_attach	Attach a skill.md to a Letta agent as a durable memory block.
agent_skill_init	Create a skill.md template for an agent.
agent_status	Show live status of a fleet — tier health, KV hit rate, queue depth, cost.
agent_teardown	Terminate all fleet instances and remove fleet state.
agent_vector_db_down	Teardown a vector database provisioned for an agent fleet.
agent_vector_db_up	Provision a vector database for an agent fleet.
ml_vllm_lora_link	Load the active registry version of an adapter onto a vLLM server.
ml_vllm_lora_list	List LoRA adapters currently loaded on a vLLM server.
ml_vllm_lora_load	Hot-load a LoRA adapter onto a running vLLM server.
ml_vllm_lora_sync	Synchronize an adapter from the registry across multiple vLLM replicas.
ml_vllm_lora_unload	Hot-unload a LoRA adapter from a running vLLM server.
database_weaviate_create_collection	Create a Weaviate collection.
database_weaviate_delete_collection	Delete a Weaviate collection.
database_weaviate_hybrid_search	Hybrid vector + BM25 search in a Weaviate collection.
database_weaviate_insert	Insert objects into a Weaviate collection.
database_weaviate_list_collections	List Weaviate collections.
database_weaviate_query	Vector similarity search in a Weaviate collection.
database_weaviate_up	Initialize a Weaviate connection.
train_unsloth_run	Run an Unsloth local model server.
train_unsloth_start	Start a coding agent backed by Unsloth's local model server.
train_unsloth_stop	Stop a running Unsloth server started with `unsloth run`.
create_sqlite_connection	Create a SQLite database connection with auto-table creation. Returns a connection ID for subsequent operations.
create_postgresql_connection	Create a PostgreSQL database connection with auto-table creation. Returns a connection ID for subsequent operations.
query_database	Execute a SELECT query on a database connection. Returns query results as a list of dictionaries.
upsert_database	Insert or update data in a database table. Performs upsert operation (insert or update on conflict).
get_database_connection	Get information about a database connection including type, status, and configuration.
secrets_set	Store a secret in the Terradev secrets store. Values are encrypted at rest.
secrets_get	Retrieve a stored secret. By default the value is masked.
secrets_list	List stored provider and key names. Values are never shown.
secrets_remove	Remove a provider or a single key from the secret store.
secrets_sync	Import TERRADEV_* environment variables into the secret store for supported cloud providers.
secrets_verify	Check which providers are fully configured and which keys are missing.
secrets_env	Print environment-style export lines for a provider. By default values are masked.
secrets_run	Run a shell command with secrets injected into the environment.

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