Use AdalFlow to build, debug, evaluate, optimize, trace, and operate LLM task pipelines, RAG systems, agents, tools, and structured-output workflows.
Skills in this repository
VectorSpaceLab/AREX-Skill - Page 2
SkillsMP has collected 5,368 skills from VectorSpaceLab/AREX-Skill. Open a skill to review its source and details.
VectorSpaceLab/AREX-SkillShowing 40 of 5,368 collected skills.
Use AdalFlow Agent, Runner, ReActAgent, FunctionTool, ToolManager, streaming events, permissions, and MCP tools safely.
Build service-free AdalFlow Component, DataClass, Prompt, parser, and structured-output pipelines.
Guide AdalFlow evaluation metrics, datasets, parameters, gradients, prompt/few-shot/text-grad optimization, AdalComponent, Trainer, and optimize_anything workflows.
Use AdalFlow ModelClient, Generator, and Embedder workflows for provider integration, prompt/model kwargs, output processors, caching, streaming basics, and no-credential fake-client tests.
Document preprocessing, LocalDB persistence, retriever indexing, and RAG context assembly.
Route AdalFlow setup, logging, tracing, MLflow, and debug-artifact workflows.
Routes AdelaiDet users through legacy-compatible setup, model config selection, training/evaluation, demos, text spotting, dataset preparation, and export/conversion workflows.
Guides AdelaiDet COCO/PIC/LVIS/text dataset layout, semantic-mask generation, dataset registration, mapper expectations, and MEInst mask components.
Guides AdelaiDet image/video/webcam demos, VisualizationDemo usage, confidence thresholds, output handling, and dataset visualization.
Guides AdelaiDet checkpoint key conversion, optimizer stripping, FCOS/BlendMask weight migration, ONNX export, and optional deployment-runtime caveats.
Guides legacy-compatible AdelaiDet installation, Detectron2/PyTorch/CUDA version selection, editable extension builds, and runtime smoke checks.
Guides AdelaiDet BAText/ABCNet text spotting, BezierAlign, text datasets, dictionaries, lexicons, and TextEvaluator workflows.
Routes AdelaiDet config selection, training, evaluation, Detectron2 launch flags, checkpoints, and model-family run workflows.
Use Google ADK Python to build agents, Workflow graphs, tools, runtime services, CLI apps, evaluations, deployments, and ADK repository changes.
Build and troubleshoot ADK Python Agent/LlmAgent definitions, model settings, modes, callbacks, schemas, and multi-agent delegation.
Use ADK CLI commands, agent/app discovery, YAML configuration, local run/web/API server flows, eval/test commands, deployment commands, and safe CLI/config inspection.
Design and run ADK evaluation, test, and debugging workflows for eval sets, JSON fixtures, event traces, sessions, and safe native verification.
Modify the ADK Python repository itself with repo-specific setup, style, testing, docs, samples, schema generation, and review conventions.
Configure and troubleshoot ADK runtime services: Runner, App, sessions, memory, artifacts, plugins, telemetry, code executors, environments, event persistence, and service lifecycles.
Bind and troubleshoot ADK Python tools, ToolContext, long-running and confirmation tools, auth, MCP/OpenAPI/Google API/cloud integrations, A2A helpers, and optional extras.
Build and debug ADK 2.0 Workflow graphs, BaseNode/function nodes, graph routing, dynamic nodes, HITL, retry, checkpoint/resume, and workflow event flow.
Use Adversarial Robustness Toolbox (ART) for estimator wrappers, adversarial attacks, defences, poisoning/privacy/extraction, metrics, and certification workflows.
Choose and configure ART estimator wrappers for sklearn, PyTorch, TensorFlow/Keras, black-box, boosted tree, GPy, and regression models.
Compute ART robustness/privacy metrics, run evaluation objects, route SummaryWriter logging, and perform gradient checks and certification/verification workflows.
Use ART evasion attacks, preprocessing defences, and adversarial training recipes for image/tabular robustness workflows.
Plan ART poisoning, privacy inference, model inversion/reconstruction, extraction, and detector/mitigation workflows.
Install, import, optional dependency, backend-selection, and environment diagnostics guidance for ART users.
Use this repo skill for Agent Lightning package tasks: authoring trainable agents, tracing rewards and spans, running LightningStore/Trainer loops, using agl CLI services, choosing examples, and troubleshooting optional backends.
Author and debug Agent Lightning agents with rollout decorators, LitAgent classes, resource injection, return contracts, and single-rollout smoke checks.
Use Agent Lightning agl CLI commands, store and Prometheus services, LLMProxy/vLLM service patterns, metrics, and safe endpoint checks.
Choose, adapt, and troubleshoot Agent Lightning example workflows and optional dependency/backend recipes without reopening original repo examples.
Operate Agent Lightning runners, LightningStore APIs, rollout status and retry behavior, custom algorithms, Trainer.fit, and Trainer.dev workflows.
Emit, inspect, adapt, and troubleshoot Agent Lightning spans, rewards, operation traces, OpenTelemetry/AgentOps tracers, and token-ID signals.
Routes Agent Starter Pack tasks to the right workflow for creating, maintaining, and deploying generated agent projects.
Guides CI/CD setup, deployment commands, observability, data ingestion, and Gemini Enterprise registration for Agent Starter Pack projects.
Guides in-place enhancement, extraction, and version upgrades for projects generated by Agent Starter Pack.
Guides creation and discovery of new Agent Starter Pack projects from built-in, local, or remote templates.
AgentScope repo skill for building agents, provider connectors, RAG and memory workflows, service deployments, and local or sandboxed workspaces.
AgentScope agent, toolkit, permission, event, and local skill-loading workflows.