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Found 139 Skills
Implement account linking using StackOne Connect Sessions and the Hub React component. Use when user asks to "connect a provider", "embed the integration picker", "add BambooHR to my app", "create a connect session", "set up auth links", or "handle account webhooks". Covers the full flow from session creation to webhook handling. Do NOT use for making API calls after linking (use stackone-platform) or building AI agents (use stackone-agents).
Guides agents in compiling and packaging C/C++ source code into dynamic or static libraries (Code Assets) using Dart's Native Assets hook system (via hook/build.dart and hook/link.dart utilizing package:hooks and package:native_toolchain_c). Use when a user asks to: 'setup native assets', 'compile C/C++ source code', 'bundle dynamic libraries', 'build native C code', 'link native assets', 'implement build.dart or link.dart hooks', or 'integrate C/C++ interop in Dart/Flutter'. Helps agents avoid manual toolchain orchestration and configures secure hash-validated binary downloads or advanced linker tree-shaking with package:record_use mapping.
Use switch expressions and pattern matching where appropriate
Guide agents to use `package:ffigen` to automatically generate FFI bindings instead of writing them manually. Use this skill when a task involves writing new FFI bindings, extending C/Objective-C/Swift integrations, or replacing hand-crafted `dart:ffi` setups.
Analyzes Azure costs and provides optimization recommendations including reserved instances, rightsizing, and unused resources. Use when optimizing Azure spending or analyzing Azure costs.
Provides production-ready Kubernetes manifest guidance including resource management, security, high availability, and configuration best practices. This skill should be used when working with Kubernetes YAML files, deployments, pods, services, or when users mention k8s, container orchestration, or cloud-native applications.
Build and deploy custom StackOne connectors using the CLI and Connector Engine. Use when user asks to "build a custom connector", "deploy my connector", "use the StackOne AI builder", "set up CI/CD for connectors", "test my connector locally", or "install the StackOne CLI". Covers the full connector development workflow from init through deployment. Do NOT use for using existing connectors (use stackone-connectors) or building AI agents (use stackone-agents).
Discovers user intent and generates a structured, step-by-step customization plan that orchestrates other skills. Always activate at the start of every conversation, when all tasks in a plan are completed, or when the user asks to modify the current plan. Handles intent discovery, plan generation, plan iteration, and mid-execution plan alterations. When in doubt, use this skill.
Resolves shared ecosystem environment constants (HuggingFace credentials, dataset repo IDs, project root path) for any plugin without depending on internal shared libraries. V2 enforces Token Leakage constraints.
Selects a base model and fine-tuning technique (SFT, DPO, or RLVR) for the user's use case by querying SageMaker Hub. Use when the user asks which model or technique to use, wants to start fine-tuning, or mentions a model name or family (e.g., "Llama", "Mistral") — always activate even for known model names because the exact Hub model ID must be resolved. Queries available models, validates technique compatibility, and confirms selections.
Generates a Jupyter notebook that fine-tunes a base model using SageMaker serverless training jobs. Use when the user says "start training", "fine-tune my model", "I'm ready to train", or when the plan reaches the finetuning step. Supports SFT, DPO, and RLVR trainers, including RLVR Lambda reward function creation.
Configure Python package metadata, setup.py, and pyproject.toml for distribution using UV or setuptools. Use when setting up Python packages, configuring build systems, or preparing projects for PyPI publication.