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Found 128 Skills
Convert mermaid diagrams mmd/mermaid to .png and have an option to pick/increase resolution level. V2 includes L5 Delegated Constraint Verification for strict binary image linting.
Scaffold a traditional deterministic GitHub Actions CI/CD workflow. Use this when creating build, test, deploy, lint, release, or security scan pipelines. This is distinct from agentic workflows — no AI is involved at runtime.
(Industry standard: Loop Agent / Single Agent) Primary Use Case: Self-contained research, content generation, and exploration where no inner delegation is required. Self-directed research and knowledge capture loop. Use when: starting a session (Orientation), performing research (Synthesis), or closing a session (Seal, Persist, Retrospective). Ensures knowledge survives across isolated agent sessions.
Generate a custom checklist for the current feature based on user requirements.
Provides active execution protocols to rigorously audit how code, directory structures, and agent actions comply with the authoritative ecosystem specs. Trigger when validating new skills, plugins, or workflows.
Shows the Wasp plugin's available features, commands, and skills.
Get advice on app improvements and functionality from a Wasp expert. Takes optional arguments for more specific requests e.g. `/expert-advice how can I improve account management?`.
Adds Wasp knowledge, LLM-friendly documentation fetching instructions, and best practices to your project's CLAUDE.md or AGENTS.md file
Add Wasp's built-in features to your app — auth, email, jobs, and more. These are full-stack, batteries-included features that Wasp handles for you. Use when the user wants to add meta tags, authentication (email, social auth providers), email sending, database setup, styling (tailwind, shadcn), or other Wasp-powered functionality.
Validates dataset formatting and quality for SageMaker model fine-tuning (SFT, DPO, or RLVR). Use when the user says "is my dataset okay", "evaluate my data", "check my training data", "I have my own data", or before starting any fine-tuning job. Detects file format, checks schema compliance against the selected model and technique, and reports whether the data is ready for training or evaluation.
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.
Remote command execution and file transfer on SageMaker HyperPod cluster nodes via AWS Systems Manager (SSM). This is the primary interface for accessing HyperPod nodes — direct SSH is not available. Use when any skill, workflow, or user request needs to execute commands on cluster nodes, upload files to nodes, read/download files from nodes, run diagnostics, install packages, or perform any operation requiring shell access to HyperPod instances. Other HyperPod skills depend on this skill for all node-level operations.