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Found 172 Skills
Train custom TTS voices for Piper (ONNX format) using fine-tuning or from-scratch approaches. Use when creating new synthetic voices, fine-tuning existing Piper checkpoints, preparing audio datasets for TTS training, or deploying voice models to devices like Raspberry Pi or Home Assistant. Covers dataset preparation, Whisper-based validation, training configuration, and ONNX export.
Standard implementation workflow for all coding tasks. Executes a systematic 5-phase cycle: Investigate → Plan → Implement → Verify → Complete. Integrates Serena think checkpoints, introspection markers, and quality gates. Supports --frontend-verify flag for browser/app/CLI visual verification. Use when: - User asks to implement a feature, fix a bug, or refactor code - User provides a task that requires code changes - User says "do this", "build this", "fix this", "add this" - Any implementation work involving code editing Keywords: task, implement, build, fix, add, create, refactor, update, change
Use when managing Crunch coordinators, competitions (crunches), rewards, checkpoints, staking, or cruncher accounts via the crunch-cli.
Apply a structured migration workflow from native mini-program projects to weapp-vite and wevu. Use when converting Page/Component code to Vue SFC, replacing setData-heavy state updates with ref/reactive, migrating properties/observers/triggerEvent contracts, introducing platform guards, and building migration-focused e2e validation and rollback checkpoints.
Phase-level planning workflow for planner agents. Handles reading templates, exploring codebase references, creating plan.md and phase files, self-validation, and checkpoint reporting to the orchestrator. Invoke this skill as your first action — not user-invocable.
Ensures tasks are genuinely resolved before marking them done. Activates at task checkpoints during plan execution — validates that fixes actually work, tests genuinely pass, and acceptance criteria are met. Prevents premature completion declarations.
Guides SwiftUI navigation using the Navigator/NavigatorUI library—NavigationDestination enums, ManagedNavigationStack, NavigationLink(to:label), deep linking (send/onNavigationReceive), checkpoints, dismissible views, and modular/provided destinations. Use when implementing or discussing SwiftUI navigation with Navigator, deep linking, checkpoints, or NavigatorUI.
LangGraph framework for building stateful, multi-agent AI applications with cyclical workflows, human-in-the-loop patterns, and persistent checkpointing.
This skill should be used when the user wants to implement features or fix bugs using test-driven development. Enforces the RED-GREEN-REFACTOR cycle with vertical slicing, context isolation between test writing and implementation, human checkpoints, and auto-test feedback loops. Uses multi-agent orchestration with the Task tool for architecturally enforced context isolation. Supports Jest, Vitest, pytest, Go test, cargo test, PHPUnit, and RSpec.
Data validation using Great Expectations. Expectation suites, checkpoints, and data docs for pipeline monitoring.
Use when the user wants to turn one or more Entire-tracked sessions, checkpoints, or repeated agent workflows into a reusable agent skill.
External NeMo-RL end-to-end validation workflow for Megatron-Bridge model/provider changes, including downstream compatibility checks, external RL lifecycle behavior, Megatron policy setup, HF import/export, checkpoint/resume, non-colocated vLLM refit, delta weight transfer, optional LoRA/generation variants, and questions such as "does this model work in NeMo-RL", "run NeMo-RL e2e", or "external RL loop validation". Covers running NeMo-RL Megatron policy jobs from a Bridge checkout, choosing GRPO/SFT/checkpoint/non-colocated refit variants, setting PYTHONPATH so NeMo-RL imports the local Bridge tree, and reporting pass/fail evidence.