'guide' 검색 결과

One-time setup that gathers design context for your project and saves it to your AI config file. Run once to establish persistent design guidelines.

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Guide for when to use and when not to use return validators in Convex functions. Use this skill whenever the user is writing Convex queries, mutations, or actions and needs guidance on return value validation. Also trigger when the user asks about Convex type safety, runtime validation, AI-generated Convex code, Convex AI rules, Convex security best practices, or when they're debugging return type issues in Convex functions. Trigger this skill when users mention "validators", "returns", "return type", or "exact types" in the context of Convex development. Also trigger when writing or reviewing Convex AI rules or prompts that instruct LLMs how to write Convex code.

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Full-stack Convex development guidelines covering React, Vite, TypeScript, mutations, auth, design system, and documentation practices. Use when building features, writing Convex functions, or making code changes in this project.

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Guide for the Tinker CLI — managing training runs, checkpoints, downloading weights, and publishing to HuggingFace. Use when the user asks about CLI commands, listing runs, managing checkpoints from the terminal, or uploading to HF Hub.

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Guide for dataset construction — SupervisedDatasetBuilder, RLDatasetBuilder, ChatDatasetBuilder, and custom dataset creation from JSONL, HuggingFace, or conversation data. Use when the user asks about datasets, data loading, data preparation, or custom data formats.

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Guide for contributing to the tinker-cookbook repo — development setup, code style, type checking, PR process, and design conventions. Use when the user asks about how to contribute, set up the dev environment, code style, or project conventions.

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Guide for testing conventions and CI pipelines — unit tests, integration smoke tests, pytest markers, and GitHub Actions workflows. Use when the user asks about testing, CI, running tests, or adding tests for a recipe.

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Guide for using the Tinker Python SDK APIs — ServiceClient, TrainingClient, SamplingClient, RestClient, forward_backward, optim_step, sampling, and async patterns. Use when the user asks about Tinker API basics, how to call training/sampling, or how the SDK works.

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Guide for hyperparameter selection — learning rate formulas, LoRA rank, batch size, group size, schedules, and model-specific tuning. Use when the user asks about learning rate, batch size, hyperparameter tuning, or how to configure training parameters.

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Guide for the weight lifecycle — downloading trained weights from Tinker, merging LoRA adapters into HuggingFace models, and publishing to HuggingFace Hub. Use when the user asks about exporting, downloading, merging, or publishing trained model weights.

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Guide for using completers — TokenCompleter and MessageCompleter for text generation during RL rollouts and evaluation. Use when the user asks about generating text, completing messages, or using completers in RL environments.

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Guide for evaluation — inline evaluators, Inspect AI integration, and custom evaluators for measuring training progress. Use when the user asks about evaluation, metrics, benchmarks, or how to measure model quality during training.

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Guide for using renderers — the bridge between chat-style messages and token sequences. Covers renderer setup, TrainOnWhat, vision inputs, model family matching, and custom renderers. Use when the user asks about renderers, tokenization, message formatting, or vision inputs.

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Guide for defining RL environments — the Env protocol, EnvGroupBuilder, RLDataset, and custom environment creation. Use when the user asks about RL environments, reward functions, or how to define custom tasks for RL training.

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Guide for training outputs, metrics logging, logtree reports, tracing/profiling, and debugging training runs. Use when the user asks about training logs, metrics, debugging, tracing, profiling, timing, Gantt charts, or understanding training output files.

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Guide for installing Tinker, setting up the environment, getting an API key, and verifying everything works. Use when the user is getting started, setting up their environment, or troubleshooting installation issues.

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Guide for choosing models in Tinker — available model families, model types (base, instruction, reasoning, hybrid, vision), architecture (dense vs MoE), and how to match renderers to models. Use when the user asks which model to use, what models are available, or how to pick a model for their task.

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Guide for checkpointing — saving, loading, and resuming training with CheckpointRecord. Use when the user asks about saving weights, resuming training, checkpoint management, or the checkpoint lifecycle.

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Guides systematic root-cause debugging. Use when tests fail, builds break, behavior doesn't match expectations, or you encounter any unexpected error. Use when you need a systematic approach to finding and fixing the root cause rather than guessing.

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Behavioral guardrails for implementation work. Reduces common LLM coding mistakes: overcomplication, silent assumption-picking, scope drift, and unverifiable success criteria. Load on Builder and software-engineer-mode.

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Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations.

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Evaluates code against Apple's App Store Review Guidelines. Use this skill when reviewing iOS, macOS, tvOS, watchOS, or visionOS app code (Swift, Objective-C, React Native, or Expo) to identify potential App Store rejection issues before submission. Triggers on tasks involving app review preparation, compliance checking, or App Store submission readiness.

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Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.

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Guide for quality focused software architecture. This skill should be used when users want to write code, design architecture, analyze code, in any case that relates to software development.

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