Découvrez les skills Outils, comparez leurs dépôts et consultez leurs instructions.
Use when implementing in-app purchases, StoreKit 2 subscriptions, consumables, non-consumables, or transaction handling. Covers testing-first workflow with .storekit configuration, StoreManager architecture, an...
Use when writing tests with Swift Testing (@Test,
Use when implementing on-device AI with Apple's Foundation Models framework (iOS 26+), building summarization/extraction/classification features, or using @Generable for type-safe structured output.
Use when implementing gesture composition (simultaneous, sequenced, exclusive), adaptive layouts (ViewThatFits, AnyLayout, size classes), or choosing architecture patterns (MVVM vs TCA vs vanilla, State-as-Brid...
Use when implementing Network.framework connections (NWConnection, NetworkConnection), debugging connection failures, migrating from sockets/URLSession streams, or handling network transitions. Covers UDP/TCP p...
Use when building features with TCA (The Composable Architecture), structuring reducers, managing state, handling effects, navigation, or testing TCA features. Covers @Reducer, Store, Effect, TestStore, reducer...
Use when debugging NavigationStack issues (not responding, unexpected pops, crashes), build failures (SPM resolution, "No such module", hanging builds), or memory problems (retain cycles, leaks, deinit not call...
Use when encountering unfamiliar import statements, exploring dependency APIs, or when user asks "what's import X" or "what does X do". Generates on-demand API documentation for Swift package dependencies.
Guide for working with Splitrail's MCP server. Use when adding tools, resources, or modifying the MCP interface.
Reference for Splitrail's core data types. Use when working with ConversationMessage, Stats, DailyStats, or other type definitions.
Guide for updating model pricing in Splitrail. Use when adding new AI model costs or updating existing pricing data.
Guide for Splitrail's terminal UI and file watching. Use when modifying the TUI, stats display, or real-time update logic.
Guide for adding a new AI coding agent analyzer to Splitrail. Use when implementing support for a new tool like Copilot, Cline, or similar.
Performance optimization guidelines for Splitrail. Use when optimizing parsing, reducing memory usage, or improving throughput.
Agent for diagnosing and fixing flaky terminal UI tests in the Hex1b test suite. Use when tests pass locally but fail in CI, or when tests exhibit timing-sensitive behavior.
Diagnoses and fixes Earl problems including installation failures, template errors, authentication issues, MCP connectivity, and SSRF blocks. Use when Earl isn't working, when earl call returns errors, when MCP...
Analyze all failures in a convex-evals run, spawning parallel sub-agents to investigate each failure and producing a report with classifications and recommendations. Use when the user asks to analyze an entire...
Spec 创建前的信息收集与探索。由角色 spec-explorer 调用。 触发条件：(1) 角色 spec-explorer 需要在 Spec 创建前收集背景信息， (2) 需要检索历史经验（exp-search）， (3) 需要探索项目代码库、外部文档或第三方库， (4) T...
Create and edit Obsidian Flavored Markdown with wikilinks, embeds, callouts, properties, and other Obsidian-specific syntax. Use when working with .md files in Obsidian, or when the user mentions wikilinks, cal...
L1-L4 語法和結構驗證 - 檢查 Python 語法、AST 結構、縮進格式、命名規範。 BlueMouse 17-Layer Validation Group 1。 Triggers: "syntax", "格式檢查", "naming", "PEP8"
L9-L12 依賴關係驗證 - 檢查導入語句、標準庫、第三方庫、循環依賴。 BlueMouse 17-Layer Validation Group 3。 Triggers: "dependencies", "imports", "依賴檢查", "circular"
L5-L8 函數簽名驗證 - 檢查參數、返回值、類型提示、文檔字符串。 BlueMouse 17-Layer Validation Group 2。 Triggers: "signature", "參數檢查", "type hints", "docstring"
BlueMouse 17-Layer Code Validation System - Complete Python code quality validation covering syntax, structure, function signatures, dependencies, types, logic, security, and performance. Triggers: "validate",...
L13-L17 類型和邏輯驗證 - 檢查類型一致性、邏輯完整性、錯誤處理、安全性、性能。 BlueMouse 17-Layer Validation Group 4（最深層檢查）。 Triggers: "logic", "security", "performance", "error handling", "安全檢查"
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.