4,957 件 ・ 関連度順
概要と使いどころ
Pre-commitment attack on a plan by an agent that did not write it. Use when presenting a feature-deep-dive plan, assigning vision-to-fleet briefs, moving a register to build mode, ratifying a scope list, or coding a new contract or table. NOT for built work or DONE reports (use subagent-dispatch).
日本語の概要は準備中です。原文の説明を表示しています。
The procedure for every doc change in common-docs or a repo's CLAUDE.md, FEATURE.md or SKILL.md. Use when creating, editing, moving or deleting a doc, finding duplicate or disagreeing docs, planting a repo pointer, consolidating a node, or on /docs and the daily docs-steward run.
日本語の概要は準備中です。原文の説明を表示しています。
Retiring a dead table by moving it to the deprecated schema and clearing every reference in both repos. Use when asked to deprecate or retire a table, when a table is no longer used, or to take a table offline without dropping it. NOT for relocating a still-used table (use db-move-table-schema).
日本語の概要は準備中です。原文の説明を表示しています。
Diagnostic method for any bug, failure, regression, flake, or slowdown. Use before proposing a fix: something is broken, throwing, not saving, not showing, or 'worked yesterday'; on 'debug this' or 'find the root cause'; and immediately after a fix attempt did not work.
日本語の概要は準備中です。原文の説明を表示しています。
Locate CCSPlayerInventory::GetItemInLoadout in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the WeaponPaints gamedata entry "CCSPlayerInventory::GetItemInLoadout" (symbol CCSPlayerInventory_GetItemInLoadout). Non-virtual inventory method resolving a player's loadout item. Shortlist via callers inside CCSPlayerInventory code that walk the loadout item cache (m_pSOCache / CGCClientSharedObjectCache), and via the CSO/econ loadout message paths. The function takes an item definition index + loadout slot and returns the matching CEconItemView — the body iterates a small item list comparing definition indices and loadout slots. Trigger: CCSPlayerInventory_GetItemInLoadout, CCSPlayerInventory::GetItemInLoadout
日本語の概要は準備中です。原文の説明を表示しています。
Convert an existing find-XXXX SKILL.md into a preprocessor Python script, updating configs/<GAMEVER>.yaml and removing the old SKILL.md. Covers xref-string-based and LLM_DECOMPILE-based discovery patterns.
日本語の概要は準備中です。原文の説明を表示しています。
React composition patterns that scale. Use when refactoring components with boolean prop proliferation, building flexible component libraries, or designing reusable APIs. Triggers on tasks involving compound components, render props, context providers, or component architecture. Includes React 19 API changes.
日本語の概要は準備中です。原文の説明を表示しています。
Apple Human Interface Guidelines for iPad. Use when building iPad-optimized interfaces, implementing multitasking, pointer support, keyboard shortcuts, or responsive layouts. Triggers on tasks involving iPad, Split View, Stage Manager, sidebar navigation, or trackpad support.
日本語の概要は準備中です。原文の説明を表示しています。
Apple Human Interface Guidelines for Mac. Use when building macOS apps with SwiftUI or AppKit, implementing menu bars, toolbars, window management, or keyboard shortcuts. Triggers on tasks involving Mac UI, desktop apps, or Mac Catalyst.
日本語の概要は準備中です。原文の説明を表示しています。
Apple Human Interface Guidelines for iPhone. Use when building, reviewing, or refactoring SwiftUI/UIKit interfaces for iOS. Triggers on tasks involving iPhone UI, iOS components, accessibility, Dynamic Type, Dark Mode, or HIG compliance.
日本語の概要は準備中です。原文の説明を表示しています。
Apple Human Interface Guidelines for Apple Watch. Use when building watchOS apps, complications, or workout features. Triggers on tasks involving Watch UI, Digital Crown, glanceable interfaces, or wrist-based interactions.
日本語の概要は準備中です。原文の説明を表示しています。
Apple Human Interface Guidelines for Apple Vision Pro. Use when building spatial computing apps, implementing eye/hand input, or designing immersive experiences. Triggers on tasks involving visionOS, RealityKit, spatial UI, or mixed reality.
日本語の概要は準備中です。原文の説明を表示しています。
Generative Engine Optimization for AI search engines (ChatGPT, Claude, Perplexity). Use when improving how content is discovered and cited by AI assistants.
日本語の概要は準備中です。原文の説明を表示しています。
Use when designing or improving prompts, commands, hooks, skills, or sub-agent instructions, including output quality, reliability, and token efficiency.
日本語の概要は準備中です。原文の説明を表示しています。
Collect and submit developer feedback to the LiveAvatar team. Use when: (1) A LiveAvatar integration is done, working, finished, or complete, (2) The user says "it works", "we're done", "that's it", "ship it", (3) The user is frustrated, stuck, or giving up — "this is broken", "this doesn't work", "I'm stuck", "I give up", "this is frustrating", "I want to report an issue", (4) The user explicitly asks to give feedback, report a bug, file an issue, or share a suggestion about LiveAvatar, (5) You've seen 3+ failed attempts at the same integration step, (6) You just finished writing or generating LiveAvatar integration code for the user.
日本語の概要は準備中です。原文の説明を表示しています。
Enables Claude Code to collaborate with OpenAI Codex CLI on Windows. Use this skill when the user wants to get a second opinion from Codex, compare approaches between Claude and Codex, or leverage both AI assistants for collaborative problem-solving. This skill supports both non-interactive mode (automatic response retrieval) and interactive mode (visual pane splitting with tmux).
日本語の概要は準備中です。原文の説明を表示しています。
Generate blog articles for Qiita and Zenn from GitHub release information. Use when creating release notes or technical articles that follow specific markdown formats with frontmatter. Supports generating articles from release data, creating custom formatted articles, and saving articles to specified repositories for both Qiita and Zenn platforms
日本語の概要は準備中です。原文の説明を表示しています。
Rebuild a degraded AI image to remove crunchy textures and repeated-edit artifacts while preserving identity, style, and composition. Use for clean remakes, not ordinary retouching or upscaling.
日本語の概要は準備中です。原文の説明を表示しています。
Run convex-doctor static analysis, interpret findings, and fix issues across security, performance, correctness, schema, and architecture categories. Use when running convex-doctor, fixing convex-doctor warnings or errors, improving the convex-doctor score, or when asked about Convex code quality, static analysis, or linting Convex functions.
日本語の概要は準備中です。原文の説明を表示しています。
Tones down visually aggressive or overstimulating designs, reducing intensity while preserving quality. Use when the user mentions too bold, too loud, overwhelming, aggressive, garish, or wants a calmer, more refined aesthetic.
日本語の概要は準備中です。原文の説明を表示しています。
Strip designs to their essence by removing unnecessary complexity. Great design is simple, powerful, and clean. Use when the user asks to simplify, declutter, reduce noise, remove elements, or make a UI cleaner and more focused.
日本語の概要は準備中です。原文の説明を表示しています。
Reflection-first problem solving methodology for Convex development. Use before implementing any solution to ensure proper root cause analysis, 98% code confidence, and minimal change scope.
日本語の概要は準備中です。原文の説明を表示しています。
Knowledge base for Claude Code formats and patterns. Use when creating or improving commands, agents, skills, or hooks.
日本語の概要は準備中です。原文の説明を表示しています。
When the user requests information about academic benchmarks, datasets, or research papers, particularly in machine learning, deep learning, or logical reasoning domains. This skill enables systematic research of academic benchmarks by searching web sources, downloading and analyzing arXiv papers, extracting key metadata (number of tasks, training availability, difficulty levels), and compiling comparative summaries. It triggers on requests involving dataset comparisons, benchmark analysis, or academic paper research for table creation.
日本語の概要は準備中です。原文の説明を表示しています。