「lean」の検索結果

The standing order for local git state in every AI Matrx repo — zero worktrees, zero local branches, zero dirty files, zero open PRs, everything merged to origin/main, and no code ever lost. Use when you find a worktree, a local branch, a stale checkout, an unpushed commit, or a dirty tree in any repo under /Users/armanisadeghi/code, when asked to "clean up", "unify", "merge everything", or when you are tempted to create a worktree or branch. Works without any access to Arman.

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Safe removal of a duplicate component in favor of the one being kept. Use when asked to remove a component in favor of another, eliminate duplicates, reduce two versions of something to one, or clean up redundant UI components.

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Find and identify the CCSGameRules_ResetRound function in CS2 server binary using IDA Pro MCP. Use this skill when reverse engineering CS2 server.dll or libserver.so to locate the round-reset routine. It is the only function that references the "GMR_ResetRound\n" debug log string, and is the predecessor anchor for find-CCSGameRules_PostCleanUp (which it calls). Emits a unique IDA-style byte signature and a func YAML. Mirrors the find-CCSGameRules_ResetRound.py preprocessor (xref_strings "GMR_ResetRound\n"). Trigger: CCSGameRules_ResetRound, ResetRound, GMR_ResetRound

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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.

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このマシンに溜まった Claude Code の履歴・痕跡・キャッシュを，何を消すか列挙して承認を得たうえで消し，ログイン・設定・導入済みプラグインは残す．「Claude Code の履歴を消して」「痕跡を掃除して」など，Claude Code がローカルに残したものの削除を明示的に求められたときだけ使う——「クリーンアップして」等の一般の整理の依頼では発動しない．実行中ジョブのログを見たいだけなら /ops:progress を使う．

AndroidおよびKotlin Multiplatformプロジェクトのためのクリーンアーキテクチャパターン — モジュール構成、依存関係ルール、UseCase、Repository、データレイヤーパターン

Elite website image-to-code skill for Codex. For visually important web tasks, it must first generate the design image(s) itself, deeply analyze them, then implement the website to match them as closely as possible. In Codex, it must prefer large, readable, section-specific images instead of tiny compressed boards, generate fresh standalone images for sections or detail views instead of cropping old ones, avoid lazy under-generation, avoid cards-inside-cards-inside-cards UI, and keep the hero clean, spacious, readable, and visible on a small laptop.

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Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.

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Clean editorial-style interfaces. Warm monochrome palette, typographic contrast, flat bento grids, muted pastels. No gradients, no heavy shadows.

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[71] CLOSE. Quick, safe cleanup after completing a milestone. Fix objective issues only (syntax errors, dead code, poor naming). Must be <5% of main task time, <30 seconds per fix, and reversible. Use after key points, not after every small change.

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[52] EXECUTE. Three-stage refactoring workflow: (1) iterative research of refactor/modularization options, (2) plan + risk/edge-case analysis + Scope150 validation, then implement with tests after user confirmation, and (3) apply Scout105 cleanup protocol. Use when asked to refactor, modularize, or restructure code safely.

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。

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across 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。

A hard-edged writing style contract for timeless, forceful English prose without modern AI tics. Use when users ask for prose or rewrites that must be clean, exact, concrete, and free of AI cadence, filler, or therapeutic tone.

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Build production-grade Azure Cosmos DB NoSQL services following clean code, security best practices, and TDD principles.

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Master proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design to build maintainable, testable, and scalable systems.

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Fetch any X/Twitter post as clean LLM-friendly JSON. Converts x.com, twitter.com, or adhx.com links into structured data with full article content, author info, and engagement metrics. No scraping or browser required.

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Normalizes GitHub issue and PR titles by removing any bracketed [NemoClaw] tag case-insensitively, even when the tag appears later in the title. Use when cleaning issue tags, bulk-renaming titles, or normalizing repo title hygiene.

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Use this skill when implementing, refactoring, or extending the internal table runtime inside this repository. Covers the current architecture layers, state model, composable flow, where different responsibilities live, how to add features cleanly, and what kinds of refactors are encouraged.

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Use this skill for internal Vue reactivity and state-model work in the nuxt-ui-tools repository. Covers lean reactivity, query-state integration, when to use computed vs watch vs shallowRef, VueUse usage, and how to simplify over-layered state models.

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Use this skill when importing Excel, CSV, or pasted rows with nuxt-ui-tools as a package consumer. Covers defineSpreadsheetSchema (typed context, chained text/number/date/boolean/select columns, columns depending on the columns above (options, defaults, rules per row), groups and dynamic groups built from the context, exact matching, option sources shared with forms, unknown-value policies, rules and levels, validate, row identity with duplicates and stored records, output), useSpreadsheetImport (headless models, onSubmit with batches and rejections), the UiSpreadsheetImport* parts, useSpreadsheetSteps with custom steps, and the ready-made UiSpreadsheetImport wizard.

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GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.

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High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.

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Manage Docker containers, images, volumes, networks, and Compose stacks — lifecycle ops, debugging, cleanup, and Dockerfile optimization.

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Operate and maintain OpenViking server - configure, install, start, stop, and cleanup the server. Use when need to setup or manage OpenViking service deployment.

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。