「clean」の検索結果

Programmatically clear WP Rocket cache from a third-party plugin / theme when data changes — the public rocket_clean_* function family (rocket_clean_post, rocket_clean_files, rocket_clean_term, rocket_clean_user, rocket_clean_home, rocket_clean_minify, rocket_clean_cache_busting, rocket_clean_domain, rocket_clean_cache_dir). Critical detection rule — WP Rocket is a PAID plugin not on Packagist; always feature-detect via function_exists('rocket_clean_post') OR defined('WP_ROCKET_VERSION') before calling, since not every site has it. Never raw-unlink the cache directory or call wp_cache_flush() expecting it to clear WP Rocket — wp_cache_flush is WP object cache, WP Rocket is FILE cache. The before_*_clean_* / after_*_clean_* action lifecycle hooks fire around every clean — useful for audit logging, monitoring, custom invalidation chains. Use when integrating cache invalidation in a companion plugin, WC integration, custom data plugin. Triggers on rocket_clean_, before_rocket_clean, af...

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Find and identify the CCSGameRules_PostCleanUp function in CS2 server binary using IDA Pro MCP. Use this skill when reverse engineering CS2 server.dll or libserver.so to locate the post-round entity-cleanup routine. It is non-virtual and has no unique string of its own, so it is found via the CCSGameRules_ResetRound predecessor: PostCleanUp is the entity-cleanup call ResetRound makes. Emits a unique IDA-style byte signature and a func YAML. Mirrors the find-CCSGameRules_PostCleanUp.py preprocessor (LLM_DECOMPILE CCSGameRules_ResetRound). Trigger: CCSGameRules_PostCleanUp, PostCleanUp, CGameRules::PostCleanUp

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Remove AI-generated code smells (slop) from branch changes or an explicit file list. Locks behavior with regression tests FIRST, then runs categorized cleanup via parallel `deep` agents in batches of 5, then verifies with quality gates. Covers 10 slop categories including performance equivalences, excessive complexity (object annotations, if/elif variant chains), and oversized modules (250+ pure LOC with mandatory modular refactoring). MUST USE when the user asks to "remove slop", "clean AI code", "deslop", "clean up AI-generated code", "remove AI slop", or wants to clean up AI-generated patterns from recent changes. Triggers - "remove ai slops", "clean ai code", "deslop", "cleanup AI generated", "remove AI slop", "clean up AI-generated code", "strip slop", "ai-slop cleanup".

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Use when the user wants to simplify, clean up, refactor, tidy, or refine code — their staged/unstaged git changes or a target file/path. Reduces complexity, flattens nesting, removes redundancy and dead code, scores each change by value (holding low-value churn), then runs tests to prove nothing broke. Invoke on: "simplify", "simplify this", "simplify my code/changes", "clean up", "clean this up", "clean up my changes", "refactor this", "make this cleaner", "tidy this up", "reduce complexity", "flatten this", "remove dead code", "make it more readable", "polish before commit", or "absolute simplify". Acts on your working diff; for repo-wide dead code use absolute-prune; for lint/type debt use absolute-debt.

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AWS Cost Cleanup workflow skill. Use this skill when the user needs Automated cleanup of unused AWS resources to reduce costs and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Use when asked to run /clean-clean-cut to cut accumulated records and residue. Not for untracked or non-VCS changes, or branch/worktree cleanup: use git-cleanup.

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Clean up merged feature branches after PR to main is merged. Use when the user says "ブランチ削除", "cleanup", "マージ後の片付け", or wants to delete a merged branch.

Facilitate a structured conversation to define clean code principles for a repository. Produces a formal clean-code.md document that the clean-code atom will use as its override. Use when setting up coding standards, defining code quality rules, or when the user says 'setup clean code', 'define coding standards', 'code quality principles', 'coding guidelines', or 'help me define my code standards'.

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Systematic code cleanup and refactoring agent that identifies and fixes code quality issues without changing functionality. Focuses on: - Dead code removal - Import organization - Type improvements - Consistent patterns - File organization Use when "User asks to "clean up" code, User mentions "dead code" or "unused", User wants to "organize imports", User asks about "code quality", User wants to "refactor" without changing behavior, Code review identifies cleanup items, After major feature completion, cleanup, refactoring, code-quality, maintenance, organization" mentioned.

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AWS Cost Cleanup workflow skill. Use this skill when the user needs Automated cleanup of unused AWS resources to reduce costs and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Use when safely removing dead frontend code, unused exports, stale components, obsolete routes, unused dependencies, or cleanup targets found by tools such as knip, depcheck, ts-prune, TypeScript, ESLint, or manual review. Do not use for feature rewrites that change behavior; Chinese triggers include dead code, clean up unused, refactor clean, dependency cleanup, delete useless code.

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Use when executing admin-level cleanup in a Salesforce org: deleting unused fields, removing inactive Flow versions, deactivating legacy automation, running Salesforce Optimizer or Org Check, and performing destructive metadata deploys. Triggers: 'clean up unused fields', 'delete inactive flows', 'run Optimizer', 'org has too many custom fields', 'remove old workflow rules', 'Flow version limit'. NOT for assessing or reporting on technical debt (use architect/technical-debt-assessment). NOT for code-level refactoring or Apex cleanup (use apex/ skills).

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Deep codebase cleanup — kills dead code, fixes all lint/format warnings, removes orphaned files, cleans stale TODOs, strips security hazards, tightens TypeScript strict mode, and organizes imports. Triggers: clean up, dead code, unused imports, lint, technical debt cleanup.

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Clean up the local workspace after a dev branch has been merged into main. Verifies the current dev branch is fully merged into origin/main, then switches to main, pulls the latest, deletes the local dev branch, and deletes the remote dev branch if it still exists. STOPS and warns the user if the current branch is not yet merged. Triggers: cleanup workspace, cleanup branch, delete merged branch, finish dev branch, post-merge cleanup

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リモート環境やDockerで、導入・連携・実操作のテストを進めるスキル。コードの信頼性に応じた環境の選択から、障害調査と実行後の後片付けまで扱います。

• クリーン環境でのパッケージ導入テスト
• 実サービスとの連携を検証したいとき
• macOS・Windows・WSL2の動作確認

AndroidやKMPのモジュール構成と依存関係を整理し、業務処理・画面・データ取得を分けて設計するスキル。実装例をもとに各層の役割を明確にします。

• AndroidやKMPの構成を決めたいとき
• 画面から業務処理を分けたいとき
• 端末とサーバーのデータ取得設計

Clean up messy spreadsheet data — trim whitespace, fix inconsistent casing, convert numbers-stored-as-text, standardize dates, remove duplicates, and flag mixed-type columns. Use when data is messy, inconsistent, or needs prep before analysis. Triggers on "clean this data", "clean up this sheet", "normalize this data", "fix formatting", "dedupe", "standardize this column", "this data is messy".

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AI audio editing pipeline: Whisper word-level transcription → Claude segment classification (KEEP/CUT_FILLER/CUT_FALSE_START/CUT_STUTTER/CUT_DEAD_AIR) → ffmpeg with 40ms qsin crossfades and room-tone fill → optional Cleanvoice cloud polish; plus GateScan/GateRepair for noise-gate ticking artifacts. Modes: --preview, --aggressive, --polish. Workflow: Clean. USE WHEN clean audio, edit audio, remove filler words, clean podcast, remove ums, cut dead air, polish audio, trim recording, cut stutters, ticking audio, clicking audio, audio clicks, gate artifacts, popping audio. NOT FOR video composition (use Remotion).

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[omh] Messy or AI-generated code to clean up: delete AI-generated slop, dead code, and duplication while observable behavior stays identical. Use when the user says: ai-slop-cleaner, cleanup, deslop, refactor, risky, behavior-preserving refactor, risk analysis, refactor workflow.

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Write readable, maintainable code through disciplined naming, small functions, and clean error handling. Use when the user mentions "clean up this code", "this function is too long", "code smells", "naming conventions", "boy scout rule", "single responsibility", or "unit test quality". Also trigger when reviewing a pull request for readability, untangling a messy function, debating comment styles, or improving error-handling patterns. Covers SRP, comment discipline, formatting, and unit testing. For refactoring techniques, see refactoring-patterns. For architecture and dependency rules, see clean-architecture.

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Use when targeting the Journal of Cleaner Production or deciding whether a cleaner-production/sustainability manuscript fits this venue. Encodes the journal's fit, the cleaner-production contribution and methodological-transparency bar, LCA/system-boundary expectations, house style, official-submission re-check, and desk-reject heuristics.

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Identify stale and empty Claude Code sessions in the Agent Monitor and explain the cleanup endpoint (POST /api/settings/cleanup), always showing the exact list of what WOULD be removed before anything is deleted. Cleanup permanently deletes data, so this skill previews first and requires explicit user confirmation. Use when tidying the monitoring database.

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Clean and normalize A-share financial data for modeling. Adapts the original clean-data-xls skill for Chinese financial statements, CAS conventions, and A-share data formats. Triggers on "A股数据清洗", "财务数据清洗", "clean financial data China", "数据清洗", or "normalize financial statements".

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Clean-code engineering standards for writing, refactoring, and reviewing code in any programming language. Use this whenever the user asks to write clean code, follow clean-code principles, refactor for clarity, improve naming, reduce complexity or duplication, separate concerns, tighten error handling, work test-first or do TDD, or otherwise raise code quality, readability, and maintainability. Also use when writing or reviewing Operately APIs or Ecto queries. Apply these rules by default when producing or changing code for a quality-conscious 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。