「cleaning」の検索結果

Knip dead code detection best practices for JavaScript and TypeScript projects. Use when configuring Knip, analyzing unused code, setting up CI integration, or cleaning up codebases. Triggers on knip.json, dead code, unused exports, unused dependencies, bundle optimization.

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。

Capture a solved, verified problem as a durable learning in `docs/solutions/` so the next agent or engineer does not repeat the mistake or redo the investigation, and keep the `CONCEPTS.md` glossary in step. Use after a fix, debugging session, or design decision whose reasoning is not readable from the final code, tests, or existing docs — "document this", "write up what we learned", "capture this learning", "add it to solutions", "that worked, save it for next time". Writes nothing for routine fixes the code already explains. For auditing or cleaning up existing learnings, use `learn-with-docs-refresh`.

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。

Label unlabeled GitHub issues with priority and type labels. Use when triaging issues, cleaning up the backlog, or when asked to label/triage issues.

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。

Rework a change as if the intended UX and architecture had existed from day one — deleting compatibility cruft, dead branches, and accidental complexity instead of patching around them. Use when refactoring, cleaning up after a feature lands, removing flags, collapsing legacy paths, or when the user says the code "should look like X from scratch".

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 this Canvas-owned clean-room skill when a spreadsheet file is the primary input or output. Trigger for `.xlsx`, `.xls`, `.xlsm`, `.csv`, `.tsv`, workbooks, templates, trackers, models, dashboards, charts, formulas, data cleaning, formatting, validation, recalculation, and Google Sheets-targeted handoff.

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。

Master orchestration for repository governance, North Star impact, sub-Star boundaries, and repair-first or evidence-first drift checks. Guides an agent through the complete lifecycle of making architectural decisions, documenting them, writing FAQs, and cleaning up stale plans while adhering strictly to repo ethics and brand tone. Use whenever you need to make a structural change, write an ADR, update the doc lattice, or govern repository architecture.

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。

Manage agent-skill lifecycles across local, global, project, Skill Hub, skills.sh, GitHub, plugin-cache, and system roots. Use when searching, recommending, installing, updating, upgrading, syncing, merging, auditing, deduplicating, cleaning, packaging, publishing, or validating skills; choosing project versus global scope; verifying upstream source, stars, or installs; or working with skill-upgrade, check_upstreams.ps1, sync-pull/push, junctions, skills-lock.json, or skill-upstreams.json. Do not use for ordinary software-package upgrades or non-skill file cleanup.

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。

Manage Agent Skills across Codex, Claude Code, local/global/project roots, Skill Hubs, GitHub, skills.sh, and managed plugin caches. Use when searching, installing, updating, upgrading, syncing, merging, auditing, deduplicating, cleaning, packaging, publishing, validating, resolving upstream sources, choosing runtime or scope, or working with skill-upgrade, check_upstreams.ps1, Claude Code plugins, skills-lock.json, or skill-upstreams.json. Do not use for ordinary software-package upgrades or non-skill file cleanup.

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。

SOP for auditing AI-generated code. Trigger when: - Reviewing, refactoring, or cleaning up AI-generated code to prevent regressions or hallucinated APIs. - Prompt contains: /ai-audit, code audit, AI cleanup, common flaws.

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。

Accessibility review for Vue surfaces — the code-cleaning analog of /code-review and /simplify, pointed at WCAG/ARIA. Reviews just your diff (or a package/file), rates findings by severity, and either comments inline on the PR (--comment) or applies the safe fixes for you (--fix). Steers the depth-aware `a11y` subagent. Use when asked to "check accessibility", "a11y this", "audit ARIA/keyboard", or run /a11y.

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Control MARS/Innate robots over rosbridge WebSocket. Covers connecting to the robot, executing skills, moving the arm, driving the base, tilting the head, and reading sensors — all via JSON-over-WebSocket to rosbridge_server on port 9090. Use this skill whenever working with MARS robots, Innate OS, rosbridge, ROS2 robot control, maurice_arm, brain_client, or any project that talks to a robot over WebSocket. Also trigger when seeing imports from brain_client, brain_messages, maurice_msgs, or references to topics like /cmd_vel, /mars/arm, /brain/chat_in, /execute_skill.

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 this skill any time a spreadsheet file is the primary input or output. Covers reading, editing, cleaning, modeling, formula repair, workbook generation, and converting tabular data into validated spreadsheet deliverables.

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。

Enable nullable reference types in a C# project and systematically resolve all warnings. USE FOR: adopting NRTs in existing codebases, file-by-file or project-wide migration, fixing CS8602/CS8618/CS86xx warnings, annotating APIs for nullability, cleaning up null-forgiving operators, upgrading dependencies with new nullable annotations. DO NOT USE FOR: projects already fully migrated with zero warnings (unless auditing suppressions), fixing a handful of nullable warnings in code that already has NRTs enabled, suppressing warnings without fixing them, C# 7.3 or earlier projects. INVOKES: Get-NullableReadiness.ps1 scanner script.

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。

Catalog of MSBuild anti-patterns with detection rules and fix recipes. Only activate in MSBuild/.NET build context. USE FOR: reviewing, auditing, or cleaning up .csproj, .vbproj, .fsproj, .props, .targets, or .proj files. Each anti-pattern has a symptom, explanation, and concrete BAD→GOOD transformation. Covers Exec-instead-of-built-in-task, unquoted conditions, hardcoded paths, restating SDK defaults, scattered package versions, and more. DO NOT USE FOR: non-MSBuild build systems (npm, Maven, CMake, etc.), project migration to SDK-style (use msbuild-modernization).

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。

Refactor and review SwiftUI view files with strong defaults for small dedicated subviews, MV-over-MVVM data flow, stable view trees, explicit dependency injection, and correct Observation usage. Use when cleaning up a SwiftUI view, splitting long bodies, removing inline actions or side effects, reducing computed `some View` helpers, or standardizing `@Observable` and view model initialization 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。

Refactor SwiftUI view files into stable, testable structure. Use when splitting large views, tightening data flow, or cleaning Observation ownership.

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。

Refactor Python code to improve quality while keeping functionality intact. Use when cleaning up complexity, removing duplication, or tightening structure.

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。

Generates standard operating procedures for B2B SaaS marketing operations: campaign naming conventions, UTM governance, tool administration, workflow QA, data hygiene, lead routing, and incident response playbooks. Use when setting up or auditing marketing ops processes, onboarding a new marketing ops hire, standardizing campaign tracking, fixing broken lead routing, cleaning up HubSpot or CRM data, building QA checklists for campaign launches, or creating incident response procedures for marketing system failures. Also use when asked to "fix our UTM mess," "standardize campaign naming," "build a QA checklist," "audit our marketing ops," or "create an ops runbook." Designed for B2B SaaS marketing teams running HubSpot with multi-geo, multi-segment operations.

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。

Identify and clean up stale feature flags in a PostHog project. Use when the user wants to find unused, fully rolled out, or abandoned feature flags, review them for safety, and then disable or delete them. Covers staleness detection, dependency checking, and safe removal workflows.

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。

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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Build AI data cleaning systems. TRIGGERS - Use when user needs help with ai-data-cleaner related tasks.

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。

集群数据管理 Skill —— 涵盖数据使用规范、压缩优化、数据共享（ACL）、文件扫描与清理、文件完整性校验、genozip 基因组压缩等全流程。Use when the user asks about: managing data on the HPC cluster, compressing genomic files (fq/bam/vcf), sharing data between users/groups, scanning for large files or cleaning up disk space, checking file integrity (md5/gzip/bam), using genozip, checking disk quota, or any data lifecycle management task on the NCPGR HPC cluster.

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Organize, categorize, rename, and manage files systematically using automated rules, naming conventions, and folder structures for efficient file management. Use when organizing uploaded files, implementing file naming conventions, categorizing files by type or metadata, creating folder structures, cleaning up messy directories, automating file movements, implementing media libraries, or building file management systems.

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。

Triage application logs, open GitHub issues, pending feedback, in-app Issues, and agent conversations. Runs five phases: logs → close shipped issues → feedback → in-app issues → agent FAQ gaps. Use when /whats shows pending feedback, checking app logs, cleaning up shipped issues, or working the in-app Issues backlog.

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。