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概要と使いどころ
文本内部重复片段模糊清理工具。读取 JSONL 文本数据，按文本字段删除近似重复的段落、句子或连续片段，保留首次出现内容并输出清理后的样本。 当用户提到清理重复片段、删除重复段落、去掉近似重复内容等需求时使用此 skill。 即使用户没有明确说出 "duplicate_fragment_cleaner"，只要任务涉及同一文本内部的重复片段清理，就应该使用此 skill。 不负责跨记录去重、摘要改写或语义重写。
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。
Post-implementation cleanup that removes AI-generated bloat while preserving functionality. Runs pass-by-pass with test verification after each pass. Activate after kraken/spark complete a feature, or when a codebase needs hygiene 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。
Extract clean markdown or text content from specific URLs via the Tavily CLI. Use this skill when the user has one or more URLs and wants their content, says "extract", "grab the content from", "pull the text from", "get the page at", "read this webpage", or needs clean text from web pages. Handles JavaScript-rendered pages, returns LLM-optimized markdown, and supports query-focused chunking for targeted extraction. Can process up to 20 URLs in a single call.
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 the free official JetBrains ReSharper Command Line Tools for .NET repositories. USE FOR: `jb inspectcode`; `jb cleanupcode`; stronger C# inspections, cleanup profiles, and CI-friendly JetBrains analysis. DO NOT USE FOR: replacing tests with inspection output; ad-hoc formatting-only work when the repo intentionally standardizes on another formatter. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.
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。
ALWAYS USE when asked to fix, rewrite, update, improve, modernize, show corrected code for, or explain existing MSTest tests or MSTest-specific configuration. Use for "review" when corrected code or edits are wanted, even for one pasted assertion or passing tests with bad failure output. Covers expected/actual labels; generic Boolean, collection, string, numeric, null, identity, exception, hard-cast, and object[] checks; TestContext/lifecycle; timeout/cancellation; OS/CI conditions, retry, cleanup, parallelization, MSTest.Sdk project setup, and MSTESTxxxx. Honor the installed MSTest version. DO NOT USE to design new test cases (code-testing), perform report-only audits, create project files rather than explain MSTest setup, run tests, migrate frameworks, or handle non-MSTest/non-.NET code.
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。
Design or review .NET solution architecture across modular monoliths, clean architecture, vertical slices, microservices, DDD, CQRS, and cloud-native boundaries without over-engineering. USE FOR: .NET architecture choices; layer and domain boundary review; service decomposition; clean architecture, vertical slice, DDD, CQRS, and modular monolith decisions. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.
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 before answering, planning, or editing any MSTest 3.x-to-4.x upgrade or post-upgrade failure. Triggers include "MSTest v4 breaking changes"; CS0507/CS0103/CS1061/CS1615; ExecuteAsync, CallerInfo, DisplayName, or custom TestMethodAttribute; ClassCleanupBehavior; ContainsKey; ThrowsExactly or ExpectedException; IsInstanceOfType out parameters; TestTimeout.Infinite; ManagedType; net6/net7 compatibility; TestCase.Id history; TestName in ClassInitialize; TreatDiscoveryWarningsAsErrors; discovery errors after a clean build; and MSTest.Sdk/MTP or vstest.console discovery changes. Do not use for v1/v2-to-v3 leftovers, framework conversion, runner-only migration, or a general .NET upgrade.
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。
Cut token usage across the whole session without losing correctness or substance — concise user replies, lean tests/docs/plans/comments, tight code, disciplined context and tool use, and cheap sub-agent delegation. Load when the user wants less verbosity, lower cost, fewer tokens, or a "token diet" / "lean mode"; also triggered by /token-diet or "token diet on".
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。
Restructure or clean up code with no behavior change, proved by before-and-after checks. Use when: asked to clean up, extract, dedupe or simplify, even one function.
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, implementing, reviewing, testing, or cleaning up feature toggles, feature flags, kill switches, runtime configuration gates, canary controls, staged rollouts, experiments, or temporary compatibility switches in Java enterprise systems. This should trigger for requests such as Design a feature toggle strategy; Review this feature flag; Add a kill switch safely; Test toggle on and off paths; Clean up an expired feature toggle; Plan controlled rollout and rollback behavior. Part of Plinth Toolkit
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。
Remove AI-generated code slop from branches. Use after AI-assisted coding sessions to clean up defensive bloat, unnecessary comments, type casts, and style inconsistencies. Focuses on identifying and removing AI artifacts that degrade code quality.
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. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
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 the improvements backlog at .turbo/improvements.md: merge duplicate entries, state the user story or simplification behind each one, and decide with the user which to keep and which to drop. Use when the user asks to "triage improvements", "triage the backlog", "clean up the backlog", "clean up improvements", "groom the backlog", "prune the improvements backlog", or "go through the backlog with me".
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 a multi-agent review of code comments and markdown documentation for unnecessary content, then fix the issues. Covers comments that misdescribe the code, what-restating comments, name-mirroring doc comments, status-update prose, and other documentation noise. Use when the user asks to "simplify docs", "simplify documentation", "clean up comments", "clean up docs", "review documentation", "strip unnecessary comments", "fix stale comments", "reduce doc noise", or "run simplify-docs".
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。
Stops an agent from destroying uncommitted work with git. Requires git status and git diff before any command that discards changes, treats "revert" and "undo" as manual edits, and never runs reset, checkout, clean, stash drop, or force push without explicit approval. Use when a task involves reverting, undoing, resetting, cleaning, rebasing, or force pushing, or before any git command in a repo with uncommitted 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。
Minimal / clean / Linear / Notion / Vercel / whitespace-heavy UIs. Locked knobs: CRAFT=8, MOTION=3, DENSITY=2. Monochrome + one accent, Inter/Geist, hairline borders over shadows. Trigger on: minimal, clean, Linear-like, Notion-like, Vercel-like, whitespace-heavy.
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。
Prepare PRs for review by cleaning noisy history, improving PR descriptions, and adding reviewer guidance without changing code behavior. Use for "make this easy to review", "tidy this PR", "clean up commits", or "annotate the diff".
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。
Applies principles from Robert C. Martin's 'Clean Code'. Use this skill when writing, reviewing, or refactoring code to ensure high quality, readability, and maintainability. Covers naming, functions, comments, error handling, and class design.
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。
Provides implementation patterns for Clean Architecture, Hexagonal Architecture (Ports & Adapters), and Domain-Driven Design in Python applications with FastAPI or Flask. Use when designing maintainable backends with separation of concerns, implementing repository patterns, creating entities/value objects/aggregates, or structuring domain logic independent of frameworks for testability.
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。
Provides implementation patterns for Clean Architecture, Domain-Driven Design (DDD), and Hexagonal Architecture (Ports & Adapters) in NestJS/TypeScript applications. Use when structuring complex backend systems, designing domain layers with entities/value objects/aggregates, implementing ports and adapters, creating use cases, or refactoring from anemic models to rich domain models with dependency inversion.
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。
Provides implementation patterns for Clean Architecture, Hexagonal Architecture (Ports & Adapters), and Domain-Driven Design in PHP 8.3+ with Symfony 7.x. Use when architecting enterprise PHP applications with entities/value objects/aggregates, refactoring legacy code to modern patterns, implementing domain-driven design with Symfony, or creating testable backends with clear separation of concerns.
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。
Provides implementation patterns for Clean Architecture, Hexagonal Architecture (Ports & Adapters), and Domain-Driven Design in Java 21+ Spring Boot 3.5+ applications. Use when structuring layered architectures, separating domain logic from frameworks, implementing ports and adapters, creating entities/value objects/aggregates, or refactoring monolithic codebases for testability and maintainability.
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。
Applies KISS, YAGNI, and SOLID principles for clean code with reduced complexity. Use when refactoring or reviewing code for over-engineering.
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。
Routes cleanlab data-centric AI workflows for finding label errors, dataset issues, outliers, annotator quality, and task-specific label-quality problems.
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。