「lean」の検索結果

Default material, information-density hierarchy, clean-rendering, negative-prompt hygiene, and evidence-preserving photo-repair layer for IM2 / GPT Image 2 / gpt-image-2 / image_gen. Use for clean generation/redraws, artifact cleanup, focal-detail control, atmospheric depth, physically coherent light paths, air scattering, volumetric light, light ratio, material realism, architecture/terrain/hard-surface scene mass and structural grounding, and 企业/公司活动/门店/团队合影/商品/餐饮/证件文档照片修复. Preserve identity, headcount, placement, product facts, text, logo, QR, date, price, and scene evidence; prefer residual blur over invented restoration detail.

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。

Formally verify programs by writing Lean 4 proofs. Trigger this skill whenever the user wants to formally verify code, generate Lean 4 proofs, prove properties about algorithms or smart contracts, verify invariants, convert program logic into formal specifications, or anything involving Lean 4 and formal verification. Also trigger when the user mentions "qedgen", "lean proof", "formal proof", "verify my code", "prove correctness", "formal verification", or wants mathematical guarantees about their implementation.

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。

Review code for readability, then simplify it to be easy to follow and understand. Use this skill when the user wants to simplify code, improve readability, clean up for handoff, make code skimmable, reduce complexity, refactor for clarity, strip boilerplate, prepare code for future maintainers, or make a codebase easier to pick up. Trigger liberally — any request touching readability, simplification, cleanup, or "make this easier to understand" should use this skill. Also trigger when the user says things like "review this", "clean this up", "tidy this", "make this simpler", "reduce complexity", "flatten this", "strip the noise", or "I want to understand this code quickly next time I open it".

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。

Inspects URL paths and proposes, tests, orders, and applies project-level path cleaning rules so dynamic segments (numeric IDs, UUIDs, slugs, dates) collapse into readable aliases. Use when the user says "clean the paths", "normalize URLs", "group similar pages", "too many distinct paths", "/users/123 and /users/456 are the same page", "set up path cleaning", or asks why a Web analytics or Paths breakdown is fragmented across thousands of nearly-identical URLs. Covers regex syntax (re2), alias placeholder convention, rule ordering, the test workflow, and applying rules via the project-settings-update MCP tool.

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。

Delete old snapshots from _Archive/Snapshots/. Accepts a day threshold (default 180). Also `daily-intel [days]` mode — prune auto-generated n8n scanning surfaces (news briefs, X dashboards) from Daily Intel/ older than 90 days. Use when user says "clean", "clean snapshots", "delete old snapshots", "clean daily intel", or when $lint flags stale snapshots.

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。

Delete old snapshots from _Archive/Snapshots/. Accepts a day threshold (default 180). Also `daily-intel [days]` mode — prune auto-generated n8n scanning surfaces (news briefs, X dashboards) from Daily Intel/ older than 90 days. Use when user says "clean", "clean snapshots", "delete old snapshots", "clean daily intel", or when /lint flags stale snapshots.

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。

Undo a haircut trim. Puts back the skills, commands, subagents, MCP servers, hooks, plugins and files that haircut parked into a dated bag, using the receipt it wrote at the time. Use this skill whenever the user says undo the cleanup, undo the haircut, put my config back, restore what was trimmed, I need that MCP server again, that skill is gone and I want it back, roll the trim back, what is in the bag, or which files were moved. Use it also when something stopped working right after a config cleanup and they suspect the cleanup caused it, and whenever they ask how to reverse a config cleanup they regret.

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。

現在のブランチの変更差分だけを点検し、冗長なコメントや不要な防御処理、型の強引な変換、周囲と合わない書き方を、動作を変えずレビュー前に整理するスキル。

• AI生成コードの冗長コメント整理
• 不要な防御処理や型変換の点検
• 変更箇所を周囲の書き方にそろえる

Google Drive から文書・表・スライドを探し、内容を確認して要約・編集・整理します。計画書や管理表の統合、重複や古い資料の見直しにも使えるスキル。

• 現行の計画書を探して要約したいとき
• 共有の顧客一覧を整理したいとき
• Google Slides への取り込みと整理

チームの開発に合うGitのブランチ運用を選び、コミットやプルリクエストの書き方、履歴の整理、変更の衝突解消、リリース管理まで手順を整えるスキル。

• 新規プロジェクトのブランチ運用選定
• コミット文とPR説明の統一
• PR前にローカルの履歴を整理したいとき

Claude Codeの設定を点検し、重複したスキルや古いメモリ、不要な権限などを整理します。候補ごとに確認し、元に戻せる形で無効化・退避するスキルです。

• 大量追加したスキルの重複を確認したいとき
• 古いメモリや不要なフックの点検
• 重複した権限設定の整理

Credit risk data cleaning and variable screening pipeline for pre-loan modeling. Use when working with raw credit data that needs quality assessment, missing value analysis, or variable selection before modeling. it covers data loading and formatting, abnormal period filtering, missing rate calculation, high-missing variable removal,low-IV variable filtering, high-PSI variable removal, Null Importance denoising, high-correlation variable removal, and cleaning report generation. Applicable scenarios arecredit risk data cleaning, variable screening, pre-loan modeling preprocessing.

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。

Clean up local git branches and remotes accumulated from PR reviews. Use when the user asks to clean branches, remove stale remotes, or tidy up the local git state.

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 a Lean Canvas with problem, solution, metrics, cost structure, UVP, unfair advantage, channels, segments, and revenue. Use when exploring a lean startup canvas, testing a business hypothesis, or modeling a new venture.

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。

Clean and finalize authorized natural-language text intended for readers by auditing suspicious invisible Unicode and rewriting prose while preserving facts, meaning, and the writer's voice. Use when the user asks to clean, humanize, polish, or finalize articles, manuscripts, reports, documentation, emails, product copy, UI text, Markdown, or HTML prose, or when a project rule or instruction file explicitly requires this workflow. Don't use for code-only tasks or undisclosed authorship evasion; leave code, commands, identifiers, paths, APIs, formulas, citations, required disclosures, and verbatim quotations unchanged.

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 adding or changing ClawHub Convex tables, TTL fields, cleanup crons, retention policy, auth/session cleanup, metric dedupe cleanup, or deprecated table removal

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。

Clean up Dropbox content by identifying obsolete, duplicate, temporary, or unwanted files and deleting only after explicit review. Use when the user explicitly asks to delete, remove, or clean up Dropbox files or folders.

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。

Deep-crawl any website from start URLs, return per-page LLM-ready text/markdown/HTML plus metadata (title, description, author, language, canonical URL, OG) and in-scope outbound links. Use when user mentions deep crawl website, recursive crawl, crawl a whole site, scrape entire website, scrape docs site, scrape documentation, scrape knowledge base, scrape blog, build RAG corpus, build vector database from website, knowledge base for chatbot, GPT knowledge files, llms.txt, sitemap crawl, BFS crawl, scrape with depth or page limit, include exclude URL globs, remove boilerplate, strip navigation header footer, website to markdown, website to text, multi-page extraction, bulk page scraping, clean markdown from URL, docs site to markdown corpus, site to clean corpus. Also applies to building RAG pipelines, indexing a customer site, syncing docs into a vector store, generating training corpora from any docs hub, or expanding a single start URL into a clean corpus of every reachable in-scope page.

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。

Cleans a Quarto (.qmd) file by replacing LaTeX holdovers with proper Quarto/Pandoc syntax so the document renders correctly to HTML, PDF (Typst/LaTeX), and Word. Fixes citations (\citep → [@key]), cross-references (\ref → @label), figures (\includegraphics → markdown), tables, text formatting (\textbf → **bold**), inline math (\( \) → $ $), hyperlinks (\href → [text](url)), footnotes (\footnote → ^[]), list environments, section headings, and R Markdown chunk options (dot-style → #| YAML). Use when asked to clean, fix, modernize, or port a Quarto or R Markdown document; when LaTeX commands appear in a .qmd file; when a document fails to render to Word or Typst; or when citations/cross-references do not appear in non-LaTeX output.

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。

Top Lean AI 榜单监控 - 监控 Top Lean AI 榜单的变化，记录更新历史，提供分析报告。

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。

Troubleshoots and debugs AWS Clean Rooms collaboration issues related to IAM roles, S3 bucket policies, KMS keys, Lake Formation permissions, and CloudWatch logging for custom ML model training and inference jobs. Use when a customer reports permission failures, access errors, or log publishing issues in Clean Rooms.

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。

Guided journey from a working-but-untested vibe-coded prototype to a production-ready product with tests, clean structure, a business-rules boundary, and resilience at scale. Orchestrates nine skills phase by phase - working-with-legacy-code, clean-code, refactoring-patterns, software-design-philosophy, clean-architecture, pragmatic-programmer, release-it, system-design, ddia-systems - asking the user questions at every decision point and recording results in the project docs/ folder (TESTING.md, TECH-DEBT.md, IMPROVE-CODE-QUALITY-PLAN.md) so the journey resumes across sessions. Use when the user wants to harden an AI-generated prototype, add tests before refactoring, make code safe to change, or says 'this works on my machine but I am scared to touch it'. For an aged codebase, remove-technical-debt; for greenfield structure, design-code-architecture; for a product and UX pass, improve-app; to optimize speed and structure, architecture-optimization. For one framework in isolation, invoke that skill directly.

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。

Apply named refactoring transformations to improve code structure without changing behavior. Use when the user mentions "refactor this", "code smells", "extract method", "replace conditional", "technical debt", "move method", "inline variable", "decompose conditional", or "clean up this messy code". Also trigger when cleaning up legacy code, preparing code for new features by restructuring, or identifying which transformation fits a specific code smell. Covers smell-driven refactoring, safe transformation sequences, and testing guards. For code-quality foundations, see clean-code. For managing complexity, see software-design-philosophy.

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。

Choose and audit startup metrics using Croll and Yoskovitz's "Lean Analytics". Use when the user mentions "what metrics should we track", "KPIs", "north star metric", "One Metric That Matters (OMTM)", "vanity metrics", "analytics dashboard", "DAU/MAU", "churn benchmark", or "measure product-market fit". Also trigger when choosing metrics for a startup or feature, auditing a dashboard for vanity metrics, setting metric targets and baselines, or instrumenting a product by business model and stage. Covers good-vs-vanity metrics, the One Metric That Matters, metrics by business model, the five startup stages, and benchmarks. For the build-measure-learn loop, see lean-startup. For fixing activation and retention, see improve-retention.

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。