「lab」の検索結果

Master REST and GraphQL API design principles to build intuitive, scalable, and maintainable APIs that delight developers and stand the test of time.

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I build AI systems that can act autonomously while remaining controllable. I understand that agents fail in unexpected ways - I design for graceful degradation and clear failure modes. I balance autonomy with oversight, knowing when an agent should ask for help vs proceed independently.

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Convert human-written CLAUDE.md into AI-native structured-label format. Battle-tested across 4 models. Same rules, fewer tokens, higher compliance.

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bu skill covers integrating OWASP ZAP (Zed Attack Proxy) for Dynamic Application Security Testing in CI/CD pipelines. It addresses configuring baseline, full, and API scans against running applications, interpreting ZAP Bul:ings, tuning scan policies, and establishing DAST quality gates in GitHub Actions and GitLab CI.

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Automates SOC 2 Type II audit preparation including gap assessment against AICPA Trust Services Criteria (CC1-CC9), evidence collection from cloud providers and identity systems, control testing validation, remediation tracking, and continuous compliance monitoring. Covers all five TSC categories (Security, Availability, Processing Integrity, Confidentiality, Privacy) with automated evidence gathering from AWS, Azure, GCP, Okta, GitHub, and Jira. Use when preparing for or maintaining SOC 2 Ty...

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, triage, label, assign GitHub issues via gh or REST.

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Drive the macOS desktop in the background — screenshots, mouse, keyboard, scroll, drag — without stealing the user's cursor, keyboard focus, or Space. Works with any tool-capable model. Load this skill whenever the `computer_use` tool is available.

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Deploy a new LLM model via LiteLLM (GitOps) and/or Azure (Terraform + node backend), add it to the ai toolkit, create the required pull requests, and verify end-to-end. Use when rolling out a model to any environment, adding a model to the toolkit, troubleshooting "model not available" errors, or finding pricing/token limits/model ids for any provider.

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授業のシラバスに生成 AI の使用ルールを書き込みたい、学生に AI 使用可否と範囲を明示したい、 学科で AI 使用の共通テンプレートを作りたい、同僚教員から「うちの授業でどう書けば」と聞かれた、 教務委員会でシラバス記載ルールを議論する必要がある場面で使うシラバス AI 方針設計スキル。 4 段階テンプレート（自由／場面限定／完全禁止／その他）と科目類型別選択肢カタログで、 教員が 10 分で決められる判断フローを提供する。

Create a construction plan for a multi-step engineering task that requires multiple PRs or a phased approach. Produces a self-contained plan file with dependency graph, review gates, and step-by-step execution instructions for coding agents. TRIGGER when: user requests a plan, blueprint, or roadmap for a complex task, or describes work that clearly needs multiple PRs. DO NOT TRIGGER when: task is completable in a single PR or fewer than 3 tool calls, user asks to modify/update an existing plan, user says "just do it", or user asks for code review/audit.

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Design and create Busabase-backed App-in-Skill packages with a canonical app project, Busabase-native setup and product onboarding, review/execution workflows, a standardized responsive UI, and AirApp-first delivery. Use when a user wants a Busabase research desk, review queue, planner, action console, operating dashboard, control panel, collaboration workspace, onboarding/readiness flow, or an existing Kelly App-based skill updated. Every generated skill contains a complete app/ project, deploys that source to Busabase AirApp by default, runs pnpm dev only when local preview is explicitly requested, follows the Kelly desktop and phone UI contract, delegates AirApp runtime, SDK, security, scaffolding, and deployment constraints to busabase-app-creator, and delegates repository-level conformance testing to kelly-app-skill-creator-tests.

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Produces and audits externally published marketing copy where factual claims must survive scrutiny — landing pages, product pages, launch emails, one-pagers, search/social metadata. Every statistic, competitor comparison, capability description, and proof point is traced to a fetched source or the project's own documents, labeled, and adversarially verified before it ships. Use when writing or rewriting high-stakes marketing copy; when copy must pass legal, compliance, brand, or claims review; when the audience is skeptical technical or executive buyers; when the company has little or no customer proof yet (pre-launch, zero customers); or when auditing existing copy for unsupported claims, invented capabilities, unverified statistics, or AI-slop language.

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当使用者说"给照片加参数边框"、"加个相机参数水印"、"给这张照片加个信息带"、"照片下面印上光圈快门"时使用。Add a configurable information band to an existing photograph using embedded EXIF facts such as aperture, shutter speed, exposure compensation, ISO, capture time, and camera. Use when a user asks to frame, label, or export a photo with shooting metadata. Do not use for interpreting photographic intent, inventing missing metadata, or editing the photograph itself.

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当使用者说“按我的模型写代码”“把方程实现成 Python/MATLAB/R”“调试这段建模代码”“补验证和灵敏度分析”“让结果可复现”时使用。只实现学生已确认的模型与数据契约，先写实现合同、手算小例、不变量和验收矩阵，再按读取、变换、求解、验证、敏感性与导出拆分；执行后记录输入快照、种子、环境、求解设置和输出，不静默修数据、不硬编码结果、不替学生发明模型或伪造数据。

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。

当使用者说"给这张照片写图注"、"帮我写一段作品说明"、"这张照片配一句什么文字"、"展览标签怎么写"时使用。Write a factual short photo caption or a longer artist statement from photographer-provided context and optional EXIF metadata. Use when a user asks for a caption, wall label, portfolio note, or brief statement for an existing photograph. Do not infer emotions, intent, place, identity, or circumstances from the image alone.

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Rewrite AI-sounding text so it reads like the writer without changing what it says. Use when editing or reviewing prose for AI tells: not-X-but-Y contrasts, one-line closers, staged openers, forced triads, dashes everywhere, inflated claims, sales language, stock AI words, bold labels, or filler. Based on Wikipedia's "Signs of AI writing."

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Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section. Transforms your writing process from solo effort to collaborative partnership.

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Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn/ui). Use for complex artifacts requiring state management, routing, or shadcn/ui components - not for simple single-file HTML/JSX artifacts.

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Generates creative domain name ideas for your project and checks availability across multiple TLDs (.com, .io, .dev, .ai, etc.). Saves hours of brainstorming and manual checking.

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Design and implement scalable API gateways, RESTful APIs, GraphQL endpoints, WebSocket handlers, and microservice communication patterns. Use when creating API routes, designing endpoints, implementing middleware, or setting up service mesh architectures.

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Use when tasks involve reading, creating, or reviewing PDF files where rendering and layout matter; prefer visual checks by rendering pages (Poppler) and use Python tools such as `reportlab`, `pdfplumber`, and `pypdf` for generation and extraction.

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Transcribe audio files to text with optional diarization and known-speaker hints. Use when a user asks to transcribe speech from audio/video, extract text from recordings, or label speakers in interviews or meetings.

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Use when the user explicitly asks for a desktop or system screenshot (full screen, specific app or window, or a pixel region), or when tool-specific capture capabilities are unavailable and an OS-level capture is needed.

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。

Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn/ui). Use for complex artifacts requiring state management, routing, or shadcn/ui components - not for simple single-file HTML/JSX artifacts.

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。