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概要と使いどころ
Behavioral guardrails for implementation work. Reduces common LLM coding mistakes: overcomplication, silent assumption-picking, scope drift, and unverifiable success criteria. Load on Builder and software-engineer-mode.
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。
Mirror an iOS Simulator into the Codex in-app browser and render SwiftUI previews from importable Swift packages in that simulator with hot reload. Use when a user wants to watch or interact with an iOS app in the browser, see a SwiftUI preview outside Xcode Canvas, iterate live on a preview, or capture browser-visible simulator proof.
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。
Structured task planning with clear breakdowns, dependencies, and verification criteria. Use when implementing features, refactoring, or any multi-step 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。
Iterative session history with timestamped checkpoints and LATEST.md symlink
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。
Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation 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。
Azure AI Text Translation SDK for real-time text translation, transliteration, language detection, and dictionary lookup. Use for translating text content in applications.
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, iterate, and scale paid ad creative for Google Ads, Meta, LinkedIn, TikTok, and similar platforms. Use when generating headlines, descriptions, primary text, or large sets of ad variations for testing and performance 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。
Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.
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。
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。
tespit etme (s) prompt injection attacks targeting LLM-based applications using a multi-layered defense combining regex pattern matching for known attack signatures, heuristic scoring for structural anomalies, and transformer-based classification with DeBERTa models. The tespit etme (or) analyzes user inputs before they reach the LLM, flagging direct injections (system prompt overrides, role-play escapes, instruction hijacking) and indirect injections (encoded payloads, multi-language obfuscation, delimiter-...
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。
Iterative Python via live Jupyter kernel (hamelnb).
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。
Durable repo-native multi-goal plans with embedded success criteria and evidence audit.
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。
テックブログ記事をSEOに最適化してリライトするスキル。タイトル最適化、メタディスクリプション生成、本文のキーワード配置、コンテンツ拡充提案、古い表現の更新、構造化データ提案など、包括的なSEOライティングを実施。「SEOリライトして」「検索順位を上げたい」「記事を最適化」などのリクエストで利用。
「新任職員向けに生成 AI 基礎研修を作りたい」「SD 研修資料を短時間で組み立てたい」 「自分自身のリテラシー棚卸しをしたい」「新人に AI の使い分けを 30 分で説明したい」 「ハルシネーションの見抜き方を教えたい」場面で使う、大学職員向け AI リテラシー入門スキル。 プロンプトの基本構造・ハルシネーション検出・サービス選定を、30〜60 分のコア研修に そのまま流用できる章立てで提供する。
Automatically consume open GitHub issues one by one. Fetches the oldest open issue, delegates implementation to the issue-implementer agent, runs code review via the code-reviewer agent, iterates on feedback, merges the PR, and moves on to the next issue. Invoke with `/auto-issue-worker`.
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。
Markdown で提案書・報告書・議事録などのビジネスドキュメントを構造的に作成するスキル。対象読者に応じた表現で、そのまま使える品質を目指す。
Meta-skill that generates compliant PLAYBOOK.md files following the BRAINIAC template. Use when creating or editing modules in the command center. For general readme creation, see create-readme.
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。
Repurpose source content into channel-ready drafts with a Busabase App-in-Skill review and export workflow. Use when the user asks to write content, make a content pack, turn a main blog/long article/transcript/notes into Xiaohongshu, WeChat, newsletter, LinkedIn, X/Twitter, short video scripts, SEO snippets, or a multi-platform publishing plan; also use when they ask for a content approval dashboard or App-in-Skill content workflow.
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 Busabase-backed sandbox desk for scoring and triaging course-instructor ("讲师") candidates found on a recruiting-style platform. Use whenever the user invokes $kelly-instructor-sourcing or /kelly-instructor-sourcing, wants a lightweight instructor candidate tracker, needs to score sourcing-platform leads against a three-axis rubric (background endorsement, expertise depth/breadth, teaching-service ability) before reaching out, or wants to log a WeChat add without building a live-connected outreach tool. Three subcommands - setup, criteria, review - plus the desk itself. v1 is sandbox-only — every dataset is deterministic mock data, it never connects to a real sourcing-platform or messaging account, and it never sends a message or automates a WeChat add - that stays entirely human-performed outside the app.
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。
End-to-end ABM program design and orchestration for B2B SaaS mid-market and enterprise motions. Use when building a target account list, designing account tiering criteria, planning ABM channel orchestration, coordinating BDR outreach with marketing campaigns, defining ABM measurement frameworks, or running an ABM program review. Also use when asked to "build an ABM program," "set up account-based marketing," "design a target account strategy," "plan ABM campaigns," or "measure ABM results." Designed for demand gen teams selling to Mid-Market companies with $10K-$50K+ ACV, multi-persona buying committees, and 3-6 month sales cycles.
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。
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."
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。
Detects prompt injection attacks targeting LLM-based applications using a multi-layered defense combining regex pattern matching for known attack signatures, heuristic scoring for structural anomalies, and transformer-based classification with DeBERTa models. The detector analyzes user inputs before they reach the LLM, flagging direct injections (system prompt overrides, role-play escapes, instruction hijacking) and indirect injections (encoded payloads, multi-language obfuscation, delimiter-based escapes). Based on the OWASP LLM Top 10 (LLM01:2025 Prompt Injection) and Simon Willison's prompt injection taxonomy. Activates for requests involving prompt injection detection, LLM input sanitization, AI security scanning, or prompt attack classification.
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。
Designs and documents structured incident response playbooks that define step-by-step procedures for specific incident types aligned with NIST SP 800-61r3 and SANS PICERL frameworks. Covers playbook structure, decision trees, escalation criteria, RACI matrices, and integration with SOAR platforms. Activates for requests involving IR playbook creation, incident response procedure documentation, response runbook development, or SOAR playbook 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。
Turns product or tech specs into concrete Notion tasks that Claude code can implement. Breaks down spec pages into detailed implementation plans with clear tasks, acceptance criteria, and progress tracking to guide development from requirements to completion.
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。