230 件 ・ 関連度順
概要と使いどころ
Content planning, creation, and distribution strategy. Use when planning content calendars, developing content pillars, creating editorial strategies, or optimizing content for different funnel stages.
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Launches and configures the standalone presubmit CLI, an API-driven adversarial peer-review pipeline with 30-plus stages, including Red Team finders, Blue Team defence, verification cascade, legal pass, and copyedit, producing one consolidated report on disk. Verifies installation and API key, chooses the output location and smoke, standard, or custom mode, launches the run, and reports the result path. Use when the user asks to run presubmit, wants a deep unattended pre-submission audit, or needs a math or replication-code audit. Everyday self-audits go to paper-review-lite, and external refereeing to journal-review.
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Map how customers discover, evaluate, and choose a product across Google, AI chatbots (ChatGPT, Claude, Perplexity), and social platforms, using the 5-stage awareness journey. Use when the user asks about search intent, AI SEO prompts, discoverability, buyer journeys, awareness stages, or what content to create for a brand, website, or keyword list.
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Plan, evaluate, and produce paid ad campaigns across platforms (Meta, Google, LinkedIn, TikTok, Microsoft, Pinterest, Snapchat, Amazon, X). Evaluate existing ads, map funnel stages (TOF/MOF/BOF), produce ad variants per platform with policy compliance, output ready-to-upload copy. Use when "ad campaign", "create ads", "run ads for", "paid campaign", "media plan", "launch ads", "Meta campaign", "Google Ads campaign", "multi-platform ads", "evaluate my ads", "how are my ads doing", "audit my ads", "ad performance", "analyze ads", or any request to evaluate existing or create new advertising.
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。
Detect documentation and architecture-model drift by comparing source-of-truth code (CLI commands, flags, env vars, GH Action inputs/outputs, package structure, pipeline stages, providers, adapters, platforms) against web docs, README, and LikeC4 architecture model (.c4 files). Use when files that define the user-facing interface or internal structure change, when the user asks to check docs, or when mentioning doc drift, architecture drift, stale docs, or documentation sync.
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。
Manages AWS CodePipeline stages and actions using AWS SDK for JavaScript (CodePipeline, CodeBuild, CodeDeploy APIs). Automates blue-green deployments and cross-account pipeline configurations.
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 the user says 'plan a campaign', 'promote this product', 'write the copy for our sale', 'what should we push this month', or 'draft the launch for this collection'. Reads what the store actually sells, drafts the campaign around it, and stages every page and product change for approval.
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。
ALIGN for paper figures — draw a method-overview figure as an executable p5.js program that replays its own construction in named stages, exports SVG, and improves through pixel-only independent Codex subagent review whose gate is 'explain the method back from the figure alone, and is it worth looking at'. The method sets the relationships the figure must show, the chosen visual identity sets how its marks look, a composition source lends arrangements; the method may be the source's own (意临) or yours in its arrangement (风格迁移). Not for one-shot raster image generation.
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。
ALIGN for paper figures — draw a method-overview figure as an executable p5.js program that replays its own construction in named stages, exports SVG, and improves through pixel-only cross-model review whose gate is 'explain the method back from the figure alone, and is it worth looking at'. The method sets the relationships the figure must show, the chosen visual identity sets how its marks look, a composition source lends arrangements; the method may be the source's own (意临) or yours in its arrangement (风格迁移). Not for one-shot raster image generation.
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 one milestone is too large for a single specification or plan and must ship as several ordered stages across sessions.
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。
Exposes Claude's reasoning chain as an auditable, decomposable artifact. Quick mode (default) gives assumption inventory + weakest-link in 2 stages. Full mode (--full) adds decision branching, confidence decomposition, and falsification conditions. Triggers on "왜 그렇게 생각해", "reasoning", "근거", "show your work", "어떻게 그 결론이", "trace", "판단 근거", "why do you think that".
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。
Register trained models in MLflow Model Registry with version control, implement stage transitions (Staging, Production, Archived) with approval workflows, and manage model lineage with comprehensive metadata and deployment tracking. Use when promoting a trained model from experimentation to production, managing multiple model versions across development stages, implementing approval workflows for governance, rolling back to previous versions, or auditing model changes for compliance.
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 a person through a Coordinate Remote Viewing (CRV) session following the Stargate/SRI protocol. AI acts as the monitor/tasker role, managing protocol progression, catching Analytical Overlay (AOL), and redirecting the viewer through stages I-VI. Use when a person wants to practice CRV and needs a monitor to manage the session protocol, when training a viewer through the staged CRV process, facilitating a structured intuitive perception exercise, or developing non-local awareness skills that complement healing 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。
Orchestrate end-to-end machine learning pipelines using Prefect or Airflow with DAG construction, task dependencies, retry logic, scheduling, monitoring, and integration with MLflow, DVC, and feature stores for production ML workflows. Use when automating multi-step ML workflows from data ingestion to deployment, scheduling periodic model retraining, coordinating distributed training tasks, or managing retry logic and failure recovery across pipeline stages.
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Design and implement multi-stage CI/CD pipelines using GitHub Actions with matrix builds, dependency caching, artifact management, and secret handling. Create workflows that span linting, testing, building, and deployment stages with parallel execution and conditional logic. Use when setting up automated testing and deployment for a new project, migrating from Jenkins or CircleCI to GitHub Actions, implementing matrix builds across platforms, adding build caching, or creating multi-stage pipelines with security scanning and quality gates.
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。
Creation and genesis — generative ideation from void, structured emergence from ambiguity, and the discipline of bringing new patterns into existence. Maps Brahma's creative power to AI reasoning: transforming cleared space into coherent new structures, seeding possibilities, and nurturing nascent ideas through their fragile early stages. Use after shiva-bhaga dissolution has cleared stale patterns, when facing a genuinely novel problem with no obvious template, or when incremental modification has reached its limits and a fresh design 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。
Use when explaining pipeline stages, data or control hazards, forwarding, stalls, or superscalar basics behind a counter reading. Not for mispredict cost: use branch-prediction-and-speculation.
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 aesthetically beautiful interfaces following proven design principles. Use when building UI/UX, analyzing designs from inspiration sites, generating design images with ai-multimodal, implementing visual hierarchy and color theory, adding micro-interactions, or creating design documentation. Includes workflows for capturing and analyzing inspiration screenshots with chrome-devtools and ai-multimodal, iterative design image generation until aesthetic standards are met, and comprehensive design system guidance covering BEAUTIFUL (aesthetic principles), RIGHT (functionality/accessibility), SATISFYING (micro-interactions), and PEAK (storytelling) stages. Integrates with chrome-devtools, ai-multimodal, media-processing, ui-styling, and web-frameworks skills.
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 Moore's business ecosystem framework to analyze how firms co-evolve through four stages (birth, expansion, authority, renewal) and occupy different ecosystem roles. Use this skill when the user needs to map ecosystem dynamics in a platform or industry, evaluate keystone vs dominator strategies, assess ecosystem health, or when they ask 'what stage is this ecosystem in', 'how should we position in this ecosystem', or 'why is this ecosystem declining despite having a dominant player'.".
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。
"Map and analyze the customer journey across Awareness, Consideration, Decision, Usage, and Advocacy stages. Use this skill when the user needs to understand the customer experience end-to-end, identify drop-off points, optimize touchpoints, or improve conversion — even if they say 'where are we losing customers', 'what's our funnel look like', or 'map the user experience'.".
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。
Map the end-to-end customer journey across awareness, consideration, purchase, onboarding, adoption, retention, and advocacy stages. Identify touchpoints, pain points, emotions, and Moments of Truth per stage. Use when designing or improving customer experience. Do not use for internal employee process mapping — use process-mapper instead.
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Forge highly privileged Kerberos Ticket Granting Tickets (TGTs) to gain persistent, undetectable, and long-term administrative access across an entire Active Directory domain. Use this skill during the final stages of a Red Team operation after Domain Admin access has been achieved, simulating an Advanced Persistent Threat (APT) establishing deep persistence that survives password resets.
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。
Emulates position-independent shellcode in a controlled CPU emulator (Unicorn) to trace executed instructions, memory writes, and decoded second stages without running it on a real host. Activates for requests to emulate shellcode, trace a decoder stub, or recover a stage unpacked at runtime by shellcode.
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。
Stages and commits the current changes onto a safe working branch, enforcing branch discipline and optionally pushing upstream. TRIGGER when: the user wants to commit, save, or check in the current changes. DO NOT TRIGGER when: opening a pull request → pr.
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。