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概要と使いどころ
Transforms conversations and discussions into structured documentation pages in Notion. Captures insights, decisions, and knowledge from chat context, formats appropriately, and saves to wikis or databases with proper organization and linking for easy discovery.
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。
Scaffold a new skill that follows this repo's conventions — interviews for purpose, trigger phrases, and tool needs, then writes `.claude/skills/<name>/SKILL.md` from the skill template with frontmatter and body that pass the integrity gates on first try. Use when user says "write a skill", "scaffold a skill", "create a new skill", "I keep doing X, make it a skill", "new slash command", or "turn this workflow into a skill". NOT for capturing a one-off session discovery — that is `/learn`.
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。
Interview the user relentlessly about a plan, design, or topic, checkpointing every answer to a brainstorm file so nothing is lost. Use when the user wants to stress-test a plan, get grilled on a design, run a brainstorm or discovery session, extract what's in their head into a doc, build up their AI OS context over time, or says "grill 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。
End-to-end user research assistant — qualitative and quantitative. Use this skill whenever the user mentions user research, user interviews, discussion guides, interview guides, research plans, qualitative research, quantitative research, user surveys, survey design, usability studies, participant recruitment, research synthesis, interview transcripts, research reports, running studies with AI, or explicitly mentions Cookiy AI. Also trigger when users want to talk to customers, conduct discovery research, create a study or survey, analyze interview data, run AI-moderated interviews, or collect survey responses. Covers the full lifecycle: planning studies, creating discussion guides, running AI-moderated interviews (real or synthetic) via Cookiy, designing and distributing surveys, and synthesizing results into reports.
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。
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
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。
List, find, and show Azure resources across subscriptions or resource groups. Handles prompts like "list the websites in my subscription", "list my web apps", "show my app services", "list virtual machines", "list my VMs", "show storage accounts", "find container apps", and "what resources do I have". USE FOR: list websites, list web apps, list app services, show websites in subscription, resource inventory, find resources by tag, tag analysis, orphaned resource discovery (not for cost analysis), unattached disks, count resources by type, cross-subscription lookup, and Azure Resource Graph queries. DO NOT USE FOR: deploying/changing resources (use azure-deploy), cost optimization (use cost-optimization from the optional azure-cost plugin), or non-Azure clouds.
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。
Discovers, searches, and installs skills from multiple AI agent skill marketplaces (400K+ skills) using the SkillKit CLI. Supports browsing official partner collections (Anthropic, Vercel, Supabase, Stripe, and more) and community repositories, searching by domain or technology, and installing specific skills from GitHub. Use when the user wants to find, browse, or install new agent skills, plugins, extensions, or add-ons; asks 'is there a skill for X' or 'find a skill for X'; wants to explore a skill store or marketplace; needs to extend agent capabilities in areas like React, testing, DevOps, security, or APIs; or says 'browse skills', 'search skill marketplace', 'install a skill', or 'what skills are available'.
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。
Jira issue workflows for search, issue updates, transitions, comments, field discovery, and interactive credential setup via the Jira REST API. Use when you need to configure Jira access, search with JQL, inspect an issue, create or update work items, move an issue between statuses, post comments, or discover required fields for issue creation.
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。
Audit an LLM eval pipeline and surface problems: missing error analysis, unvalidated judges, vanity metrics, etc. Use when inheriting an eval system, when unsure whether evals are trustworthy, or as a starting point when no eval infrastructure exists. Do NOT use when the goal is to build a new evaluator from scratch (use error-discovery, write-judge-prompt, or validate-evaluator instead).
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 error analysis on a dataset. Build a review UI, select diverse samples, monitor annotations, and organize failure modes.
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。
Install and wire registry blocks and components into HyperFrames compositions. Use when running hyperframes add, installing a block or component, wiring an installed item into index.html, or working with hyperframes.json. Covers the add command, install locations, block sub-composition wiring, component snippet merging, and registry discovery.
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。
Install and wire registry blocks and components into HyperFrames compositions. Use when running hyperframes add, installing a block or component, wiring an installed item into index.html, or working with hyperframes.json. Covers the add command, install locations, block sub-composition wiring, component snippet merging, and registry discovery.
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。
Help users design and execute a modern SEO strategy that balances technical site health with programmatic content scaling and AI-driven discovery.
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。
Help users build a sustainable habit of regular customer interaction to ensure product development is driven by real-world needs rather than internal assumptions.
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。
Help users build a robust enterprise sales engine by mastering founder-led discovery, navigating complex procurement cycles, and designing products that meet organizational governance needs.
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。
Research customer pain points, trends, and competitors through the OMA market engine. Use for market discovery or voice-of-customer analysis.
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。
Guides researchers through structured ideation frameworks to discover high-impact research directions. Use when exploring new problem spaces, pivoting between projects, or seeking novel angles on existing 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。
Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning neural networks, debugging loss spikes or OOM, choosing architectures, or optimizing GPU throughput.
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 insecure file upload vulnerabilities in a codebase using a three-phase approach: discovery (find all upload sites), batched verify (check extension bypass and related issues in parallel subagents, 3 sites each), and merge (consolidate batch results). Requires sast/architecture.md (run sast-analysis first). Outputs findings to sast/fileupload-results.md. Use when asked to find file upload, unrestricted upload, or extension bypass bugs.
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 for VidSeeds MCP analytics and research - YouTube analytics, channel intelligence, video autopsy, outliers, competitors, trending/breakout discovery, keyword research, comment sentiment, and best-practices tools. Outcomes only; not internal scoring details.
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。
Codex-native Amazon PPC keyword research using LaunchFast MCP. Use when the user wants competitor keyword discovery, keyword tiering, or a ready-to-upload Amazon Sponsored Products bulk file from 1-15 ASINs. Requires the LaunchFast MCP tool `amazon_keyword_research`.
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。
Plan a feature or initiative through a computed route: the conductor derives the canon delta and assembles the document package — from a zero-document null route for small fixes to an umbrella PRD with one spec per capability for large initiatives. Modes, named as the first word: sdd (full package), sources (MRD → BRD → URD) for market research, customer discovery, and business or user requirements, iso (BRS → StRS → SyRS → SRS) for ISO 29148 and regulated work, research for a technical investigation — never market research — that the research instrument closes either by scope coverage (a research document) or by a recommendation (an rnd). Explicit form: plan research <topic>. Use for 'plan the X redesign', 'create a roadmap', 'plan a new feature', 'I need market research before we plan', 'we're regulated — start the ISO requirements cascade', 'investigate X before we plan', 'compare the alternatives for Y' — a new investigation, not a finished report already in hand. Not for recording a decision or documenting existing code — use /archcore:document. Not for checking docs against code — use /archcore:review.
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。
Используй только внутри активного Codex Project Autopilot-проекта после discovery или approval, а не для обычных архитектурных вопросов вне автопилота.
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 for AI-assisted Unity work that needs live repo discovery, project-derived routing, runtime-owner proof, runtime-visible output hard stops, runtime numeric proof for repeated visible-output failures, state-step guards, multi-agent scope ownership, modular C#/asmdef safety, UI/scene/visual asset gates, data-first content changes, validation, cleanup proof, or durable workflow rules. Best when agents must prove the actual folder/module/scene/prefab/runtime owner before editing, especially runtime UI, generated assets, code graphs, tutorial/state flows, guided selection/action flows, overlay/dim source-bound mistakes, coordinate conversions, focus/highlight/marker/HUD alignment, predictive lead aiming, ballistic trajectories, spatial IoU verification, or repeated "fix still not visible" failures.
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。