「discover」の検索結果

Emulated Google OAuth 2.0, OpenID Connect, Gmail, Calendar, and Drive for local development and testing. Use when the user needs to test Google sign-in locally, emulate OIDC discovery, handle Google token exchange, configure Google OAuth clients, work with Gmail messages/drafts/threads/labels, manage Calendar events, upload or list Drive files, or work with Google userinfo without hitting real Google APIs. Triggers include "Google OAuth", "emulate Google", "mock Google login", "test Google sign-in", "OIDC emulator", "Google OIDC", "Gmail API", "Google Calendar", "Google Drive", "local Google auth", or any task requiring a local Google API.

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Use for 'why does X work this way', 'why we picked Y', design rationale, regressions, postmortems, or data-backed thresholds. Discovers available MCPs and queries each evidence category (source control, issue tracker, long-form docs, real-time chat, infrastructure observability, error tracking, product analytics warehouse) in parallel, then returns a cited read on decisions and tradeoffs. Use how for runtime behavior.

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 Trivy vulnerability scanner in offline mode to discover security vulnerabilities in dependency files. This skill covers setting up offline scanning, executing Trivy against package lock files, and generating JSON vulnerability reports without requiring internet access.

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。

When given a project codebase, this skill observes the important functions in the codebase for future action.

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。

Comprehensive Content Security Policy audit for MCP Apps in sandboxed iframes. Discovers all network origins, traces them to source, and generates CSP configuration for registerAppResource.

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。

Observation capture and retrieval across sessions. Stores decisions, discoveries, and bugfix patterns. Searchable via tags and relevance scoring.

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。

Convention discovery and rule generation from codebase analysis. Scans project structure, builds search indexes, identifies patterns, and generates enforceable rules.

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。

This skill should be used when the user asks to "find skills in the wild", "assimilate popular workflows", "discover SKILL.md files in repos", "research external skills", "find workflow patterns", "survey the skill landscape", "what skills exist out there", or wants to investigate public repositories for extractable processes, babysitter plugins, and reusable procedural insights. Searches GitHub for SKILL.md files, classifies repos by archetype, and maintains structured research under docs/reference-repos/.

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。

Atlas turns your STATED NEED into a real systems atlas by SCANNING your actual sources (Azure via `az`, git repos, local dirs) and process/data mining them, THEN enriching against the Atlas knowledge graph. Use this skill when asked to inventory/map your real systems, scan your cloud + repos + directories, mine the real processes or data they contain, or collect their real constraints/gotchas. (atlas, scan my systems, inventory our azure account, map my repos, real systems atlas, process mining, data mining, collect nuances, system discovery)

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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.

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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`.

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Extract reusable knowledge from the current session into a persistent skill. Use when you discover something non-obvious, create a workaround, or develop a multi-step workflow that future sessions would benefit from.

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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。

Use when verifying that a generated agent package can be installed, discovered by runtimes, and checked without private dependencies.

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。

Searches the Azure skills catalog and recommends installable agent skills by matching an Azure task to skill metadata and plugin installation guidance. WHEN: before starting any task that involves an Azure or Microsoft-cloud service, product, or data source, when no currently loaded skill or tool already covers 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。

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。

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。

Audit, question, suggest, or fact-preservingly revise a press release, blog post, contributed article, or expert explainer so AI answer systems can more easily retrieve, understand, quote, and cite its useful information. Use when someone asks for AI visibility, AI search, answer-engine, AEO, GEO, AI Overview, or ChatGPT citation optimization of supplied writing; when they want an evidence-aware pre-publication audit; or when another Newsjack workflow needs a prose-level AI-discoverability pass. Do not use as a technical SEO audit, rank tracker, publishing system, or promise of rankings, mentions, citations, traffic, or coverage.

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。

Requirements authoring guide for BRD and PRD across Discover, Define, and Govern with canonical templates and handoff contracts

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。

Generates PR reference XML with commit history and unified diffs between branches, with extension and path filtering. Use when creating pull request descriptions, preparing code reviews, analyzing branch changes, discovering work items from diffs, or generating structured diff summaries.

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Read-only backlog planning for Azure DevOps, GitHub, and Jira. Use to discover, triage, sprint-plan, or resume without mutating a tracker.

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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。