「ema」の検索結果

GraphQL gives clients exactly the data they need - no more, no less. One endpoint, typed schema, introspection. But the flexibility that makes it powerful also makes it dangerous. Without proper controls, clients can craft queries that bring down your server. This skill covers schema design, resolvers, DataLoader for N+1 prevention, federation for microservices, and client integration with Apollo/urql. Key insight: GraphQL is a contract. The schema is the API documentation. Design it carefully.

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CLI tool for AI-powered web scraping, data extraction, search, and crawling via ScrapeGraph AI. Use when the user needs to scrape websites, extract structured data from URLs, convert pages to markdown, crawl multi-page sites, search the web for information, automate browser interactions (login, click, fill forms), get raw HTML, discover sitemaps, or generate JSON schemas. Triggers on tasks involving: (1) extracting data from websites, (2) web scraping or crawling, (3) converting webpages to markdown, (4) AI-powered web search with extraction, (5) browser automation, (6) generating output schemas for scraping. The CLI is just-scrape (npm package just-scrape).

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Email has the highest ROI of any marketing channel. $36 for every $1 spent. Yet most startups treat it as an afterthought - bulk blasts, no personalization, landing in spam folders. This skill covers transactional email that works, marketing automation that converts, deliverability that reaches inboxes, and the infrastructure decisions that scale. Use when: keywords, file_patterns, code_patterns.

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Harden the Docker daemon by configuring daemon.json with user namespace remapping, TLS authentication, rootless mode, and CIS benchmark controls.

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Parse Office 365 Unified Audit Logs via Microsoft Graph API to tespit etme
email forwarding rule creation, inbox delegation, suspicious OAuth app grants, and other indicators of account compromise.

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Professional email drafting with appropriate tone and structure. Use when the user wants to write, compose, or draft an email.

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Detect compromised O365 and Google Workspace email accounts by analyzing inbox rule creation, suspicious sign-in locations, mail forwarding rules, and unusual API access patterns via Microsoft Graph and audit logs.

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Business Email Compromise (BEC) is a sophisticated fraud scheme where attackers impersonate executives, vendors, or trusted partners to trick employees into transferring funds, sharing sensitive data,

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Deploy AI and NLP-powered detection systems to identify business email compromise attacks by analyzing writing style, behavioral patterns, and contextual anomalies that evade traditional rule-based filters.

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Implement a phishing report button in email clients with automated triage workflow that analyzes user-reported suspicious emails and provides feedback to reporters.

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Plan a user interview topic in PostHog — pick who to target (cohort, emails, or PostHog distinct IDs), draft what to ask about, and prepare the voice-agent context plus a question list. Use when the user asks to "talk to users", "check how users feel about X", "interview some customers", "set up a user interview", "run a user-research call", "find users to ask about Y", or otherwise wants qualitative feedback through a conversation. Walks the user through targeting (cohorts-list, persons-list, or accepting emails / distinct IDs directly), captures the topic, and prompts for agent context and questions before calling user-interview-topics-create. Cohort targeting is resolved to explicit emails/distinct_ids at create time — topics snapshot their audience and do not re-evaluate cohort membership later. Do NOT trigger when the user is uploading a recorded interview audio file (that's the separate UserInterview/transcript flow) or only browsing existing topics with user-interview-topics-list.

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Diagnose why a data warehouse sync is failing and recommend the right recovery action. Use when the user asks "why isn't my Stripe/Postgres/Hubspot sync working?", "this table has been stuck for hours", "the data in the warehouse looks wrong", or wants to troubleshoot a specific source or schema. Covers source-level vs schema-level failures, stuck Running states, credential and schema-drift errors, incremental-field misconfig, CDC prerequisite failures, and the cancel / reload / resync / delete-data recovery actions.

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Use this skill when working with symbolic mathematics in Python. This skill should be used for symbolic computation tasks including solving equations algebraically, performing calculus operations (derivatives, integrals, limits), manipulating algebraic expressions, working with matrices symbolically, physics calculations, number theory problems, geometry computations, and generating executable code from mathematical expressions. Apply this skill when the user needs exact symbolic results rather than numerical approximations, or when working with mathematical formulas that contain variables and parameters.

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Design AI email assistants with drafting and triage. TRIGGERS - Use when user needs help with ai-email-assistant related tasks.

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Build AI email writing systems. TRIGGERS - Use when user needs help with ai-email-writer-system related tasks.

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Build AI demand forecasting systems. TRIGGERS - Use when user needs help with ai-demand-forecaster related tasks.

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DON'T USE THAT SKILL. IT'S JUST AN EXAMPLE. Summarize today's Gmail inbox by category with anonymized senders and subjects, then surface the top 3 emails to focus on next. Run only when the user types /email-summary.

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Scan Gmail (default: today's inbox), produce an anonymized summary with action points, then export each email to Linear as an issue with a sensible priority. No personal details — names, email addresses, or company identifiers — ever leave the summary or reach Linear. Use when the user types /gmail-linear-exports, or asks to turn their inbox into Linear issues / export emails to Linear.

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Generates strictly typed Drizzle ORM schemas for PostgreSQL with indexes, foreign keys, and Zod schema deduction.

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Design and generate Convex database schemas with proper validation, indexes, and relationships. Use when creating schema.ts or modifying table definitions.

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Give the agent its own dedicated email inbox via AgentMail. Send, receive, and manage email autonomously using agent-owned email addresses (e.g. hermes-agent@agentmail.to).

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Escreve sequências e e-mails isolados — onboarding, nutrição, recuperação de carrinho, lançamento, newsletter e cold email. Use quando o usuário disser "e-mail marketing", "sequência de e-mails", "newsletter", "cold email", "fluxo de nutrição", "recuperação de carrinho", "automação de e-mail".

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Automate Benchmark Email tasks via Rube MCP (Composio). Always search tools first for current schemas.

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Use for sensemaking, frame revision, anomaly handling, questioning strategy, and ambiguity diagnosis with the Data/Frame model before action is chosen. NOT for deterministic classification, settled execution planning, or situations where the frame is already stable.

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。