「sms」の検索結果

Use when the user asks to rewrite, humanize, clean up, or remove AI-isms from text while preserving the writer's voice, facts, intent, structure, register, and protected material.

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 "remove AI writing patterns", "humanize this text", "make this sound more natural", "remove AI-generated traces", "fix robotic writing", or needs to eliminate AI writing patterns from prose. Supports both English and Chinese text. Based on Wikipedia's "Signs of AI writing" guide, detects and fixes inflated symbolism, promotional language, superficial -ing analyses, vague attributions, AI vocabulary, negative parallelisms, and excessive conjunctive phrases.

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。

Generate testable hypotheses. Formulate from observations, design experiments, explore competing explanations, develop predictions, propose mechanisms, for scientific inquiry across domains.

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 hypothesis formulation from observations. Use when you have experimental observations or data and need to formulate testable hypotheses with predictions, propose mechanisms, and design experiments to test them. Follows scientific method framework. For open-ended ideation use scientific-brainstorming; for automated LLM-driven hypothesis testing on datasets use hypogenic.

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。

Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory

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 hypothesis formulation from observations. Use when you have experimental observations or data and need to formulate testable hypotheses with predictions, propose mechanisms, and design experiments to test them. Follows scientific method framework. For open-ended ideation use scientific-brainstorming; for automated LLM-driven hypothesis testing on datasets use hypogenic.

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。

Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory

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 hypothesis formulation from observations. Use when you have experimental observations or data and need to formulate testable hypotheses with predictions, propose mechanisms, and design experiments to test them. Also owns explicit HypoGeniC-style or automated LLM-driven hypothesis generation/testing requests inside this single skill. For open-ended ideation use scientific-brainstorming.

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。

Data-backed email marketing skill for AI agents. Use when building or running email automation, driving an ESP from an agent (MCP/connectors), diagnosing deliverability, writing or de-slopping email copy, directing AI email design, choosing a platform, or pulling benchmarks. Covers flows, segmentation, compliance, cold email, WhatsApp, SMS and RCS, and 19 industry playbooks.

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。

Automate Klaviyo tasks via Rube MCP (Composio): manage email/SMS campaigns, inspect campaign messages, track tags, and monitor send jobs. Always search tools first for current schemas.

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。

Automate Close CRM tasks via Rube MCP (Composio): create leads, manage calls/SMS, handle tasks, and track notes. Always search tools first for current schemas.

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。

Query the ChEMBL database for bioactive molecules, drug targets, bioactivity data, approved drugs, and chemical structures. Use when the user asks about compounds, targets, IC50/Ki values, drug mechanisms, or structure searches.

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KEGG pathway and module enrichment analysis using clusterProfiler enrichKEGG and enrichMKEGG. Use when identifying metabolic and signaling pathways over-represented in a gene list. Supports 4000+ organisms via KEGG online database.

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MAGeCK (Model-based Analysis of Genome-wide CRISPR-Cas9 Knockout) for pooled CRISPR screen analysis. Covers count normalization, gene ranking, and pathway analysis. Use when identifying essential genes, drug targets, or resistance mechanisms from dropout or enrichment screens.

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Extract and analyze mutational signatures from somatic variants using SigProfiler or MutationalPatterns to characterize mutagenic processes. Use when identifying DNA damage mechanisms or etiology in cancer genomes.

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。

Sets up notification channels for CloudWatch alarms using SNS topics and subscriptions. Always use this skill when configuring alarm notifications — it creates encrypted SNS topics, configures topic policies for CloudWatch access, sets up email/SMS/webhook subscriptions, and links alarms to notification actions with proper security controls.

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 Amazon SES onboarding for domain-based email sending. Covers identity configuration, production access, an optional first test send, and troubleshooting setup, authentication, or sending failures. Use when setting up SES, resuming incomplete onboarding, or leaving the sandbox. Does not cover inbound email or Mail Manager, SMS/voice, WhatsApp, SNS, Pinpoint, or WorkMail.

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。

Provides connectivity, authentication, and troubleshooting guidance for Amazon RDS for SQL Server. Applicable when users ask about SSMS times out connecting from EC2, Cannot generate SSPI context with Windows auth, connect RDS SQL Server from Lambda with pymssql, auth_scheme shows NTLM instead of KERBEROS on ECS Fargate, SSM tunnel to RDS SQL Server from laptop, port 1433 security group, TrustServerCertificate=True for localhost tunnels, SPN MSSQLSvc, AWS Managed Microsoft AD, CNAME not RDS endpoint for Kerberos, tds_version='7.4', encryption='require', port-as-string for pymssql, Secrets Manager credential caching in Lambda, error 18456 login failed. Covers Python (pymssql, pyodbc), .NET (Microsoft.Data.SqlClient), Java (JDBC mssql-jdbc), Node.js (tedious), IAM auth via RDS Proxy, and VPC/ECS/EKS/Lambda deployment.

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 general use of AWS messaging and streaming services. Covers Amazon SQS, Amazon SNS, Amazon EventBridge, Amazon MQ, Amazon Kinesis Data Streams, Amazon Data Firehose, Amazon Managed Service for Apache Flink, and Amazon Managed Streaming for Apache Kafka (MSK). Use when reasoning about messaging and streaming patterns. Also identifies which AWS service owns each customer communication channel: email (Amazon SES), and WhatsApp, SMS, MMS, RCS, voice and mobile push (the AWS End User Messaging family of services). Routes the request to the specialized skill for that channel. Defers to the channel's specialized skill when the user already named a specific channel. In general, use specific skills or documentation searches for detailed service-specific questions. Do NOT use for MSK or Managed Service for Apache Flink questions, prefer specific skills. Does not configure customer communication channels; defers to specific 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。

Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases. Credits: Original skill by @blader - https://github.com/blader/humanizer

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。

Insecure deserialization playbook. Use when Java, PHP, or Python applications deserialize untrusted data via ObjectInputStream, unserialize, pickle, or similar mechanisms that may lead to RCE, file access, or privilege escalation.

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。

Design the small details -- triggers, rules, feedback, loops and modes -- that separate good products from great ones. Use when the user mentions "microinteraction", "button feedback", "loading state", "toggle design", "animation detail", "state transitions", "input feedback", "the interface feels dead", "make the UI feel responsive", or "add polish to interactions". Also trigger when designing form-validation responses, progress indicators, confirmation dialogs, or any element where the user expects immediate feedback. Covers trigger design, state rules, feedback mechanisms, and progressive loops. For overall UI polish, see refactoring-ui. For affordance design, see design-everyday-things.

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。

Implement authentication and authorization with Better Auth - a framework-agnostic TypeScript authentication framework. Features include email/password authentication with verification, OAuth providers (Google, GitHub, Discord, etc.), two-factor authentication (TOTP, SMS), passkeys/WebAuthn support, session management, role-based access control (RBAC), rate limiting, and database adapters. Use when adding authentication to applications, implementing OAuth flows, setting up 2FA/MFA, managing user sessions, configuring authorization rules, or building secure authentication systems for web 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。

Writes and edits newsletters and marketing emails people actually open — subject lines, opening, structure, voice, and the conversion turn where there is one. Use this to draft or rewrite a newsletter or email, sharpen weak email copy, plan a newsletter's structure and cadence, write a sequence, or repurpose an issue into other formats. For automated lifecycle and SMS programs, use `lifecycle-messaging`.

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。