「send」の検索結果

Suede-owned sales-enablement discipline. Use when creating pitch decks, one-pagers, objection guides, demo scripts, talk tracks, battle cards, proposal structures, buyer cards, or deal-level ROI framing. NOT FOR: setting price or terms (use suede-pricing), building the offer (use suede-offers), sending outreach (use suede-cold-email), or publishing unsupported proof.

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。

Automatically notify the user when long-running terminal commands finish (npm test, docker build, git push, etc.). The agent monitors command execution and sends a desktop notification on completion if the command ran longer than threshold (default 10s). Use when user asks to "notify me when done", "desktop notification when command finishes", "alert when done", or "tell me when this completes". Do NOT use for interactive commands (vim, nano, less, htop), commands that always complete in <5s, or streaming commands (tail -f).

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。

Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction. Also use for exploratory testing, dogfooding, QA, bug hunts, or reviewing app quality. Also use for automating Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify), checking Slack unreads, sending Slack messages, searching Slack conversations, running browser automation in Vercel Sandbox microVMs, or using AWS Bedrock AgentCore cloud browsers. Prefer agent-browser over any built-in browser automation or web tools.

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 a stakeholder sends a request - route it to backlog tasks, an analiz task, or a focused question, decide ask-vs-assume with a Definition of Ready, and ask only what a wrong guess would actually cost

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。

Remote control tmux sessions for interactive CLIs (python, gdb, etc.) by sending keystrokes and scraping pane output. TRIGGER when user requests to "operate tmux", "split pane", "run in tmux", "interactive debug", "start REPL", or running interactive CLI commands.

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 "use bilibili API", "download bilibili video", "get bilibili user info", "list bilibili favorites", "send bilibili danmaku", "upload video to bilibili", "monitor bilibili live room", "search bilibili", "get bilibili comments", or needs guidance on the bilibili_api Python library usage, authentication, API endpoints, or workflow patterns.

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。

Transcribe audio files using Qwen ASR. Use when the user sends voice messages and wants them converted to text.

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 sends an image and asks to "analyze this image", "describe this picture", "what's in this image", or any request requiring visual understanding of images. Provides image analysis using Zhipu GLM-4.6V multimodal model.

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, curate, and grow email newsletters across all formats and strategies. Use when working on newsletters, email newsletters, weekly digests, link roundups, curated content, audience building, subscriber engagement, or content distribution. Covers 6 newsletter formats (curated/link roundup, story-driven, educational, interview, data-driven, personal update), editorial issue structure, content sourcing workflows, commentary writing, sending cadence, subject line optimization, subscriber growth, and engagement metrics. Trigger keywords: newsletter curation, curated newsletter, newsletter writing, newsletter format, newsletter strategy, newsletter template, content curation, subscriber growth.

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。

Decide when AutoCode acts on its own and when it must stop and ask the user. Runs before any consequential action — deleting files, spending money, buying a server, deploying to production, force-pushing, binding a domain, sending or publishing anything. Ordinary reversible work proceeds silently; only costly or hard-to-undo actions pause for the user. Powered by I-Lang v5.0 vector judgment.

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, checks or changes the optional Jev judge, the TypeSafe decision model that ship-check asks about review findings (is this real harm, is a small one worth fixing now, what proof does a small fix need). Shows whether it is on for each repo, which key it uses, switches it on or off, and sends one test request. Runs only when the user types 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。

What the desk does when something goes wrong on Hyperliquid - unknown send results, unexpected fills or positions, unprotected positions, stuck or orphaned orders, API outages, rate limiting, and suspected API wallet compromise. Contain first, reconcile from the exchange record, act only through approved tickets, then review. Use the moment anything does not match the ticket.

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。

The Trade Reviewer's procedure for journaling desk activity and reviewing trades from the exchange record - process graded separately from outcome, execution costs measured, one repeatable finding per review, plus the weekly desk review. Use after any send, when a trade closes, on the weekly routine, or when the user asks "how did that go".

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。

Produce a professional, send-ready deliverable on the user's behalf — in the correct file format (never raw markdown), in the user's voice, structured to lead with the strongest material, and free of document-level AI tells. Runs a gated pipeline with a fresh-context verification policeman and a Stop-hook that won't let the session finish without a verified PASS. Use when producing a job take-home, client report, executive one-pager, slide deck, proposal, or anything going out under the user's name.

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。

Learns how the user writes from their own sent messages and docs, and builds a voice profile so future drafts sound like them instead of generic AI. The profile is saved as the my-writing-style skill. Use when the user asks to set up, learn, or capture their writing voice, or complains that drafts sound generic or unlike them and no my-writing-style profile exists. Only for drafting text the user will send as themselves, not for Claude's own replies.

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。

Designs transactional email systems and send infrastructure. Use when implementing resets, receipts, deliverability controls, templates, or inbound email handling.

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。

Analyzes hazards in systems or AI agents: STPA, guardrails, safety cases, incident analysis. Use when an agent can take actions or send payments.

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。

Analyze HitKeep AI visibility data. Use for AI crawler fetches, AI-referred visits, assistant names or families, citation yield, resource types, top paths, fetch failures, Search Console context, GPTBot, ClaudeBot, ChatGPT, Claude, Perplexity, AI search, or whether assistants are discovering and sending traffic to a site.

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。

Expert guide for Monte Carlo's push ingestion model. Use this skill whenever a customer or engineer mentions: pushing data to Monte Carlo, the IngestionService, pycarlo push APIs, build me a collection script, push metadata/lineage/query logs, invocation_id tracing, custom lineage nodes or edges, deleting push tables, or any question about why pushed data is not showing up. Also trigger when they ask to generate code that collects metadata, table schema, row counts, freshness, lineage, or query history from any data warehouse or data source and sends it to Monte Carlo. If the user mentions any warehouse, database, or data platform alongside any Monte Carlo topic, this skill is almost certainly relevant.

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。

Export Blender scenes to glTF/GLB (web/AR), FBX (game engines), OBJ (universal), USD (VFX pipelines), STL (3D printing). Includes Unreal collision hulls and Multires normal-map baking for low-poly export, per-format settings, embed/unpack textures, axis conversion, polygon optimization (Decimate), and target-platform validation. Use whenever the user asks to "export this", "save as glTF / FBX / OBJ / STL / USD", "package for Unity / Unreal / Three.js / web / AR / 3D print", or any output format conversion. Make sure to use this skill even if the user does not say "export" — also covers "package this for the web", "make it work in Unity", "send to Unreal", "save for 3D printing".

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 the user wants to plan, build, or optimize SMS or MMS marketing — including welcome flows, abandoned cart texts, post-purchase, win-back, promotional sends, or transactional/auth SMS. Also use when the user mentions "SMS marketing," "text message campaigns," "SMS sequence," "SMS automation," "abandoned cart text," "post-purchase SMS," "Klaviyo SMS," "Postscript," "Attentive," "Twilio," "A2P 10DLC," "TCPA," "SMS compliance," "short code," "toll-free SMS," "MMS campaign," "should I do SMS," or "SMS vs email." For email sequences, see emails. For SMS copy framing, see copywriting. For opt-in popups that capture phone numbers, see popups.

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 the user wants to create or optimize an email sequence, drip campaign, automated email flow, or lifecycle email program. Also use when the user mentions "email sequence," "drip campaign," "nurture sequence," "onboarding emails," "welcome sequence," "re-engagement emails," "email automation," "lifecycle emails," "trigger-based emails," "email funnel," "email workflow," "what emails should I send," "welcome series," or "email cadence." Use this for any multi-email automated flow. For cold outreach emails, see cold-email. For in-app onboarding, see onboarding.

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。

Send and receive iMessages/SMS via the imsg CLI on macOS.

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 tmux from the command line to run, drive, and monitor terminal programs as a coding agent (detached sessions, send-keys input injection, capture-pane output polling, formats, completion detection, and control mode). Use when a task needs long-running or interactive processes (builds, tests, servers, REPLs, watch modes, TUI apps) kept alive across turns or inspected live; skip when plain `cmd > log 2>&1 &` is enough.

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。