「pty」の検索結果

Drive one AI agent from another through kolu's terminals: spawn a Claude Code / Codex / opencode session in a PTY, prompt it, watch the screen for its reply, read it, and prompt again — a create→send→snapshot loop run with the `kaval-tui` CLI directly (no MCP). `kaval-tui` writes input and reads scrollback; `pulam-tui` adds a precise agent-state done-signal when you drive hooked terminals. Triggers on "drive another agent", "send a prompt to a terminal agent", "have one agent prompt another", "agent drives agent", "orchestrate agents in terminals", "make Claude drive Codex", "prompt the agent running in that terminal", or wiring a loop where one coding agent supervises another.

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。

Designing first-run product onboarding wizards that get users to the ah-ha moment without overwhelming them. Step architecture, progressive disclosure, escape hatches, completion incentives, drop-off measurement. Honest about tutorial-overload (dump everything upfront), skip-friendly-empty (skipped onboarding leads to abandoned product), and earned-progressive-disclosure (right things at the right moments) patterns. Triggers on onboarding wizard, product onboarding, first-run experience, signup flow, activation flow, FRX, time-to-value, ah-ha moment design. Also triggers when activation rates are low, when users skip onboarding and never return, when onboarding flows are being scoped for the first time, or when audience research shows users not finding key features.

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。

Post-signup user onboarding optimization covering activation metrics, time-to-value reduction, onboarding flow design, empty state optimization, multi-channel coordination, and stalled user recovery.

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 optimize post-signup onboarding, user activation, first-run experience, or time-to-value. Also use when the user mentions "onboarding flow," "activation rate," "user activation," "first-run experience," "empty states," "onboarding checklist," "aha moment," "new user experience," "users aren't activating," "nobody completes setup," "low activation rate," "users sign up but don't use the product," "time to value," or "first session experience." Use this whenever users are signing up but not sticking around. For signup/registration optimization, see signup. For ongoing email sequences, see emails.

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 optimize post-signup onboarding, user activation, first-run experience, or time-to-value. Also use when the user mentions "onboarding flow," "activation rate," "user activation," "first-run experience," "empty states," "onboarding checklist," "aha moment," or "new user experience." For signup/registration optimization, see signup-flow-cro. For ongoing email sequences, see email-sequence.

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。

Produces a copy-ready sentence about a company's funding round, and the list-building filters that select companies which have ever raised or raised recently. Triggers on "raised a round", "recently funded", "companies that raised", "Series A list", "VC backed", "post funding outreach", "who just raised". Outputs funding_line, a COMPLETE sentence including the leading "Saw" and the trailing period, so an empty value renders as nothing at all.

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。

Resolve a company domain to its verified official social profile URLs (LinkedIn, X, Facebook, Instagram, YouTube, TikTok). Triggers on "find their LinkedIn", "get the company LinkedIn URL", "find their Instagram", "we need social handles for this list", "which companies have a TikTok", "social links for the TAM". Outputs one canonical profile URL per platform, or an empty string when the company genuinely has no account.

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 three states every data UI forgets. Use for any component that fetches or lists data.

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 this skill AUTOMATICALLY before writing any Higgsfield prompt. Query the memory databases for relevant past failures and pre-apply known fixes before the user even hits generate. Triggers include: any request to write a Higgsfield prompt, any use of the higgsfield-prompt skill, any mention of generating a video or image on Higgsfield, any MCSLA prompt construction. This skill should run SILENTLY in the background — don't announce it, just apply what's known. If the databases are empty, skip silently and proceed with normal prompt generation.

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 generating or editing images from Flutter/Dart with Firebase AI Logic and a Gemini image model (Nano Banana), making the first call work, choosing Gemini Developer API vs Vertex AI, hitting quota, billing or App Check failures, getting empty or image-only responses, sending a user photo as input, controlling aspect ratio or size, writing the image prompt, or deciding what to test.

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。

Emotional design and delight patterns for product owners. Load when designing onboarding flows, empty states, first-run experiences, or evaluating the emotional quality of an interface.

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 paid ad AUDIENCES must be researched and defined before launch — target profile, interest and behavior mapping per platform, audience sizing, cold, warm, and hot tiering, lookalike seeds, exclusions, and ranked targeting hypotheses to test. Trigger on 'audience research', 'who should I target', 'interest targeting', 'Meta audience for this product', 'TikTok targeting ideas', 'my targeting is too broad'. Also use when launch is imminent and the ad set targeting is still empty. Not for — persona and JTBD depth for content, see `09-customer-insight-global`; warm retargeting tiers, see `56-retargeting-plan-global`; where the budget goes, see `54-media-plan-global`; the campaign hierarchy, see `52-account-structure-global`.

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 the user needs B2B pipeline work — ICP and account lists, prospecting, cold email and LinkedIn outbound sequences, lead scoring, MQL to SQL handoff, sales decks, objection handling, demo scripts, CRM pipeline hygiene, and RevOps reporting. Trigger on 'find leads', 'prospect list', 'cold email sequence', 'outbound campaign', 'lead scoring model', 'our pipeline is empty', 'sales deck for enterprise buyers'. Also use when the user has a strong product and no route to the companies that need it. Not for — consumer lifecycle email, see `14-email-marketing-global`; the offer being sold, see `31-offer-design-global`; positioning against rivals, see `58-positioning-global`; inbound content, see `36-content-brief-global`.

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 writing, editing, reviewing, or replacing UI copy, UX writing, microcopy, button labels, link text, form labels, helper text, empty states, error messages, confirmation dialogs, onboarding text, notifications, or user-facing product wording.

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 the user wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a frontend interface. Covers websites, landing pages, dashboards, product UI, app shells, components, forms, settings, onboarding, and empty states. Handles UX review, visual hierarchy, information architecture, cognitive load, accessibility, performance, responsive behavior, theming, anti-patterns, typography, fonts, spacing, layout, alignment, color, motion, micro-interactions, UX copy, error states, edge cases, i18n, and reusable design systems or tokens. Also use for bland designs that need to become bolder or more delightful, loud designs that should become quieter, live browser iteration on UI elements, or ambitious visual effects that should feel technically extraordinary. Not for backend-only or non-UI tasks.

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。

空数据格式化器。用于创建空数据。 本SKILL使用依赖data_juicer，请在调用前安装好python环境并安装data_juicer，你可用以下指令进行安装： pip install py-data-juicer 当用户提到创建空数据集、生成空数据、初始化数据集、测试用空数据等需求时使用此skill。

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。

First-time onboarding for new Agentara users. Use when user says "bootstrap", "/bootstrap", "get started", "first time setup", or when memory/USER.md and memory/SOUL.md are empty/missing. Inspired by the movie Her — warm, curious, subtly brilliant. The goal is to make the user feel understood within minutes and want to keep going.

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。

Grade a curated list of individual tests for readiness, A-F quality, and concrete improvements. ALWAYS USE FOR: grade tests, review only a named test, per-test readiness decisions, or quality bands for supplied methods, bodies, file spans, or bounded PR diffs, including existing tests. Produce a PR-ready Pass, Failed, Uncertain, or Not applicable table; unresolved or empty scopes omit the grade. Compose read-only per-test mutation evidence when available. Polyglot: .NET, Python, TS/JS, Java, Go, Ruby, Rust, Swift, Kotlin, PowerShell, C++. DO NOT USE FOR: suite-wide audits (test-engineer or test-anti-patterns), writing or fixing tests, or measuring 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。

Use SharpConsoleUI to build full terminal (TUI) applications in .NET — equally suited to full-screen single-window apps and multi-window desktops with overlapping draggable windows — using a compositor, a DOM layout engine, and 40+ reactive controls (data tables, tree views, forms, an embedded PTY terminal, markdown, charts, video). USE FOR: interactive console apps, full-screen TUIs, multi-window terminal desktops, dashboards, wizards, and admin/monitoring UIs that need focus, mouse, and flicker-free rendering over local terminals or SSH; NativeAOT console tools. DO NOT USE FOR: simple line-based CLI output or argument parsing; non-interactive scripts; GUI/desktop (WPF/WinForms) or web UIs. INVOKES: inspect the project, add the SharpConsoleUI package, scaffold a window/control tree, and build/run to verify the app renders.

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。

Manual QA harness for the senpi coding agent itself. MUST USE after changing packages/ai, packages/agent, packages/coding-agent, or packages/tui — a green typecheck and `npm test` are NOT QA. Drives the real CLI from source in an isolated sandbox (never touches ~/.senpi or real credentials) across four channels: remote RPC (--mode rpc JSONL stdio), TUI smoke (node-pty on Windows, tmux on POSIX), mock loop (a local fake model server for deterministic, zero-token agent-loop runs), and CLI smoke (--help/--print/--list-models). Every helper ships a --self-test. Use whenever someone says qa senpi, test the agent, verify my change, rpc qa, tui qa, mock-loop qa, smoke the cli, or needs evidence that an agent-loop, tool, keybinding, or provider change works end to end. Capture evidence to local-ignore/qa-evidence/.

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 kernel evidence shows stalled I/O, a power IRP, or completion/cancellation misuse; inspect request state and driver ownership. Not for interpreting an empty IRP search in a limited dump as proof of healthy I/O.

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 your Obsidian vault in Claude Code — finds stale drafts, empty folders, duplicate filenames, and incomplete files. Saves a dated report.

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。

Electron desktop apps with React, TypeScript, and Vite. Use for IPC, window/tray, PTY terminals, WebRTC, and packaging.

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。

Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.

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。