「prompt」の検索結果

สร้าง Midjourney/DALL-E prompt คุณภาพสูง + design brief สำหรับโลโก้ โปสเตอร์ อินโฟกราฟิก โซเชียลมีเดีย

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。

สร้าง concept design + mood board + Midjourney render prompt + material spec สำหรับงานสถาปัตยกรรม

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。

ออกแบบเครื่องประดับ — ring, necklace, earring + gem (cut/clarity/color), metal selection พร้อม render prompt

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。

สร้าง product brief + packaging design + mockup prompt + spec สำหรับสินค้าและบรรจุภัณฑ์

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。

สร้างสคริปต์ YouTube/Shorts/Reels/TikTok พร้อม hook, title, thumbnail prompt, และ SEO metadata

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。

Orchestrate AI coding agents (Claude Code, Codex, OpenCode) in isolated sandboxes using the @ai-hero/sandcastle SDK. Use when the user needs to (1) run agents AFK in Docker/Podman containers, (2) build multi-agent pipelines with plan-execute-review patterns, (3) run parallel agents on separate worktrees, (4) create iterative agent loops with maxIterations, (5) extract structured output from agent runs, (6) set up sandcastle in a new or existing project, or (7) write prompt files with template args and shell expressions.

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。

How to use the coding agents installed on this machine (claude, codex, droid, grok, vktr, hermes, opencode, copilot, pi) as plain sub-agents in orchestrated workflows: one headless invocation, one prompt in, one result out. Deliberately ignores each CLI's own orchestration features (droid --mission, codex multi_agent, hermes delegation/moa/kanban, grok --agents, opencode personas). The orchestrator is whatever session is running the skill; the CLIs are interchangeable workers. Use when the user asks "which agent should run this", "fan out workers", "run X headless", or when a lifeos skill (overnight, timeboxed-iterating, workgraph, gauntlet, bughunt, dark-factory) needs to launch an agent CLI, or when a worker needs company context it should ask Viktor for (vktr).

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 custom modules for [Harbor Boost](https://github.com/av/harbor/tree/main/boost), an optimizing LLM proxy. Use when building Python modules that intercept/transform LLM chat completions—reasoning chains, prompt injection, structured outputs, artifacts, or custom workflows. Triggers on requests to create Boost modules, extend LLM behavior via proxy, or implement chat completion middleware.

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 work as an explicit event-driven state machine executed by subagents. The orchestrator is the runtime from the canonical loop — `state, effects = init(machine); execute(effects); while state is active: event = nextEvent(); state, effects = transition(machine, state, event); execute(effects); return state.output`. All routing judgment is authored ONCE, at plan time, into a machine file (states, event alphabet, transition table, budgets, final states); at runtime the orchestrator does pure table lookups and dispatches effects — it never decides, never authors, never ingests artifacts. Subagent/sync protocol is the timeboxed-iterating one: role prompts scaffolded once to disk and dispatched BY PATH, an initialiser preamble every subagent reads first, a self-populating cheatsheet, per-unit files, tiny structured returns, independent measurement gate. Use when the user says "workmachine", "run this as a state machine", "state-machine this", or when a goal has a small number of clearly named phases with explicit outcomes (built / passed / failed / blocked) and you want the routing to be auditable rather than judged on the fly. For clock-driven loops use timeboxed-iterating; for wide graphs with judged edge criteria use workgraph.

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 a task iteratively over a user-specified duration by dispatching subagents. The orchestrator stays strictly linear and time-checked, but each iteration fans out MULTIPLE subagents in parallel over independent units (and those subagents may fan out further). Shared state — a compact progress digest and a growing environment cheatsheet — lives on disk and is passed to every subagent BY REFERENCE, killing the per-subagent rediscovery tax. Every subagent first reads an initialiser preamble that points it at that shared state and makes it write findings back, so the cheatsheet populates itself; role prompts are produced ONCE on the filesystem by a single scaffold command and dispatched by path, never re-typed per dispatch. Subagents persist their results to disk and return only a tiny status, so the orchestrator's context stays small and lasts. The orchestrator classifies the goal as FINITE or OPEN-ENDED: finite lists may finish early; open-ended goals use the full duration, never idle, never manufacture busywork. Use when the user gives a task and a duration and wants it ground out iteratively by subagents over that time.

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 researched long-form article from a topic prompt via an orchestrated pipeline - research agent (first-person sources, working-definition gate), narrative-architecture outline, writer/cold-reviewer loop with an explicit ACCEPT/REVISE verdict contract, then a catalog-deslop pass with a regression gate. The orchestrator dispatches subagents only; the writer never judges its own draft. Use when the user says "article factory", "write an article about X", "run the article pipeline", or asks for a researched long-form piece produced end-to-end. For essays and micro posts in the user's own voice without a research stage, use the prose skill 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。

Use when creating scientific magazine cover style images or prompts, research hero visuals, journal-cover-inspired concepts, academic poster covers, PPT covers, and high-impact scientific visual concepts. If the user asks to generate the image, use ChatGPT image 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 creating high-quality scientific or medical infographic images or prompts, including review figures, disease maps, teaching diagrams, mechanism summaries, classification overviews, timelines, matrices, and academic visual explainers. If the user asks to generate the image, use ChatGPT image 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 creating publication-grade scientific figure images or prompts, including graphical abstracts, mechanism diagrams, anatomy diagrams, surgical step figures, experimental workflows, pathology mechanisms, device principles, and structured paper illustrations. If the user asks to generate the image, use ChatGPT image 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 orchestrating Loop Factory specs from factory/specs/inbox or factory/specs/active, dispatching work, generating implementation prompts, or deciding next loop state.

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 orchestrating Loop Factory specs from factory/specs/inbox or factory/specs/active, dispatching work to Codex or Claude agents, generating implementation prompts, or deciding next loop state.

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。

Master the `agentbrain` CLI (npm package `agentbrain-cli`). Use this skill whenever the user runs, installs, configures, or asks about `agentbrain` commands; when they interact with the AgentBrain enterprise data hub — organizations, connectors, knowledge bases, workflows, media/documents, knowledge graph, permissions, LLM/prompt config, cost & usage, governance, audit logs; when uploading documents/files to AgentBrain (`agentbrain media upload`); when authenticating (login/logout/token/apiKey); when retrieving MCP context (`agentbrain retrieve-context`); when running SQL against a connector; when the user pastes any `agentbrain …` command; when they edit `~/.agentbrain/config.json`; or when they mention AgentBrain, agent-brain, agentbrain-cli, or the `.agentbrain` folder.

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。

Search and analyze X (Twitter) using xAI Grok 4 via OpenRouter with Live Search. Trigger on prompts that explicitly or implicitly ask to "search Twitter/X", "what's trending", "tweets from @handle", "hashtag #…", "what are people saying", or that require tweet-level activity/engagement from X.

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。

Qualitative quality review of game design documents — answers "is this design good, and does it follow best practices?" — across all three GDD axes: systems, flows, and content types. Fans out adversarial lenses (completeness, clarity, pillar alignment, systems-math, fantasy-delivery, feasibility, scope) plus domain lenses that check it against the game-design rules, each prompted to find problems rather than validate; produces a severity-graded verdict and a two-bucket report (apply-ready fixes vs research), handed to /unikit-gd-apply or /unikit-gd-explore in one move. Scope is inferred: a named system, flow, or content type reviews that document; "all" runs a cross-document review. Use when the user wants a critique, quality judgment, or rules check of the design, e.g. "review the combat GDD", "review the onboarding flow". This is the subjective quality pass — for a mechanical consistency check (numbers, terms, IDs, references matching the registry) use /unikit-gd-verify.

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。

Add a short, project-specific rule, convention, or override to .unikit/RULES.md — the quick-capture inbox for this project's rules (common rules always load; topic files in .unikit/rules/ load when the work matches), automatically loaded by /unikit-implement (later promotable via /unikit-memory migrate-rules). Works only with a rule typed as a prompt — it does NOT read files, folders, URLs, or PDFs. Cross-checks against the knowledge base (RULES_INDEX.md) to avoid duplicating core/stack entries. Use for fast, one-line conventions and corrections: "add a rule", "remember this", "convention", "always do X", "never use Y", "from now on do Z", or when the user corrects agent behavior and wants it remembered; also "split the rules into topics" (optimise), "clean up the rules" (prune), "shorten RULES.md" (compact). If the user points to a source (file, folder, URL, PDF, article, book) or wants to research/document framework usage, use /unikit-memory; for architecture decisions use ARCHITECTURE.md.

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。

Curate the project's knowledge base and memory in .unikit/memory/ — the indexed reference library of coding standards and framework usage patterns. ALWAYS use this skill whenever the user adds knowledge from a source: a URL, a file, a folder path, a PDF, an article, or a book — any path/link/document is a strong signal for the knowledge base, not a quick rule. Also use it to research or document how the project uses a framework, or to add a researched core/stack knowledge entry. Sub-commands by phrasing: "migrate the rules" or "promote rules" runs migrate-rules; "optimize the knowledge base" or "optimize the rules" runs optimise (extract large sections into references); "validate" re-syncs RULES_INDEX.md; "--module <id>" targets a module. For a quick one-line convention or correction typed as a prompt ("always do X", "never use Y", "remember this") with no source, use /unikit-rules; for architecture decisions use ARCHITECTURE.md.

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。

Pre-compaction housekeeping. Walks a checklist (persistent memory updates, git hygiene, trash cleanup) plus an open-judgment audit, produces an SBAR with a go/no-go recommendation, and emits a copy-pasteable resume prompt for the post-compaction agent if work remains. Run this immediately before /compact.

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。

AI media generation CLI tool using Google's Imagen 4, Veo 3.1, and Gemini TTS. Use when the user wants to (1) generate images from text prompts, (2) edit existing images with AI, (3) explain image contents, (4) generate videos from text or images, (5) create narration/voice audio with character settings. Triggers on requests like "generate an image of...", "create a video...", "make a voice that says...", "edit this image to...", "describe this image".

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。

Cost optimization patterns for LLM API usage — model routing by task complexity, budget tracking, retry logic, and prompt caching.

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。