調査、論文、教育、資格学習（4,109 件）
概要と使いどころ
End-to-end Codex-native Amazon FBA research workflow using LaunchFast MCP. Use when the user wants a single report covering product research, IP risk, supplier sourcing, and PPC keyword intelligence. Requires `research_products`, `ip_check_manage`, `supplier_research`, and `amazon_keyword_research`.
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。
Deep Codex-native Amazon FBA product validation using LaunchFast MCP and the LegacyX criteria. Use when the user wants a serious viability review for a keyword, niche, or adjacent niche expansion.
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。
Codex-native Amazon PPC keyword research using LaunchFast MCP. Use when the user wants competitor keyword discovery, keyword tiering, or a ready-to-upload Amazon Sponsored Products bulk file from 1-15 ASINs. Requires the LaunchFast MCP tool `amazon_keyword_research`.
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。
Codex-native supplier sourcing and outreach workflow for Alibaba using LaunchFast research. Use when the user wants supplier shortlists, outreach messages, reply triage, or negotiation support. Requires `supplier_research`. Browser automation is optional but useful when the user wants Codex to interact with Alibaba directly.
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。
Codex-native multi-keyword Amazon opportunity scan using LaunchFast MCP. Use when the user wants to research 1-10 keywords, compare niches, or decide whether a product idea is a GO, INVESTIGATE, or PASS. Requires the LaunchFast MCP tool `research_products`.
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 only after explicit Codex `$superloopy:superloopy-research` or Claude Code `/superloopy:superloopy-research` invocation, a research task started with a leading `loopy` or `루피` (such as `loopy research`), or an active Superloopy loop explicitly routing a research deliverable here. Evidence-backed Superloopy research orchestration with automatic advisory usage targets, parallel read-only lanes, per-retrieval verdicts, graded and dated sources, empirical verification, a priced claim ledger, cited synthesis, and optional reports. Do not activate from research, investigate, look-up, summarize, deep-dive, report, or similar vocabulary alone, in any language; ordinary questions, debugging, and implementation context-gathering stay with their primary workflows.
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。
Codex-native batch Amazon keyword research using LaunchFast MCP. Use when the user wants a ranked comparison across many keywords and optionally wants HTML and CSV artifacts written to disk.
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。
Entry point for new or resumed work. Researches and scopes new tasks; validates and resumes handoff/checkpoint manifests. Triggers: start, begin, do, implement, build, fix, create, resume, continue.
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。
Record a decision, document existing code, or file a supplied research material. Modes: document decision (ADR, RFC, or rule), document code (spec, doc, guide, or scenario for existing behavior), document research (only when a finished report or one external material is already in hand). Use for 'we decided', 'record this decision', 'document why we chose X', 'make it our standard', 'draft an RFC', 'should we switch to Y' proposals, 'resolve the RFC', 'we accepted the proposal', 'document the auth module', 'capture how the payment system works', reference material, how-to instructions, or a user flow with examples over an existing spec. Name any document type inside the subject — journey, prd, idea, or cpat included — to get exactly that type. Planning a feature or an unnamed intended user journey → /archcore:plan. Checking docs against code or docs health → /archcore:review.
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。
中文学习教练技能。用于学习编程、英语、设计、产品、AI、数学或任何技能时，先诊断水平，再用讲解、练习、反馈、复习的循环推进。触发语包括"进入学习模式""我想学""带我练""帮我制定学习计划""像教练一样教我"。
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。
Saves user instructions as persistent learnings for future sessions. Use when the user says 'remember this', 'always do X', 'from now on', 'never do Y', or gives any instruction they want persisted across sessions. Proactively suggest when the user states a preference, convention, or rule they clearly want followed in the future.
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。
Deep strategic research engine — decomposes questions into parallel research threads, spawns multiple agents, and synthesizes into actionable strategic analysis
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。
Capture durable session learnings, stage for human promotion to 05-knowledge/lizard, and propose skill/CLAUDE.md patches. Triggered by /harvest, SessionEnd hook staging, or nightly enhance. Never writes durable knowledge without your approval.
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 targeting Field Crops Research or deciding whether an agronomy / crop-physiology manuscript fits this venue. Encodes the journal's fit, the field-scale multi-environment and replication bar, data-reporting expectations, house style, official-submission re-check, and desk-reject heuristics.
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 targeting Water Resources Research (WRR) or deciding whether a hydrology / water-science manuscript fits this venue. Encodes the journal's fit, the quantitative water-science contribution bar, data-and-code deposition expectations, AGU house style, official-submission re-check, and desk-reject heuristics.
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 positioning an American Educational Research Journal (AERJ) manuscript in the literature. AERJ readers span the whole field, so the paper must engage the literatures a general education-research audience expects and speak past its own subfield. Builds the positioning argument; it does not write the literature review for you.
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 you need to understand how American Anthropologist (AA) evaluates a manuscript — editorial screening, anonymous peer review under an ethics of care, the four-field editorial board, decision categories, and how section choice shapes the path. Sets expectations and shapes the paper to survive review; it does not contact editors.
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 the response to an American Educational Research Journal (AERJ) revise-and-resubmit. AERJ R&Rs come back through the handling editor and multiple masked reviewers, so the response must convert each reviewer while keeping the editor confident the revision is convergent. Structures the response letter; it does not fabricate new results.
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 positioning an American Anthropologist (AA) manuscript against the literature so it reads as a four-field contribution. AA readers span sociocultural, archaeology, biological, and linguistic anthropology, so the paper must engage the conversations they expect and decenter a US/Anglophone-only canon. Stakes the contribution and citational politics; it does not write the lit review.
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 model setup, equilibrium concept, or proof architecture is the bottleneck for an American Economic Journal: Microeconomics (AEJ: Micro) manuscript — the central skill for a theory-first paper. Builds the model to the AEJ: Micro bar (clean, general, well-motivated result); it does not check whether your theorem is correct.
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 contribution of an American Economic Journal: Microeconomics (AEJ: Micro) manuscript is fuzzy or oversold relative to the closest existing results. Stakes the theorem-relative delta against the nearest theory; it does not build the model (see aejmic-theory-model) or frame the prose (see aejmic-writing-style).
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 framing the system and describing the model for an Agricultural Systems (AgSy) manuscript — defining system boundaries, components, hierarchical levels, and feedbacks, then choosing, describing, and calibrating the model (process/simulation, whole-farm, bio-economic, agent-based, or integrated assessment). This is the distinctive core of an AgSy paper. Structures the model and system; it does not run code for you.
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 to understand how Agricultural Systems (AgSy) evaluates a manuscript — single-anonymized peer review with a minimum of two reviewers and an editor decision, desk screening for fit and systems content, what reviewers weigh, and the rapid-review track for Perspectives. Sets expectations and shapes the paper to survive review; it does not contact editors.
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 model — a full theory paper's argument or the structural/conceptual scaffolding behind an empirical AER: Insights short-format manuscript — needs to be made minimal, so it carries the single insight in a few pages. Keeps only what the insight requires; it does not develop a general framework.
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。