Research, academic papers, education, and exam prep (4.5K skills)
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`.
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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`.
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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.
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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.
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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.
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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.
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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`.
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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.
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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"。
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将学术论文转化为深度HTML长文。6轮强制工作流、代码仓库搜索、公式渲染、Mermaid图表。 3种写作风格，输出可直接分享的精美HTML页面。
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将论文、技术文章或知识内容制作成高真实感的 AIGC 幻灯片。先做叙事结构和逐页视觉导演，再调用生图模型生成每一页 16:9 slide image，最后合成为 PPTX/PDF。适合论文汇报、组会、公开课、技术分享、商业化研究展示；当用户提到“论文PPT”“AI生成PPT”“不像AI的PPT”“高质感幻灯片”“逐页生图PPT”时使用。
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Fast, clean web research: search with an AI answer and cited sources. Trigger phrases: research this, web search, look up, tavily, find sources.
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Read and write Perplexity: ask AI questions with web search and citations, list models, run raw searches. Trigger phrases: perplexity, sonar, ai search.
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Run fast, progressive experiments to map tunable parameters and their effects. Use when optimizing code, prompts, configurations, or other artifacts against a goal or benchmark, or when a long research run needs a planned hypothesis queue, focused trials, and durable findings.
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Use this skill to build academic-style .docx output: journal / conference / thesis chapters carrying formal citation style (APA, Chicago, IEEE, MLA), numbered equations, figure & table cross-references, footnotes/endnotes, bibliography, or multi-column journal layout. Trigger on: 'research paper', 'journal paper', 'conference paper', 'manuscript', 'thesis', 'APA', 'MLA', 'Chicago', 'IEEE two-column', 'bibliography', 'hanging indent', 'citation style', 'abstract + keywords', 'equation numbering', 'cross-reference', paper with footnotes/endnotes. Output is a single .docx.
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Transforms knowledge sources into an Obsidian StudyVault. Two modes: (1) Document Mode — PDF/text/web sources → study notes with practice questions. (2) Codebase Mode — source code project → onboarding vault for new developers. Mode is auto-detected based on project markers in CWD.
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Interactive quiz tutor for Obsidian StudyVault learning. Use when the user wants to: (1) Take a diagnostic assessment of their knowledge, (2) Study or review specific sections/topics, (3) Drill weak areas identified in previous sessions, (4) Check their learning progress or dashboard, or says things like "quiz me", "test me", "let's study", "/tutor", "학습", "퀴즈", "평가".
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Scholarly research companion using Knows sidecar spec (.knows.yaml). Generates, validates, reviews, queries, and compares structured research-paper sidecars, and fetches them from knows.academy. Use for academic literature search, survey synthesis, paper authoring assistance, and peer review with token-efficient claim/evidence/relation access.
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Intent-based search router with trust scoring. Routes queries to optimal channels (Context7 docs, native web search, gh/glab code search, Serena local) and attaches domain trust labels. Use for search, find, lookup, reference, docs, code search, and web research.
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Research customer pain points, trends, and competitors through the OMA market engine. Use for market discovery or voice-of-customer analysis.
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Search academic literature and generate, validate, or compare Knows paper sidecars. Use for claim/evidence analysis and literature synthesis.
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Draft and revise academic prose against a rubric, evidence, and citation requirements. Use for essays, reports, literature reviews, or academic style audits.
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Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.
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Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.
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