調査、論文、教育、資格学習（4,112 件）
概要と使いどころ
Use when planning a source-grounded method overview, architecture, pipeline, system/data-flow, mechanism, or agent-workflow figure for a CS or deep-learning paper. Produces an editable figure brief before image generation or manual redrawing; not for plots, tables, or general prose.
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Use when independently reviewing a research-paper draft before submission, running a mock top-conference panel, assessing readiness or accept/reject risk, or predicting reviewer concerns for venues such as CVPR, ICCV, ECCV, ICLR, NeurIPS, and AAAI. Does not draft or audit author responses after official reviews arrive.
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 before writing custom code or a research workflow when a maintained library, tool, skill, or proven pattern may already exist. Start with local reuse and targeted evidence; broaden to external research for new dependencies, new mechanisms, or uncertain external facts.
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 designing, redrawing, or validating research-paper figures and tables, including Matplotlib plots, LaTeX tables, benchmarks, captions, legends, annotations, color, and typography. Preserves exact evidence and checks rendered PDFs for clipping, overlap, and readability.
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Search, screen, deeply read, synthesize, and publish recent research papers for a user-specified topic, window, count, quality/attention balance, and language. Use for evidence-anchored literature digests, cross-paper comparisons, original-language excerpts, Chinese explanations, guided Feishu setup, or Feishu research reports.
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 or revise audience-ready HTML paper presentations for lab meetings and journal clubs. Use when Codex needs source-grounded figures and tables, a fluent live-talk narrative, verified quotations, varied explanatory diagrams, presenter-paced reveals, or browser QA for evidence fidelity, interaction, responsive layout, and print.
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Draft or restructure research-paper sections, refine local prose, and audit manuscript-wide consistency. Use for evidence-grounded academic writing and revision, not independent acceptance-risk review or official rebuttal management.
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 research writing, reviews, rebuttals, or related-work planning needs academic evidence. Searches literature, screens candidate papers, audits novelty and missing work, verifies DOI/BibTeX/reference metadata and citation authenticity, and checks whether sources support the stated claims.
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 asks to track, scan, monitor, digest, or maintain a reading queue for new papers from top journals, Nature/Science/Cell/PNAS families, or any research field. Works with any discipline — ecology, environment, CS, medicine, physics, social sciences, etc.
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 agent when you need to systematically review existing GO annotations for a gene and make curation decisions based on literature evidence and functional analysis. This agent should be called after seeding the SPECIES/GENE/GENE-ai-review.yaml file, which seeds each annotation with `action: PENDING`; these should all be manually reviewed.
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。
Review, either de-novo, or augment the review of an existing one. A list of deep research providers may be specified.
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, review, or repair ai-gene-review ModuleReview YAML documents under modules/, including pathway/module boundary setting, parts and variant modeling, annoton-level molecular functions, representative UniProt/PTN grounding, module deep-research provenance, validation, rendering, and project batch updates.
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Use this agent when you need to create a pathway summary for a gene after completing the main gene review. This agent should be invoked specifically to generate GENE-pathway.md files that summarize the gene's role in biological pathways with proper citations and mermaid diagrams.
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Use this agent when you need to extract and summarize key findings from scientific references that are relevant to a gene's function. This agent should be used after references have been identified in a gene review to provide detailed supporting evidence from the literature. The agent will look up cached publications and extract only the most relevant findings related to the gene's direct function, excluding irrelevant experimental details unless they directly inform gene function.
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 agent when you need to review UniProt annotation rules (ARBA, UniRule) for quality, biological accuracy, and GO annotation appropriateness. This agent performs comprehensive analysis of rule condition sets, evaluates literature support, assesses taxonomic scope, and recommends curation actions.
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Use this agent when you need to synthesize and distill the core functions of a gene from multiple information sources (textual summaries, reviewed annotations, bioinformatics analysis) and populate the `core_functions` section of a gene review YAML file. This agent should be called after completing the gene research, annotation review, and bioinformatics analysis phases of a gene review project.
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Open the warm channel for a role the pipeline already assessed. Finds who the user already knows at the company, researches named targets from public professional presence when they know nobody, grades each path honestly, drafts the ask through the voice agent, and writes the contacts into the Referrals table in job_search.md.
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Fierce autonomous internal co-founder. Bounded web research for evidence-backed improvements. Structured idea formulation with self-critique filter that rejects ideas not serving the goal. Auto-implements high-confidence aligned improvements, proposes medium-confidence ideas for approval. Evaluates viral coefficient, AI search visibility (GEO), and solo SaaS economics. Considers higher pursuits: employing disabled people, spiritual tech investigation, 99% wealth donation ethos.
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说明如何读取和写入 IELTS Buddy 学习计划、精确课程与练习内容、任务提醒、学习路径、资源和学习事件；不定义诊断、推荐或计划内容。
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在已绑定且具备 IELTS Buddy 后台题库权限的账号下，发现并调用只读题库数据接口；不定义题库研究或分析方法。
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ERPNext-aware reasoning for accounting, selling, buying, stock, manufacturing, projects, HR, support, education, CRM, and other ERPNext module work. Use when a task affects ERPNext module behavior, configuration, reports, dashboards, workflows, or customizations.
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Frappe LMS customization guidance for courses, batches, lessons, quizzes, assessments, enrollment, progress, certificates, portals, and learning workflows. Use when work touches Frappe LMS or education/training flows.
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Investigation flow (Glob -> Grep -> Read), evidence-based research with file:line references, structured output format for AI consumption. Use for pattern discovery, implementation research, and codebase investigation.
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Research what people have discussed in the last 30 days across high-signal public platforms. Use when the user asks for `/last30days`, recent trend research, competitive intelligence, market pulse briefs, or engagement-ranked synthesis from Reddit, Hacker News, GitHub, X, YouTube, Polymarket, and similar sources.
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。