调研、论文、教育、考证学习（4450 个）
International journal scout — searches candidate SCI/SSCI/EI/Scopus journals via Wanfang foreign journal API, performs L1 deterministic rejection screening, and L2 citation fingerprint analysis.
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个股研究员（颜一深）。擅长单只股票全方位深度研究——业务构成、竞争格局、财报解读、公司质地评分、投资逻辑提炼。 在 A 股研究团队中由主理人调度，参与 Workflow 1 / Workflow 4。 擅长：公司基本面全景（业务、行业地位、护城河）、财报核心指标解读（营收/利润趋势、现金流、ROE）、公司质地多维评分（商业模式/管理层/财务健康/成长性，100 分制）、投资逻辑提炼（核心看点 + 主要风险）、近期重大事件与动态。
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草稿审稿人（明鉴秋 Ming）。由 research-chief-editor 主理人调度，按 6 维标准审查章节草稿（来源充分性、事实准确性、观点均衡性、内容深度、结构清晰度、格式规范性），输出 PASS 或 REVISE 判定。尊重 3 轮循环规则：第 3 轮强制 PASS。 触发词：由 research-chief-editor 主理人调度执行，不直接面向用户。当主理人下发 Phase 3.2 审稿任务时激活。
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学术选刊顾问团 v3.0 主理人 — 输入论文主题+摘要+全文，自动推荐中文普通/中文核心/SCI/SSCI投稿期刊。双管道并行分析（中外刊独立匹配），冲稳保三档策略，附录用概率、审稿周期、证据链。
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International journal matcher — performs L3 6-dimensional matching for SCI/SSCI/EI/Scopus journals, computes conditional acceptance probability with review cycle estimation, applies 4-layer weighted ranking, and outputs tiered submission strategy including CAS warning screening and Chinese scholar friendliness analysis.
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深度研究团队主理人（顾全之 · 研究主编）。带领 6 位专业成员按 5 阶段工作流（初调→规划→逐章深研→报告组装→发布输出）完成系统性深度研究，输出带多源引用的专业研究报告。 触发词：深度研究、行业研究、竞品分析、市场调研、学术综述、出一份研究报告、专题研究、趋势分析、技术综述。
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Chinese journal matcher — performs L3 6-dimensional matching (keywords, cited direction, trend, solicitation, institution, cross-discipline), calculates conditional acceptance probability with review cycle estimation, and outputs tiered submission strategy (aspiration-stable-safety) using 4-layer weighted ranking.
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研究编辑（季要纲 Ji）。由 research-chief-editor 主理人调度，分析 Phase 1 初步调研摘要，提取核心观点，确定报告标题，规划章节大纲（3-5 章），管理章节不重叠、逻辑递进。也负责根据用户反馈修改大纲。 触发词：由 research-chief-editor 主理人调度执行，不直接面向用户。当主理人下发 Phase 2 规划任务时激活。
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Investor Targeting Specialist. Use to identify, qualify, and tier investors for a round based on stage, sector, geography, check size, and portfolio fit.
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Market Research Specialist. Use to analyze target markets, ICP segments, demand patterns, competitive terrain, and strategic whitespace for founder decisions.
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Gathers information from multiple sources in parallel, compares what they say, flags where they agree or conflict, and delivers a clean synthesis with a confidence rating for each finding. Useful for making evidence-backed decisions, when evaluating competing claims, during due diligence on a vendor or market, and for building research briefs before strategy sessions. Use when the user says "research this", "synthesize multiple sources", "compare sources", "research report", "what does the evidence say", "gather information on", or "deep dive into".
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Shows you how to use Cowork's parallel workers to run multiple tasks at the same time instead of one after another — dramatically cutting the time on batch work. For processing large batches of files or research tasks, when running multiple independent analyses simultaneously, or during a performance optimization session. Use when the user says "speed up", "parallel workers", "batch task", "process multiple files", "why is cowork so slow", "faster", "do several things at once", or "can cowork multitask".
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Радар фундаментальных публикаций про автономных AI-агентов в блогах AI-лабораторий и продуктовых команд (Anthropic, Claude, OpenAI, Cursor, LangChain, Amp, Cognition). Собирает ленты и HTML-индексы за окно дат, отсеивает продуктовые анонсы и релизы моделей, оставляет глубокие посты про архитектуру агентов: память и контекст, long-running циклы, self-improvement, оркестрация, экономика прогона, эвалы, риски длинных горизонтов. Отдаёт дайджест со ссылками, сутью каждого поста и тем, что забрать в свою работу. Use when (используй когда): пользователь хочет узнать, что нового вышло в блогах про AI-агентов, собрать дайджест за неделю или период, отследить исследования про агентную память. Триггерится на: "радар", "дайджест", "что нового в блогах", "публикации про агентов", "agent research", "что вышло за неделю", "блог Anthropic", "cursor blog", "langchain blog", "long-running agents", "research digest". Do NOT use for (не для): корпоративные вики и трекеры; arXiv; доставка сообщений.
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The Arbor executor workflow: clone the repo from the git bundle your brief hands you, implement and evaluate exactly ONE hypothesis with the file tools, then report structured results via worker_complete. Preloaded automatically on arbor-executor task workers. Never touch the held-out test split.
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Hard-gated ideation for an Arbor research run. Load at the START of every IDEATE round, before drafting any hypothesis. Enforces the PI mindset (mechanism over knob), the four-question first-principles probe, the kill-filter, and the four-line hypothesis format. Ported from Arbor's idea_drafting + first_principles_probe.
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Intake for an autonomous research run (Arbor). Use when the user wants to optimize a metric in a repo over many isolated experiments — 'optimize this benchmark', 'improve the model F1 overnight', 'beat the leaderboard'. Discovers the repo/eval/splits, measures the baseline, confirms a Research Contract, then emits a ready-to-send /auto-research command. Does NOT start the run itself.
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DECIDE-phase doctrine for an Arbor research run: when to merge, prune, combine, or finalize; how the held-out merge gate works; and the rule that the final report uses TEST scores. Load before deciding what to do with completed experiments.
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The Arbor research-coordinator protocol: the OBSERVE -> IDEATE -> SELECT -> DISPATCH -> DECIDE cycle over a durable Idea Tree. Preloaded automatically on /auto-research sessions. The coordinator never edits code or runs commands — executors do that in isolated worktrees; the coordinator steers the tree with idea_tree / dispatch_experiments / merge_experiment.
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Detect and remove the tells of AI-generated writing in BOTH English and Spanish, and read back the evidence honestly. Use when the user asks to "de-AI" / "humanize" / "un-slop" a draft, to examine whether text carries the tells (delve, tapestry, "it's not just X, it's Y", "here's the thing", em-dash overuse, an assistant's own closing line left in the document), to compare documents for overlap, or mentions signs-of-ai / SignsOfAI. Backed by the SignsOfAI engine — for a measured 0–100 score, sentence-rhythm burstiness, originality, citations, a writer baseline or perplexity, hand off to that engine (web app, CLI or MCP server) as described below. It cannot determine who wrote a text and must never imply that it can.
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High concurrent SQLite access causing lock contention and missing indexes Keywords: sqlite, wal, indexes, performance
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Investigate a question against high-trust primary sources and capture the findings as a Markdown file in the repo. Use when the user wants a topic researched, docs or API facts gathered, or reading legwork delegated to a background agent.
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Reasoning data lost across agent steps and missing low-confidence fallback Keywords: sqlite, triangulation, escalation, confidence
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Perform triangulated cognitive research combining SQLite memory, GitHub 10k+ stars patterns, and ToT DAG benchmarking before critical architectural decisions. Automatically triggers subagent research escalation when confidence is low (<0.70). Keywords: research, benchmark, github, memory, triangulation, grounding, best-practices, escalation
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Delegate implementation (or, when explicitly requested, research) to GPT-6.1 Sol (high reasoning; xhigh on request) via Codex CLI. Claude plans, orchestrates, and reviews; Sol writes the code.
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