Research & Learning skills

Use when responding to 《会计研究》 (Accounting Research) reviewer reports / an R&R — drafting a point-by-point response letter and 修改说明 that addresses accounting-measure construction, institutional/standard accuracy, identification, and information-mechanism concerns, while keeping the revision anonymous. Use only after the manuscript itself has been revised.

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Use when situating an Accounting, Organizations and Society (AOS) manuscript in the literature — entering one of the journal's own long-running conversations, engaging the interdisciplinary sources (organization theory, sociology, psychology) those conversations draw on, and marking precisely what the paper adds. Positions the argument; it does not build the theory itself (aos-theory-development).

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Use when drafting an AAAI author response (rebuttal) under the single short character-limited author-feedback window, the no-URL rule, no-new-results guidance, AI-generated-review handling, and the AAAI two-phase review process where Phase-2 papers receive one feedback round before SPC/AC discussion.

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Use when the front end of an Academy of Management Journal (AMJ) manuscript needs to engage the right literatures and stake a clear position in an ongoing theoretical conversation. Positions the paper among theories; it does not derive the hypotheses (amj-theory-development) or write the contribution paragraph (amj-contribution-framing).

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Use when systematically gathering, coding, and synthesizing a management/organization literature for an Academy of Management Annals (Annals) review — the search-and-coverage methodology and the choice of narrative vs. systematic vs. bibliometric integration. Builds the corpus and synthesis notes; it does not impose the organizing spine (amann-organizing-framework) or write prose.

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Use when drafting an AAMAS rebuttal to preliminary OpenReview reviews, covering the double-blind constraint, the no-new-results and no-revised-paper norms, how to answer game-theory, MARL, and systems reviewers at once, and how to give the area chair a clean acceptance rationale within a short response window.

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Use when positioning an AAMAS submission against multiagent, game-theory, and reinforcement-learning literature spread across AAMAS, AAAI, IJCAI, NeurIPS, ICML, EC, and JAAMAS, including arXiv and workshop versions, concurrent submissions, prior conference versions, and the cross-community citation coverage AAMAS reviewers expect.

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Use when preparing AAMAS supplementary material - proofs, extended game and mechanism definitions, extra multiagent experiments, opponent-set details, and the anonymized artifact zip - under the size cap, anonymity, and reviewer-discretion limits, including how to split a game-theory-plus-experiments paper across the 8-page body and the supplement.

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Use when strengthening AAMAS reproducibility evidence for interaction claims, including proofs and game definitions, opponent and population sets, self-play protocols, random seeds, compute, uncertainty on strategic outcomes, baselines, and consistency between what the paper claims about agents and what the artifact can actually show.

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Use when deciding whether a project is a strong AAMAS fit, comparing AAMAS with AAAI, IJCAI, NeurIPS, ICML, EC, and the JAAMAS journal, identifying whether the agents are truly the research object, naming the interaction primitive (solution concept, mechanism, coordination, negotiation), and sharpening the multiagent framing before writing begins.

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Use when writing the short proposal that gates an Academy of Management Annals (Annals) review — the ≤5-page, four-heading proposal submitted under the twice-yearly deadlines before any full review is invited. Builds the proposal; it does not gather the literature (amann-literature-synthesis) or run the portal preflight (amann-submission).

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Use when judging whether a management/organization literature is an Academy of Management Annals (Annals)-scale review topic — mature, important, fragmented, and integrable into a new framework — rather than a narrow review. Decides fit and frames the review's animating problem; it does not gather the literature (amann-literature-synthesis) or write the proposal (amann-proposal-framing).

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Use when scoping or sanity-checking a research question for an Academy of Management Journal (AMJ) manuscript — fit, theory-drivenness, and feasibility. Decides whether the idea can earn a theoretical contribution; it does not build the theory itself (use amj-theory-development) or position the literature (amj-literature-positioning).

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学习笔记自动化：视频/播客转录 → 知识点提取 → 闪卡生成 → 知识图谱更新。触发词：学习笔记、闪卡、Anki、知识提取、视频学习

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Step of the analyze-performance playbook: calls ended tests, scores last week's calls, writes this week's kill/scale/hold calls and drafts learnings for the brand brain. Run analyze-performance instead; not for direct use.

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Search LinkedIn posts by keywords, sorted by engagement or date. Use when researching what people are saying about a topic on LinkedIn, finding high-engagement content, identifying thought leaders, or discovering warm leads through post engagement. Returns author, post text, reactions, comments, shares, post URL, and date. No LinkedIn cookies or login required.

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Compare competitors' social content across platforms to identify recurring topics, formats, hooks, audience reactions, distribution patterns, and credible content opportunities for a brand.

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Evaluate whether a creator, influencer, or brand account reaches the right audience using public profile, aggregate demographic, geography, language, content, comment, and commerce signals. Use for creator comparisons, sponsorship fit, market fit, and audience-quality research.

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Compatibility alias for the original ScrapeCreators social scraping skill. Use scrapecreators-api for current endpoint guidance and the task-specific Brand Growth research skills for finished analysis.

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Prepare for investor calls by pulling upcoming meetings from Google Calendar, deeply researching each investor and their firm (website scraping, portfolio analysis, thesis extraction), checking for competitor conflicts, and outputting an honest prep sheet with compatibility assessments. Use when asked to prep for investor meetings, fundraising calls, VC meetings, or demo day.

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Neural web search - find similar content, extract pages, and run deep research

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Research VCs, angels, and investors - portfolio, thesis, contact info

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Web search, AI answers, content extraction, and async deep research

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Web research API with OpenAI-compatible chat completions and async tasks

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