7,984 件 ・ 関連度順
概要と使いどころ
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 the question is what makes the result tight or what the data identify for an American Economic Journal: Microeconomics (AEJ: Micro) manuscript — covering both (a) structural/empirical-IO and experimental identification and (b) for pure theory, which assumptions drive the result and how robust the mechanism is. Stress-tests credibility; it does not build the model (see aejmic-theory-model).
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 Agricultural Systems (AgSy) manuscript against the literature so it reads as a systems contribution. AgSy readers span agronomy, modelling, livestock, economics, environment, and food-system science, so the paper must engage the systems and modelling literatures they expect — not only one subfield. Stakes the contribution; 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 positioning the contribution of an American Economic Review: Insights (AER: Insights) short-format manuscript against the closest published work in one or two tight paragraphs. Stakes the gap concisely; it does not write the full literature review (there is no room for one).
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 marginal contribution of an American Economic Journal: Applied Economics (AEJ: Applied) manuscript is fuzzy or undersold and must be staked precisely against prior applied-micro work. Positions the contribution; it does not fix the identification (aeja-identification) or the prose (aeja-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 deciding whether a project fits AEJ: Economic Policy rather than J. Public Economics, AEJ: Applied, or AER, and when sharpening the policy question and its welfare stake for an AEJ: Economic Policy manuscript. Frames fit and the policy-question-first contribution; it does not design the identification or run estimation.
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 drafting or revising the prose of an AEJ: Economic Policy manuscript — especially the abstract and introduction — to translate causal estimates into a clear policy takeaway without overclaiming. Shapes the policy-first narrative and house style; it does not design identification or build the welfare model.
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 placing the contribution against the policy-evaluation literature and sharpening it as a policy answer for an AEJ: Economic Policy manuscript. Stakes the contribution and the policy-relevant "so what"; it does not write the full introduction or design the identification.
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 executing and reporting the analysis for an Administrative Science Quarterly (ASQ) manuscript — qualitative coding and data-to-theory construction, or quantitative estimation and robustness. Makes the evidence-to-theory link transparent; it does not design the study (see asq-methods).
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 revising an ACM MM (ACM Multimedia) paper for house style — putting the cross-modal contribution on the first page, framing media (figures, video, audio) as evidence rather than decoration, making the fusion the visible claim, and compressing the argument into a 6-8 page ACM sigconf body with references-only overflow.
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 packaging ACM CCS artifacts for the artifact-evaluation committee and the ACM badges — Artifacts Available, Artifacts Evaluated Functional, Artifacts Evaluated Reusable, and Results Reproduced — covering what security evaluators inspect, how to make attacks and defenses turnkey, and how to justify withheld artifacts.
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 building the theoretical engine of an Accounting, Organizations and Society (AOS) manuscript — choosing a social/organizational theory tradition (institutional theory, governmentality, Bourdieu, actor-network theory, practice theory, psychological process theory) and making it generate the analysis rather than decorate it. Builds the lens; it does not collect data (aos-methods) or frame the contribution (aos-contribution-framing).
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 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).
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。
管理本地 Markdown/Obsidian 知识库：素材入库、健康检查、增量索引、关键词与 semantic-lite 检索、逐字原文资料包、归档和 MCP 连接。用于搜索知识库、整理资料及为 Agent 准备可定位的引用。
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Takes an existing screen recording or demo video and adds professional zoom/pan effects synchronized to the narration. Uses transcript-driven zoom targeting and Remotion for rendering. Optionally replaces audio with a soundtrack.
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。
Repurposes long-form video (podcasts, interviews, talks) into short-form vertical clips for Instagram Reels, TikTok, and YouTube Shorts. Handles transcription, moment selection, clip extraction, speaker-tracked reframing (16:9 to 9:16), and animated captions.
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。
Stitch video segments with ffmpeg concat, xfade, overlay, audio mux, and export settings, and trim a source into named clips at exact windows (trim_clips.py). Ships montage.py, a free montage assembler (python3 + ffmpeg, no keys). It takes a JSON EDL of clips and stills, normalizes them and hard-cuts them in order, burns captions from an SRT, a cue list or word timings, and lays a VO over a music bed that ducks under it, mastered to -14 LUFS.
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。
Extract leads from conferences, meetups, hackathons, and podcasts by analyzing speaker lists, sponsor lists, hackathon entries, and podcast guests. Discovers events via Sessionize, Confs.tech, Meetup, Luma, ListenNotes, and Devpost. Looks back 90 days and forward 180 days.
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。
Track product champions for job changes and qualify their new companies against ICP. Takes a CSV of known champions (with LinkedIn URLs), creates a baseline snapshot via Apify enrichment, then detects when champions move to new companies. Scores new companies on a 0-4 ICP fit scale. Outputs a downloadable CSV of movers with qualification verdicts.
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。
End-to-end outbound prospecting: detect intent signals, research companies, find decision-maker contacts, personalize messaging, launch campaign.
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。
End-to-end funding signal composite. Takes any set of companies, detects recent funding events, qualifies against your company context, finds relevant people (buyers, champions, users), and drafts personalized outreach. Tool-agnostic — works with any company source, contact finder, and outreach platform.
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。
End-to-end champion/buyer/user job change signal composite. Takes a set of known people (past buyers, champions, power users), detects when they move to a new company, researches the new company for ICP fit, and drafts personalized outreach leveraging the existing relationship. Tool-agnostic — works with any people source, detection method, and outreach platform.
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。
Research LinkedIn profiles and write personalized messages for any LinkedIn message type — connection requests, InMails, DMs, message requests, post comments, and comment replies. Takes LinkedIn URLs as input, researches each person (profile data + recent posts via Apify), and generates messages tailored to each lead's background, interests, and recent activity. Exports tool-ready CSVs for Dripify, Expandi, Botdog, PhantomBuster, or generic format. No LinkedIn cookies or login required.
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。
Fills in missing data for inbound leads — researches the company, identifies the person's role and seniority, finds other stakeholders at the company, checks for existing CRM relationships, and updates the lead record. Produces enriched lead data ready for qualification or outreach. Tool-agnostic.
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。