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3,925 件 ・ 関連度順

概要と使いどころ

Security audit of Solidity code while you develop. Trigger on "audit", "check this contract", "review for security", "loop mode", "run the auditor in loop mode", "run 3 passes". Modes - default (full repo) or a specific filename. Loop mode runs several passes in one scan, each pass told what the earlier ones found, and ends in one combined report; it remembers findings between scans in a ledger.

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。

pashov/skills1,2662026年10月5日 更新

Use when writing the response to an American Anthropologist (AA) revise-and-resubmit. AA uses anonymous peer review under an ethics of care, often with cross-subfield reviewers, so the response must convert each reviewer while keeping the editor confident and the ethnographic contribution intact. Structures the response letter; it does not fabricate new evidence.

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。

brycewang-stanford/Awesome-Journal-Skills1,2642026年9月27日 更新

Use when defending the research design of an American Anthropologist (AA) manuscript — fieldwork and participant observation, interview and archival design for ethnographic/qualitative work, material/contextual analysis for archaeology, and appropriate lab/quantitative design for biological anthropology. AA judges each mode on its own terms with reflexivity. Strengthens the design; it does not write code or run fieldwork.

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。

brycewang-stanford/Awesome-Journal-Skills1,2642026年9月27日 更新

Use when writing the response to an American Educational Research Journal (AERJ) revise-and-resubmit. AERJ R&Rs come back through the handling editor and multiple masked reviewers, so the response must convert each reviewer while keeping the editor confident the revision is convergent. Structures the response letter; it does not fabricate new results.

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。

brycewang-stanford/Awesome-Journal-Skills1,2642026年9月27日 更新

Use when defending the research design of an American Educational Research Journal (AERJ) manuscript — quantitative (multilevel, IRT, quasi-experimental, RCT), qualitative (case study, ethnography, interview), or mixed methods. AERJ judges each tradition on its own terms against the AERA reporting standards. Strengthens the design; it does not write code.

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。

brycewang-stanford/Awesome-Journal-Skills1,2642026年9月27日 更新

Use when building or trimming the exhibits of an American Economic Review: Insights (AER: Insights) short-format manuscript to the hard five-exhibit budget, one page each, no significance asterisks. Designs the minimum exhibit set; it does not run the analysis behind them.

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。

brycewang-stanford/Awesome-Journal-Skills1,2642026年9月27日 更新

Use when writing the response to an Agricultural Systems (AgSy) revision decision. AgSy review is single anonymized with at least two expert systems reviewers and an editor decision, and revisions often turn on model evaluation, sensitivity/uncertainty, and trade-off framing. The response must convert each reviewer without alienating the editor. Structures the response letter; it does not fabricate new results.

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。

brycewang-stanford/Awesome-Journal-Skills1,2642026年9月27日 更新

Use when understanding and navigating Administrative Science Quarterly's (ASQ) developmental, multi-round review process — decision types, reviewer/editor roles, and what each stage expects. Explains the process; it does not draft the response letter (see asq-rebuttal).

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。

brycewang-stanford/Awesome-Journal-Skills1,2642026年9月27日 更新

Use when planning the end-to-end ACM MM (ACM Multimedia) campaign calendar — from thematic-area scoping and track choice, through the April OpenReview abstract/paper/supplement chain, the June anonymous rebuttal, the July decision, the August camera-ready, and the November presentation in Rio, with the right skill invoked at each stage.

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。

brycewang-stanford/Awesome-Journal-Skills1,2642026年9月27日 更新

Use when strengthening the reproducibility of an ACM MM (ACM Multimedia) paper or preparing for the ACM MM Reproducibility track and ACM artifact badging — capturing environments, media/data access, seeds, and multimodal pipelines so an independent reviewer can rebuild results and reach Artifacts Evaluated or Results Reproduced badges.

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。

brycewang-stanford/Awesome-Journal-Skills1,2642026年9月27日 更新

Use when packaging code, models, datasets, or media as ACM MM (ACM Multimedia) artifacts — building the anonymous review package versus the public release, and choosing between the Open Source Software Competition, the Dataset track, the Reproducibility track, and main-track supplementary evidence, each with its own blinding and expectations.

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。

brycewang-stanford/Awesome-Journal-Skills1,2642026年9月27日 更新

Use when you need to understand how Accounting, Organizations and Society (AOS) evaluates a manuscript — triage by the joint Editors-in-Chief, double-anonymized review by interdisciplinary referees, the meaning of each decision type, and how to read the editor's letter. Explains the process; it does not draft the revision response (aos-rebuttal).

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。

brycewang-stanford/Awesome-Journal-Skills1,2642026年9月27日 更新

Use when designing the study for an Accounting, Organizations and Society (AOS) manuscript — qualitative field studies (access, case logic, interviews, observation), behavioral experiments, surveys, historical/archival inquiry, or theory-laden archival designs, each with the ethics and site-anonymity groundwork done early. Designs the inquiry; it does not run the coding or estimation (aos-data-analysis).

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。

brycewang-stanford/Awesome-Journal-Skills1,2642026年9月27日 更新

Use when constructing or auditing accounting measures for 《会计研究》 (Accounting Research) — discretionary accruals (modified Jones), real earnings management (Roychowdhury), conservatism (Basu / C-Score), comparability, disclosure indices, audit-quality and tax-avoidance proxies — so each measure is built transparently and reproducibly. Use before 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。

brycewang-stanford/Awesome-Journal-Skills1,2642026年9月27日 更新

Use when analyzing the material of an Accounting, Organizations and Society (AOS) manuscript — coding and interpreting qualitative field data, estimating experimental and survey models, or running theory-laden archival analyses, with an audit trail appropriate to each tradition. Analyzes and reports; it does not design the study (aos-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。

brycewang-stanford/Awesome-Journal-Skills1,2642026年9月27日 更新

Use when stress-testing the LOGIC of an Academy of Management Review (AMR) theory manuscript — checking logical coherence, running thought experiments and counterfactuals, addressing alternative explanations and disconfirming cases, and verifying each proposition follows from its argument. This is ARGUMENT DEVELOPMENT, NOT data analysis; AMR publishes no datasets, no statistics, and no empirical results.

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。

brycewang-stanford/Awesome-Journal-Skills1,2642026年9月27日 更新

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.

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。

gooseworks-ai/goose-skills1,2542026年10月16日 更新

Find Twitter/X influencers to promote a product or brand. Use when asked to find influencers, discover Twitter accounts for partnerships, identify creators in a niche, or build an influencer outreach list.

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。

gooseworks-ai/goose-skills1,2542026年10月16日 更新

Discover top LinkedIn influencers and voices by topic, industry, follower count, and country. Use when you need to find the top 100 voices in a space, build influencer lists for outreach, or identify thought leaders on LinkedIn.

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。

gooseworks-ai/goose-skills1,2542026年10月16日 更新

Find Key Opinion Leaders (KOLs) in a given domain by combining web research with LinkedIn post search. Given a company/idea and target domain, generates authority keywords, searches LinkedIn posts to find prolific authors with high engagement, and merges with web-researched influencers. Use when someone wants to "find influencers in X space" or "who are the KOLs for Y industry."

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。

gooseworks-ai/goose-skills1,2542026年10月16日 更新

Handles disqualified and near-miss inbound leads gracefully. Drafts polite rejection emails, referral requests (right company wrong person), and nurture routing (future fit). Ensures no inbound lead gets ignored and every disqualification preserves the relationship. 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。

gooseworks-ai/goose-skills1,2542026年10月16日 更新

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.

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。

gooseworks-ai/goose-skills1,2542026年10月16日 更新

Find attendees at conferences/events, research their companies, qualify against ICP, and launch outreach

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。

gooseworks-ai/goose-skills1,2542026年10月16日 更新

For churned accounts, research what has changed since they left — new funding, team growth, competitor dissatisfaction, product updates that address their pain — then assess re-engagement potential and generate a personalized win-back email sequence with timing recommendations. Chains web research and LinkedIn monitoring with email sequence generation.

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。

gooseworks-ai/goose-skills1,2542026年10月16日 更新