3,775 件 ・ 関連度順
概要と使いどころ
Runs your conversion data through different attribution models side by side — last click, first click, linear, time decay, position based, and data-driven. Shows you how credit shifts between campaigns depending on the model so you can make better budget decisions instead of over-investing in last-touch campaigns. Platform: Google and Meta.
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Analyzes your conversion lag data to determine the optimal retargeting window for each audience segment. Tells you whether your 30-day retargeting window should actually be 7 days, 14 days, or 60 days based on when people actually convert after their first visit. Platform: Meta.
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。
Given your total budget, recommends the optimal split across Google Search, PMax, Meta prospecting, Meta retargeting, and any other active channels based on your last 60-90 days of marginal ROAS and CPA by channel. Tells you where each additional dollar produces the best return. Platform: Google and Meta.
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。
Focus on explaining the practical configuration and usage of XRequest, providing accurate configuration instructions based on official documentation
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。
Analyze any TRES Finance report XLSX and produce an automatic findings summary. Trigger this skill whenever a user uploads a .xlsx file that came from TRES Finance and asks to "analyze", "summarize", "review", "check", "audit", "look at", "what does this show", "any issues", "anything interesting", or "walk me through" the report. Also trigger when the user uploads an Excel file and mentions TRES, reconciliation, balances, transactions, cost basis, roll forward, or ERP sync. The skill identifies the report type from the file structure, runs targeted analysis based on report-specific logic, and produces a clear findings summary highlighting anomalies, key metrics, and action items.
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。
Software project effort estimation assistant. Outputs three-point estimates (optimistic/most-likely/pessimistic values with confidence intervals), T-shirt sizes, or Function Point Analysis (FPA) counts. Triggered when users ask 'how long will this feature take,' need to assess project workload, perform PERT estimation, T-shirt sizing, FPA, sprint planning, or quote-based effort breakdowns.
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。
Provide CI/CD best practices and pipeline templates for GitHub Actions and GitLab CI, recommending configurations based on project type (frontend, backend, fullstack, library, monorepo, mobile). Trigger when users ask about setting up CI/CD, automating builds, improving pipelines, or mention keywords like GitHub Actions, GitLab CI, pipeline templates, or deployment automation.
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。
React Three Fiber animation - useFrame, useAnimations, spring physics, keyframes. Use when animating objects, playing GLTF animations, creating procedural motion, or implementing physics-based movement.
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。
This skill should be used when the user asks to "remove AI writing patterns", "humanize this text", "make this sound more natural", "remove AI-generated traces", "fix robotic writing", or needs to eliminate AI writing patterns from prose. Supports both English and Chinese text. Based on Wikipedia's "Signs of AI writing" guide, detects and fixes inflated symbolism, promotional language, superficial -ing analyses, vague attributions, AI vocabulary, negative parallelisms, and excessive conjunctive phrases.
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。
Publication Specialist - Writing, Review, Pre-registration & Quality Assurance Light VS applied: Avoids template-based writing + audience-specific message design Absorbed G3 (Peer Review Strategist), G4 (Pre-registration Composer), F1-F3 (Quality functions) capabilities Use when: writing abstracts, creating summaries, peer review response, pre-registration, reporting checklists, reproducibility Triggers: abstract, plain language, press release, summary, communication, peer review, revision, pre-registration, OSF, PRISMA, CONSORT, reproducibility
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。
VS-Enhanced Journal Matcher with Journal Intelligence MCP — Real-time journal data pipeline with checkpoint-based human decisions. Uses OpenAlex + Crossref APIs for live metrics. Light VS applied: Avoids IF-centric recommendations + multi-dimensional matching strategy Use when: selecting target journals, planning submissions, comparing publication options Triggers: journal, submission, impact factor, academic journal, publication, submit
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。
This skill covers structural econometric models. Use when the user is building, estimating, or debugging structural models — including BLP demand estimation, dynamic discrete choice, auction models, or any workflow involving moment conditions, nested fixed-point algorithms, or MPEC formulations. Triggers on "structural model", "moment conditions", "NFXP", "MPEC", "BLP", "random coefficients", "dynamic discrete choice", "CCP", "Rust model", "auction estimation", "GMM objective", "inner loop", "contraction mapping", or convergence/starting value problems in optimization-based 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。
Revise an academic paper based on journal referee reports. Reads referee comments from review report or annotated manuscript, then directly modifies main.tex one comment at a time with user approval. Generates response letter after revision. Trigger when user says "referee revise" / "paper-referee-revise" / "审稿意见修改" / "根据审稿人意见修改" / "referee report".
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。
This skill covers causal machine learning methods in applied economics and quantitative social science. Use when implementing or choosing between modern ML-based causal estimators — including double machine learning, DML, partially linear models, interactive regression models, cross-fitting, Neyman orthogonality, debiased ML, causal forests, generalized random forest, GRF, honest causal trees, AIPW with machine learning, doubly robust with machine learning, DR-Learner, T-Learner, S-Learner, X-Learner, meta-learners, heterogeneous treatment effects, conditional average treatment effect, CATE, HTE, high-dimensional controls, LASSO controls, post-LASSO, post-double selection, Belloni-Chernozhukov-Hansen, Riesz representer, Chernozhukov, sample splitting, econml, DoubleML package, or any combination of machine learning and causal inference.
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。
Revise an academic paper based on internal review comments. Reads review report or annotated manuscript, then applies revisions one by one with user approval. Trigger when user says "self revise" / "paper-selfrevise" / "内部修改" / "根据审稿意见修改".
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。
Generate a complete academic literature survey from scratch using multi-agent orchestration. Searches academic databases (OpenAlex, CrossRef, Unpaywall), downloads PDFs, builds BibTeX, drafts a LaTeX review, compiles it, reviews for quality, and revises based on feedback — all automated. Use this skill whenever the user asks to "generate a literature review", "write a survey paper", "review recent papers on [topic]", "create a survey of [field]", "综述", "文献综述", "survey papers in top journals", or wants a multi-agent pipeline to produce a publishable-quality literature review. Also trigger when the user wants to search multiple journals for papers on a topic and synthesize findings into a structured document. Works for any academic field but optimized for economics journals.
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。
Remove signs of AI-generated writing from academic medical papers. Use when editing or reviewing manuscripts to make them sound more natural and professionally written. Based on Wikipedia's "Signs of AI writing" guide, adapted for medical literature.
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。
This skill should be used when the user asks to "implement a DiD regression", "write a causal inference pipeline", "set up an event study", "implement instrumental variables", "run a regression discontinuity design", "build a synthetic control model", "implement propensity score matching", "write parallel trends test", "implement Bacon decomposition", or needs code templates for causal inference methods in Python, R, or Stata. Based on Scott Cunningham's Causal Inference: The Mixtape.
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。
Generate academic research proposals for PhD applications. Use when user asks to "write a research proposal", "create PhD proposal", "generate research plan", "撰写研究计划", "写博士申请", "doctoral proposal", or mentions specific research topics for PhD application. Supports STEM, humanities, and social sciences with field-specific adaptations. Follows Nature Reviews-style academic writing conventions. Supports both English and Chinese output based on user preference.
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。
Apply the ScholarEval framework to systematically evaluate scholarly work across quality dimensions (novelty, rigor, significance, and clarity). Use to critique academic papers, research proposals, literature reviews, or grant applications and produce a structured, criterion-based assessment.
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。
Write a blameless incident postmortem under postmortems/ following the Google SRE shape — evidence-based timeline, trigger vs root cause vs symptom, contributing factors, what went well, and owned+dated+verifiable action items. Read when asked to write a postmortem, do an incident review, run a root cause analysis, write up the outage, retro on the outage, or when the user says we had an incident and wants it documented. Do NOT read to frame a proposal (use frame-a-proposal), write a spec (use write-a-spec), record a decision (use record-a-decision), or review a design (use review-a-design) — a postmortem documents an incident that already happened, it does not propose, specify, decide, or critique future work.
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。
Cloud-based AI swarm deployment and event-driven workflow automation with Flow Nexus 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。
Read a `.cs` script file and return its content as a string. Supports a 1-based `lineFrom`/`lineTo` slice for partial reads. Pair with 'script-update-or-create' to write back.
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。
Render a target GameObject from a chosen camera angle with optional layer-based isolation, configurable background (solid/skybox/transparent), multi-light setup via JSON, and Composite (2x2 Front/Right/Back/Top) mode. Returns a PNG image. When isolated=true, inactive children may briefly fire OnEnable — see the body for side-effect notes.
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。