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概要と使いどころ
Build agents on OpenAI's platform patterns — assistants-style threads, function calling, structured outputs, and multi-step tool workflows. Use when working with OpenAI models as the agent engine.
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。
Marketing analytics and data-driven optimization for B2B SaaS campaigns. Use this skill when analyzing marketing performance, optimizing conversion rates, running A/B tests, conducting customer research, creating marketing dashboards, building attribution models, analyzing funnel metrics, or storytelling with data. Also triggers on: marketing ops, performance analytics, CRO, A/B testing, data storytelling, customer insights, tracking plan, event taxonomy, UTM taxonomy, GA4 audit, consent mode, instrumentation contract, sample size calculation, minimum detectable effect, test duration, not enough traffic to A/B test, statistical significance, Bayesian vs frequentist, sample ratio mismatch, experiment feasibility, martech stack, marketing tech stack audit, tool consolidation, build vs buy, vendor evaluation, shadow IT tools, ESP/CRM/platform migration, switching cost, cutover plan, suppression list migration, data portability, customer interviews, survey design, sampling frame, non-response bias, how many people should I interview, thematic saturation, voice of customer, jobs-to-be-done, buyer persona research, is this finding real, data enrichment, which enrichment provider should we buy, waterfall enrichment, contact data quality, match rate, our list data is bad, email verification, catch-all domain, accept-all, bounce rate from bad data, B2B data decay, how often should we re-verify contacts, ZoomInfo vs Apollo vs Clearbit evaluation, cost per usable record, where did this list come from, GDPR Article 14 notice, purchased list compliance, suppression screening, TAM list building, demand planning, marketing plan, how many leads do we need, what pipeline target should marketing carry, funnel model, top-down vs bottom-up plan, plan of record, annual marketing planning, quarterly planning, pipeline coverage we have to create, sales cycle lag, our leads cannot close this quarter, carry-in pipeline, scenario planning, conservative moderate aggressive forecast, stretch target, reforecast, re-forecast triggers, assumption register, capacity ceiling, addressable audience ceiling, marginal cost per lead, blended CPL is wrong, we missed plan and nobody knows why, AI policy for the marketing team, AI governance, AI acceptable use policy, which AI tools are we allowed to use, can I paste customer data into ChatGPT, shadow AI, AI use register, human review of AI output, when can we stop reviewing every AI draft, can the agent send on its own, the AI made a mistake now what, pull back AI autonomy, AI evals, our AI vendor changed the model, AI output went out wrong, prompt injection, can our AI read prospect replies safely, lethal trifecta, the AI leaked internal notes, turn off auto-generated ad assets, auto-applied recommendations, AI literacy training, EU AI Act Article 4, duplicate records in the CRM, dedupe before import, which value wins in a merge, CRM cleanup.
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。
Diagnose and fix slow pyfixest regression GRIDS (many feols/fepois calls run sequentially) that stay slow despite demeaner_backend="cupy64" and an idle GPU. Use when: (1) a script looping dozens of pf.feols models on a 100k+ row panel takes ~1 min/model, (2) process inspection shows ~1-1.5 cores busy and nvidia-smi shows ~0% GPU utilization with a resident cupy context, (3) planning any worker prompt that will run a model grid (robustness variants x FE structures x domains). Root cause: per-model CPU-side single-threaded fixed costs (formulaic model-matrix build, interaction construction, singleton detection, cluster vcov) dominate wall time; GPU demeaning is a small slice. Fix: shard the model grid across OS processes and/or use pyfixest multiple-estimation syntax; mandate this IN THE WORKER PROMPT.
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 this skill whenever the user asks to run Stata commands, estimate econometric models, work with .dta files, run a .do file, generate Stata output, or do any statistical analysis where Stata is involved. Also trigger when the user mentions Stata variables, Stata syntax, or econometric tasks where Stata is the natural tool, including regressions, IV estimation, diff-in-diff, RDD, panel data, clustering, summary statistics, and margins. Stata runs through pystata on StataNow 19.5 BE; configure once with stata_setup, then drive everything with stata.run() and exchange data directly with pandas. Prefer this skill over subprocess calls or .do-file shelling for Stata work, including cases where the user does not say pystata.
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 creating, maintaining, debugging, or reviewing real-browser end-to-end tests with Playwright or Cypress, including Page Object models, CI artifacts, traces, flaky tests, cross-page visual regression, and critical user journeys such as login, payment, permissions, or CRUD. For layer planning or tests close to UI components, choose the matching testing workflow first; Chinese triggers include E2E, end-to-end testing, Playwright, Cypress.
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。
Build read models by reducing events into queryable state
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。
Implement query handlers that read from event-sourced read models
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。
Create a complete Grain domain service with schemas, read models, commands, queries, and tests
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 source-aware mechanical-engineering judgment to research, analysis, coding, writing, teaching, research identity, and release work. Use for thermal-fluid systems, heat transfer, fluid mechanics, thermodynamics, HVAC, energy systems, turbomachinery, piping, multiphase flow, experiments, correlations, CFD, reduced-order models, AI/ML, uncertainty, engineering datasets, literature reviews, citations, manuscripts, reviewer revisions, Overleaf packages, figures, proposals, research software, reproducibility, public releases, engineering teaching materials, or a research-project logo and visual identity.
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 ENCODE functional genomics data for disease mechanism research. Use when the user wants to connect GWAS variants to regulatory elements, annotate disease-associated loci with functional data, identify therapeutic targets from epigenomic data, build disease regulatory models, cross-reference with clinical trials and drug databases, or conduct any disease-focused, pathology-driven, or clinical variant interpretation workflow. Covers the full pipeline from disease-tissue mapping through GWAS variant annotation, heritability enrichment, cancer epigenomics, drug target identification, and clinical trial cross-referencing. Integrates ENCODE with Open Targets, PubMed, ClinicalTrials.gov, and bioRxiv.
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。
Expert guidance for Domain-Driven Design architecture and implementation. Use when designing complex business systems, defining bounded contexts, structuring domain models, choosing between modular monolith vs microservices, implementing aggregates/entities/value objects, or when users mention "DDD", "domain-driven design", "bounded context", "aggregate", "domain model", "ubiquitous language", "event storming", "context mapping", "domain events", "anemic domain model", strategic design, tactical patterns, or domain modeling. Helps make architectural decisions, identify subdomains, design aggregates, and avoid common DDD pitfalls.
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。
Survey any codebase as a senior advisor and produce prioritized, self-contained implementation plans for OTHER models/agents to execute. Strictly read-only on source code — never implements, fixes, or refactors anything itself. Use when asked to audit a codebase, find improvement opportunities (bugs, security, performance, test coverage, tech debt, migrations, DX), suggest features or where to take the project next (roadmap, product direction), or generate handoff plans for another agent to implement.
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。
Defines service level objectives, creates error budget policies, designs incident response procedures, develops capacity models, and produces monitoring configurations and automation scripts for production systems. Use when defining SLIs/SLOs, managing error budgets, building reliable systems at scale, incident management, chaos engineering, toil reduction, or capacity planning.
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 affordable housing compliance and risk audit: analyze property management and waitlist operations, predict eviction risk with early warning models, review Fair Housing Act and HUD regulatory compliance, and model rent burden and subsidy accuracy across the portfolio..
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。
Pre-launch compliance and security gate for fintech apps: audit PCI DSS payment card handling, review financial API integrations for idempotency and error handling, evaluate fraud detection coverage, validate credit risk models for fairness, then run preflight checks.
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。
Build a production-ready data API from scratch: scaffold REST endpoints with models and validation, generate integration tests that verify every route, then load test for scalability. Use when you need an API backend, data service, CRUD layer, or microservice with verified correctness and performance.
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。
Audit customer service triage systems for ticket routing, classification, and SLA compliance. Triggers: you need to evaluate ticket auto-classification accuracy, priority scoring models, skill-based routing logic.
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。
Audit a real estate analytics platform -- evaluate comparable sales and rental engines, automated valuation models (AVM), demographic and economic indicator pipelines, submarket scoring, gentrification detection, price forecasting accuracy, market cycle analysis, and risk modeling.
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。
Audit rehabilitation setback prediction systems for clinical risk modeling and early intervention. Triggers: you need to evaluate risk factor models (Charlson, Elixhauser), early warning indicators for functional decline.
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。
Audit litigation analytics and case outcome prediction systems -- ML outcome models (logistic regression, gradient boosting, neural nets with temporal train/test splits), settlement range modeling (Monte Carlo simulation, comparable case matching, BATNA 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。
Analyze energy load forecasting systems including demand prediction models (ARIMA, Prophet, LSTM), weather API integration, peak shaving strategies, demand response program optimization, renewable intermittency handling, net load duck curve management.
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。
Audit demand forecasting models and inventory optimization logic -- forecast accuracy (MAPE, bias), safety stock calculations, reorder point strategies (EOQ, ROP, min/max), ABC/XYZ classification, demand signal pipelines, and multi-location network optimization..
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 donor retention and CRM systems — lapse risk scoring and prediction models, stewardship workflow automation, donor lifetime value (LTV) modeling, communication personalization engines, giving pattern analysis, and retention metric benchmarking.
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 food supply chain systems for waste reduction opportunities including shelf life prediction models, FIFO and FEFO inventory rotation enforcement, demand forecasting accuracy and bias, donation logistics workflows, cold chain temperature monitoring, and sustainability reporting against EPA Food Recovery Hierarchy and UN SDG 12.3 targets.
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。