データ・AIのスキル

How to read experiment results without fooling yourself. Confidence intervals, p-values, multiple testing, sequential testing, CUPED, heterogeneous treatment effects, ratio metrics, network effects, dashboard reconciliation, and the interpretation failures that produce confidently wrong shipping decisions. Use this skill whenever the user is reading a finished experiment result panel and about to make a ship, kill, or iterate decision, or when an experiment number does not match the dashboard number. Triggers on read experiment results, result panel, ship or kill decision, p-value, confidence interval, statistical significance, multiple testing, peeking, sequential testing, CUPED, variance reduction, heterogeneous treatment effects, ratio metric, network effects, inconclusive test, experiment versus dashboard mismatch. Use `experiment-design` instead when the test has not run yet and the question is hypothesis, sample size, duration, or what to test.

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。

How to read paid media dashboards without fooling yourself. Attribution models, platform reporting quirks, multi-platform reconciliation, ROAS vs LTV horizon traps, statistical noise in performance metrics, incrementality testing, and the failure modes that produce expensive lessons. Triggers on read paid media dashboard, attribution analysis, ROAS vs LTV, multi-platform reconciliation, ad incrementality, geo holdout, conversion lift study, ghost bidding, paid media reporting, board-deck paid media metrics, blended CAC, MMM, MTA, last-click attribution. Also triggers when a marketer is about to scale, kill, or rebudget a campaign based on platform metrics, or when reconciling platform reports against warehouse revenue.

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 or change a customer-system integration with explicit data mapping, permissions, retries and reconciliation. Use for connectors, imports, write-back and upstream APIs.

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。

Drive one AI agent from another through kolu's terminals: spawn a Claude Code / Codex / opencode session in a PTY, prompt it, watch the screen for its reply, read it, and prompt again — a create→send→snapshot loop run with the `kaval-tui` CLI directly (no MCP). `kaval-tui` writes input and reads scrollback; `pulam-tui` adds a precise agent-state done-signal when you drive hooked terminals. Triggers on "drive another agent", "send a prompt to a terminal agent", "have one agent prompt another", "agent drives agent", "orchestrate agents in terminals", "make Claude drive Codex", "prompt the agent running in that terminal", or wiring a loop where one coding agent supervises another.

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。

Evaluate code (especially LLM-generated) for structural simplicity using Rich Hickey's "Simple Made Easy" framework. Use this skill whenever reviewing a PR, diff, or code snippet for accidental complexity — particularly when the code was generated by an AI coding assistant and line-by-line review isn't feasible. Also use when the user asks about complecting, simplicity vs. easiness, structural coupling, or concept deduplication. Trigger on phrases like "is this simple", "does this complect", "review for complexity", "structural analysis", or any reference to Hickey, Simple Made Easy, or grey-box 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。

Expert LGPD compliance advisor for Brazil's Lei Geral de Proteção de Dados (Law 13,709/2018). Use this skill whenever a user asks about LGPD, Brazilian data protection, ANPD, personal data processing in Brazil, data subject rights under Brazilian law, legal bases for processing, sensitive data handling, DPO appointment in Brazil, data breach notification to ANPD, LGPD penalties (fines up to 2% of revenue / R$50M), international data transfers from Brazil, Brazil-EU mutual adequacy (January 2026 — SCCs/BCRs no longer needed for Brazil-EU transfers), LGPD gap assessments, privacy policy drafting for Brazilian operations, DPIA under LGPD, consent management, or comparing LGPD with GDPR. Trigger for any Brazil privacy or data protection question even if LGPD is not named explicitly.

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 India Digital Personal Data Protection Act, 2023 (DPDPA) compliance advisor. Use this skill whenever a user asks about the DPDPA, DPDP Act, DPDP Rules 2025, India data privacy law, Data Fiduciary obligations, Data Principal rights, Significant Data Fiduciary, Data Protection Board of India, consent under DPDPA, notice requirements, breach notification India, children's data India, cross-border data transfer India, India privacy compliance, DPDPA gap analysis, DPDPA vs GDPR, or any obligation under India's personal data protection framework. Also trigger for: "Section 6 consent", "Section 7 legitimate uses", "Section 9 children's data", "Section 10 SDF", "Section 16 cross-border", "Rule 6 breach notification", "Rule 13 SDF obligations", "Data Protection Board complaint", "verifiable parental consent India", "DPDPA compliance roadmap", or "India privacy law global company".

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。

Grade a ForgeCAD or CAD-as-code model against a requirement, brief, prompt, reference, or acceptance criteria with evidence and a 0-10 score.

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 builder-honest AI image prompts from a concrete ForgeCAD model, build brief, HLD, or LLD without hiding how the artifact is built.

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。

Data-driven Scrum Master for sprint health scoring, Monte Carlo velocity forecasting, retrospective analysis, capacity planning, and Tuckman team coaching. Use when facilitating sprint planning, diagnosing velocity, or running retrospectives.

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。

Product analytics for instrumenting products, defining metrics, and building retention funnels. Use when designing a metric tree, instrumenting a feature, auditing instrumentation, defining a North Star, or building an analytics roadmap.

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。

People analytics across workforce metrics, predictive modeling, and employee insights. Use when building turnover models, analyzing engagement surveys, running pay equity regressions, or scoring flight risk.

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 "optimize SQL queries", "explore database schemas", "generate migration SQL", "analyze query performance", or "document database structure".

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。

Analyzes campaign performance with multi-touch attribution, funnel conversion, and ROI calculation for marketing 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。

Data breach incident response with ENISA severity scoring, notification timelines, and compliance tracking. Use for breach assessment and response.

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 analytics implementation for web and SaaS: GA4, Google Tag Manager, event taxonomy, conversion tracking, and UTM strategy. Use when building a tracking plan, debugging missing events, setting up GTM, or auditing existing analytics.

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 "estimate LLM costs", "count tokens in prompts", "optimize prompt token usage", "compare model pricing", or "reduce LLM API costs".

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。

Data engineering for batch and streaming pipelines with Airflow, dbt, Spark, and Kafka. Use when designing data architectures, building pipelines, adding data-quality checks, optimizing ETL/ELT, or troubleshooting pipeline failures.

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。

Prompt engineering frameworks for building, testing, versioning, and evaluating prompts: chain-of-thought, few-shot, regression testing, and rubrics. Use when designing production prompts, running A/B tests, or building prompt libraries.

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 "optimize Snowflake queries", "analyze Snowflake SQL performance", "size Snowflake warehouses", "review Snowflake data models", or "troubleshoot Snowflake cost issues".

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 user asks to "design an experiment", "build a predictive model", "run A/B test analysis", "perform causal inference", "engineer features", "evaluate model performance", "set up MLOps pipeline", "analyze time series", "calculate sample size", or "deploy a model to production". Expert data science covering statistical modeling, experimentation, causal inference, feature engineering, ML deployment, and advanced analytics with Python, R, and SQL.

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 "audit prompts for safety", "check prompts for injection vulnerabilities", "manage a prompt catalog", "version control prompts", or "review prompt quality and compliance".

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。

ML engineering skill for productionizing models, building MLOps pipelines, and integrating LLMs. Covers model deployment, feature stores, drift monitoring, RAG systems, and cost 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。

Prompt engineering and LLM evaluation. Use when optimizing prompts, designing prompt templates, evaluating LLM outputs, building agentic systems, implementing RAG, creating few- shot examples, or designing structured-output workflows.

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。