「ai-agent-skill」の検索結果

Analyze datasets to answer defined questions through statistical methods, trend identification, hypothesis testing, and correlation analysis. Use when the user needs evidence-backed findings or decisions from data; use exploratory-data-analysis instead for open-ended first-pass profiling before questions are defined.

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。

Classify, prioritize, and route incoming support tickets by extracting intent and entities, assigning severity, and generating initial responses. Use when the user requests ticket triage or provides relevant inputs for this workflow.

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 clear, searchable help center articles and FAQ entries based on support data, product documentation, and common customer questions. Use when the user requests knowledge base article writing or provides relevant inputs for this workflow.

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 NPS, CSAT, and qualitative customer feedback to extract themes, identify trends, and generate actionable insight reports. Use when the user requests customer feedback analysis or provides relevant inputs for this workflow.

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 polished, audience-tailored presentations from outlines or documents, with support for Marp, reveal.js, Google Slides, and PPTX output formats. Use when the user requests presentation creation or provides relevant inputs for this workflow.

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。

Transcribe meeting audio with speaker diarization, generate structured summaries with action items, decisions, and follow-ups, and support multiple audio formats and languages. Use when the user requests meeting transcription or provides relevant inputs for this workflow.

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。

Draft a professional, audience-aware email or reply with calibrated tone and a clear call to action. Use when the user needs one message, response, follow-up, support note, or meeting request; use sales-email-sequences for a coordinated multi-touch outbound 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。

Create, schedule, and verify database backups with support for full, incremental, and point-in-time recovery strategies. Use when the user requests database backup or provides relevant inputs for this workflow.

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 SQL queries from natural-language requirements using SELECT, JOIN, GROUP BY, window functions, CTEs, and subqueries. Use when the user needs a new query from a business question or schema; use query-optimization when an existing query or execution plan is slow.

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。

Perform systematic exploratory data analysis to understand dataset structure, distributions, relationships, and anomalies before modeling. Use when a dataset is new, its quality is unknown, or the user requests open-ended profiling; use data-analysis instead for a defined hypothesis or decision question.

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, execute, and roll back versioned database schema migrations using tools like Alembic, Prisma Migrate, Flyway, and Knex. Use when the user requests database migration or provides relevant inputs for this workflow.

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 clear, effective charts and dashboards from structured data using matplotlib, seaborn, and plotly. Use when the user requests data visualization or provides relevant inputs for this workflow.

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。

Place trusted contextual information into prompts or agent state using explicit boundaries, provenance, and templates. Use when relevant context has already been selected and must be inserted safely; use context-retrieval to find it or context-optimization to choose and order it.

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。

Populate databases with realistic, reproducible test data for development, testing, and staging environments. Use when the user requests database seeding or provides relevant inputs for this workflow.

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 optimize existing slow SQL queries using execution plans, indexing strategies, query rewriting, and ORM tuning. Use when the user provides a query, performance symptom, or EXPLAIN plan; use sql-query-generation when creating a new query from requirements.

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。

Design normalized database schemas with tables, relationships, indexes, and constraints for any application domain. Use when the user requests database schema design or provides relevant inputs for this workflow.

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。

Compress selected context to a target token budget while preserving decisions, evidence, constraints, and unresolved questions. Use when relevant material is already selected but too long; use context-optimization when selection, deduplication, and ordering are also 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。

Identify at-risk customer accounts by analyzing usage patterns, engagement signals, and support history to generate churn risk scores and intervention recommendations. Use when the user requests churn analysis or provides relevant inputs for this workflow.

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。

Retrieve relevant information from a knowledge base using semantic, keyword, or hybrid search to ground a query. Use when the task starts with a corpus or index that must be searched; use context-ranking when candidate chunks already exist and only need ordering.

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。

Rank an existing set of context chunks by relevance, diversity, freshness, and utility. Use when retrieval has already produced candidates that must be scored or reranked; use context-retrieval when the source corpus still needs to be searched.

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。

Clean and preprocess datasets by handling missing values, removing duplicates, correcting types, resolving outliers, and enforcing validation schemas. Use when the user requests data cleaning or provides relevant inputs for this workflow.

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。

Optimize a complete candidate context package by deduplicating, filtering, ordering, and allocating its token budget. Use when retrieved or assembled material is noisy or exceeds the useful context budget; use context-ranking for scoring chunks and context-compression for shrinking selected content.

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 professional reports — sprint retrospectives, financial summaries, analytics dashboards, and incident postmortems — from structured data with templates, charts, and multi-format output. Use when the user requests report generation or provides relevant inputs for this workflow.

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。

Design structured, engaging chatbot conversations with robust intent handling, slot filling, disambiguation, error recovery, and graceful fallback strategies. Use when the user requests chatbot conversation design or provides relevant inputs for this workflow.

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。