データ・AIのスキル

Create, revise, or evaluate repository Agent Skills when the user asks to write a SKILL.md, improve triggers or information hierarchy, restructure references/scripts/assets, or diagnose skill predictability; do not use for product code, AGENTS.md initialization, general prompt editing, or automatic repository-wide 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。

AI regression scouting routing. Use when agents, prompts, skills, model/tool routing, harness fixtures, or generated-output expectations change and need regression scenarios; do not use for ordinary product-code regressions.

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 user-reported AILI/ROSE harness or workflow behavior problems without editing files; use when the user says a command, skill trigger, subagent packet, memory flow, installer, docs, or agent prompt behavior is wrong and wants to know where the issue lives and how to fix it. Do not use for normal product-code bugs; return any approved modification need to ROSE.

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。

Structure source-grounded consulting-style analysis for strategy, market, product, operations, or decision memos. Use for MECE problem framing, hypotheses, options, tradeoffs, executive recommendations, or "consulting analysis" requests; do not use for legal/financial advice, implementation, data computation, chart rendering, or unsourced business claims.

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, edit, fix, or explain Mermaid diagrams for relationships, decision branches, interactions, lifecycles, and data models; also use when the parent selects a diagram to materially clarify an explanation. Do not trigger for simple status, one fact, short lists, quantitative charts, or marketing slides.

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 source data, tables, CSV/XLSX extracts, metrics, or experiment results with explicit data-authenticity checks. Use for profiling, cleaning assumptions, statistical summaries, segmentation, trend analysis, or "analyze this data"; do not use for spreadsheet file surgery, chart design only, database migrations, or claims without data provenance.

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。

Idiomatic Go concurrency - sync primitives, channel semantics, the select statement, and the standard channel patterns (cancellation/done-channel, fan-out/fan-in, pipeline, or-done, context, worker-pool). Use when writing or reviewing Go that spawns goroutines, shares state, or coordinates over channels, even if the user never says "concurrency" - e.g. data races, deadlocks, goroutine leaks, sync/context/select usage, or "make this parallel". Distilled from luk4z7/go-concurrency-guide.

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。

Creates data table patterns with filtering, sorting, pagination, row actions, column configuration, server/client rendering modes, and empty/loading states. Use when building "data tables", "list views", "admin tables", or "data grids".

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。

Implements TanStack Query (React Query) for server state management with caching, mutations, optimistic updates, and infinite queries. Use when users request "react query", "tanstack query", "data fetching", "cache management", or "server 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。

Deep competitive intelligence for any market. Analyzes competitors' products, pricing, customer sentiment, GTM strategy, and growth signals using real web data. Produces battle cards, pricing landscape, and feature matrix. Use when the user wants to understand their competitive landscape, analyze competitors, compare products in a market, or research who they're competing against. Triggers for "who are my competitors", "competitive analysis", "competitor research", "battle cards", "pricing comparison", "competitor pricing", "market players", "competitive intelligence", "competitive landscape", "who else is in this space", "competitive moat", or any request to profile, compare, or map competitors in a category. Works standalone — no prior startup-design session needed.

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。

Sets up vector databases for semantic search including Pinecone, Chroma, pgvector, and Qdrant with embedding generation and similarity search. Use when users request "vector database", "semantic search", "embeddings storage", "Pinecone setup", or "similarity search".

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。

Extracts structured data from LLM responses using JSON schemas, Zod validation, and function calling for reliable parsing. Use when users request "structured output", "JSON extraction", "parse LLM response", "function calling", or "typed responses".

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 and optimizes SQL queries using EXPLAIN plans, index recommendations, query rewrites, and performance benchmarking. Use for "query optimization", "slow queries", "database performance", or "EXPLAIN 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。

Prevents logging sensitive user data with redaction rules, logging policies, and safe log formatting. Use for "PII redaction", "log privacy", "GDPR logging", or "data privacy".

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。

Diagnoses LLM output failures including hallucinations, constraint violations, format errors, and reasoning issues. Provides root cause classification, prompt fixes, tool improvements, and new test cases. Use for "debugging AI", "fixing prompts", "quality issues", or "output errors".

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。

Plans and executes safe Prisma schema migrations with data backfills, rollback strategies, and SQL preview. Handles complex schema changes including data transformations. Use for "Prisma migrations", "schema changes", "database migrations", or "data backfills".

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。

Compares old vs new prompts across test cases with diff summaries, stability metrics, breakage analysis, and fix suggestions. Use for "prompt testing", "A/B testing prompts", "prompt versioning", or "quality regression".

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。

Creates reusable prompt templates with strict output contracts, style rules, few-shot examples, and do/don't guidelines. Provides system/user prompt files, variable placeholders, output formatting instructions, and quality criteria. Use when building "prompt templates", "LLM prompts", "AI system prompts", or "prompt engineering".

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。

Designs retrieval-augmented generation pipelines for document-based AI assistants. Includes chunking strategies, metadata schemas, retrieval algorithms, reranking, and evaluation plans. Use when building "RAG systems", "document search", "semantic search", or "knowledge bases".

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。

Builds phased data and system migrations using feature flags, dual writes, backfills, and validation. Includes rollback plans and risk mitigation. Use for "data migration", "system migration", "database migration", or "platform migration".

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。

Converts documents into clean, chunked datasets suitable for embeddings and vector search. Produces chunked JSONL files with metadata, deduplication logic, and quality checks. Use when preparing "training data", "vector datasets", "document processing", or "embedding data".

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。

Implements content safety filters with PII redaction, policy constraints, prompt injection detection, and safe refusal templates. Use when adding "content moderation", "safety filters", "PII protection", or "guardrails".

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。

Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.

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。

Builds document embedding pipelines with text chunking, embedding generation, indexing, and retrieval optimization. Use when users request "embedding pipeline", "document indexing", "text chunking", "RAG preprocessing", or "semantic indexing".

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。