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概要と使いどころ
Work on a Technical Project Plan. Use when starting complex features, cross-platform changes, native module modifications, or multi-session work that needs persistent context.
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Iterative deep planning with critiques and alternatives. Use when facing complex design decisions, cross-platform changes, native module modifications, or API changes requiring thorough 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。
Review someone else's PR through an architectural/strategic lens — does it shift design, raise complexity, or quietly change precedent? Drives a brief reflective discussion with the user, then posts a concise review comment via `gh`. Defers line-level and bug-hunting concerns to AI review bots and the PR author.
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。
Problem solving and analysis: Structured thinking processes for complex problems. Divide & Conquer, Root Cause Analysis, SWOT, Pareto, and decision heuristics.
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Author TikZ/LaTeX diagrams that compile: use the host's prepared preamble, the right libraries, layout and plot recipes, and read the compiler's line-accurate errors to converge in a couple of writes. A bundled reference covers complex, large pictures.
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。
Author Mermaid diagrams that parse the first time: choose the right diagram type for the question, write syntax the parser accepts, and keep the picture readable in a narrow panel. A bundled reference covers complex, large diagrams.
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。
Resolve complex or high-impact problems when the cause, solution path, requirements, or proof boundary is genuinely uncertain. Use when work needs evidence-led diagnosis, consequential design or implementation, or a completion audit; do not trigger merely because work has multiple steps, or for simple facts, fully specified routine changes, or pure creative generation.
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 detailed implementation plan before coding. Use when starting a new feature, multi-step refactor, or complex task. Invoked by saying "plan", "create a plan", or "plan this task".
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。
Submission-grade remote-sensing Nature/high-impact journal figure workflow for Python or R. Use whenever the user asks to create, revise, audit, or polish manuscript figures, multi-panel scientific plots, map-led composites, remote-sensing image plates, sensor/product workflow figures, validation/error/uncertainty panels, or journal-ready SVG/PDF/TIFF outputs, especially for Nature-family, Remote Sensing of Environment, ISPRS, IEEE TGRS, or other high-impact venues. Before plotting, define the figure's conclusion, evidence logic, export needs, and review risks. If the user has not chosen Python or R, ask "Python or R?" and stop. Use only the selected backend for figure generation, previewing, exporting, and QA. Supports matplotlib/seaborn and ggplot2/patchwork/ComplexHeatmap. Not for dashboards or Illustrator/Figma-first infographics. Also trigger on general academic-writing figure needs even without the word "Nature", such as making figures/plots for a paper, scientific/academic plotting, data visualization for a manuscript, and Chinese phrasings like 遥感论文配图、学术写作配图、科研绘图、科研作图、地图出图、论文图表、可视化.
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 security audit for WordPress plugin/theme PHP code, covering issues beyond the basic sanitize/escape/nonce checklist — PHP object injection (unserialize), SSRF in remote requests, CSRF on state-changing GET handlers, mass assignment via $_POST loops, insecure file include / template injection, mail header injection, ZipSlip in archive extraction, type-juggling in auth comparisons, and TOCTOU race patterns in option/meta locks. Use after or alongside wp-security-audit when reviewing complex plugins, REST APIs, integrations that fetch remote URLs, file processors, or any code that handles uploads, archives, or self-rolled auth tokens.
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。
Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn/ui). Use for complex artifacts requiring state management, routing, or shadcn/ui components - not for simple single-file HTML/JSX artifacts.
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。
Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn/ui). Use for complex artifacts requiring state manag
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。
Advanced JavaScript animation library skill for creating complex, high-performance web animations.
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。
Route complex marketing work across strategy, narrative, SEO/GEO, CRO, lifecycle, paid, social, influencer, launch, sales and measurement. Use for broad marketing plans, multi-channel work, or when the right specialist skill is unclear.
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 an Estuary derivation that joins two or more collections on a shared key, merging their fields into a single enriched collection. Use for data enrichment, denormalisation, building wide tables for BI, and real-time CDC joins. Derivations add complexity and cost — confirm the user wants a derivation before reaching for this (a destination-side SQL join or BI tool view is often simpler). Use when user says "join-collections derivation", "derivation to join two collections", "transformation to enrich with customer data", "derive a wide table", "lookup-join derivation", or "build a join derivation".
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 an Estuary derivation that flattens a nested array into one output document per array element. Use for normalising nested JSON, exploding line items, unpacking tags, or producing one-row-per-item tables from arrays. Derivations add complexity and cost — confirm the user wants a derivation before reaching for this (some destinations can `UNNEST` server-side without one). Use when user says "flatten-array derivation", "derivation to explode an array", "transformation to unnest", "derive one row per item", or "flatten nested data with a derivation".
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 an Estuary derivation with custom SQLite state (internal tables) to make per-document decisions based on accumulated history — account balances, inventory levels, approval workflows, deduplication. Derivations add complexity and cost — confirm the user wants a derivation before reaching for this. Use when user says "stateful derivation", "derivation to track balance", "transformation to approve based on state", "derive an approval workflow", "deduplication derivation", or "build a stateful derivation".
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 an Estuary derivation that continuously aggregates metrics (daily totals, running counts, min/max, lifetime revenue) using reduction annotations. Use for real-time dashboards, continuous materialised views, and replacing batch ETL aggregations with streaming. Derivations add complexity and cost — confirm the user wants a derivation before reaching for this. Use when user says "aggregation derivation", "derivation to sum daily totals", "running-count derivation", "transformation for min/max per group", "derive lifetime metrics", "aggregate-by-date derivation", or "real-time-dashboard derivation".
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 an Estuary derivation that maintains a sliding time window of events (e.g. "transfers in the last 24h", "failed logins in the last 5 minutes") using internal SQLite state and readDelay for time-based expiration. Derivations add complexity and cost — confirm the user wants a derivation before reaching for this. Use when user says "windowing derivation", "derivation for last-24h events", "rolling-window derivation", "sliding-window transformation", "fraud-detection derivation", "rate-limit derivation", or "time-window derivation".
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 Estuary derivations in Python — async-generator transforms, Pydantic-typed source/output, optional persistent state, and dependency installation via uv. Use for ML/embeddings, async API calls, or any transform where SQL and TypeScript are awkward. Derivations add complexity and cost (and Python derivations require a private/BYOC data plane) — confirm the user wants a Python derivation before reaching for this. Use when user says "python derivation", "write a derivation in python", "embed with a python derivation", "ML derivation", "call an API from a derivation", or anything mentioning model2vec / huggingface / pydantic in a derivation context.
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 an Estuary derivation for stateless filtering, field selection, and per-document field transformation. Use for WHERE clauses, projecting fields, computing new fields from existing ones, or data cleansing. Derivations add complexity and cost — confirm the user wants a derivation before reaching for this (renaming alone is usually a projection, not a derivation). Use when user says "filter-transform derivation", "derivation to filter by status", "transformation with WHERE clause", "derive computed fields", "cleanse data with a derivation", or "stateless derivation".
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 proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design. Use when architecting complex backend systems or refactoring existing applications for better maintainability.
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。
Master advanced Git workflows including rebasing, cherry-picking, bisect, worktrees, and reflog to maintain clean history and recover from any situation. Use when managing complex Git histories, collaborating on feature branches, or troubleshooting repository 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。
Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns. Use when building LangChain applications, implementing AI agents, or creating complex LLM 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。