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When the user wants to edit, review, or improve existing marketing copy. Also use when the user mentions 'edit this copy,' 'review my copy,' 'copy feedback,' 'proofread,' 'polish this,' 'make this better,' 'copy sweep,' 'tighten this up,' 'this reads awkwardly,' 'clean up this text,' 'too wordy,' or 'sharpen the messaging.' Use this when the user already has copy and wants it improved rather than rewritten from scratch. For writing new copy, see copywriting.

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Use when building or debugging Typer/Rich CLI applications. Activates on Rich table rendering, console output in non-TTY environments, CliRunner testing with Rich output, snapshot testing, Typer command wiring, exception chain prevention with AppExit/AppExitRich patterns, table width at 80-column wrapping, Progress/Live in non-interactive contexts, stderr/stdout separation, or force_terminal vs width configuration. Grounds AI-generated CLI code in verified correctness patterns and prevents known Typer/Rich integration mistakes.

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Use when debugging software, investigating incidents, diagnosing flaky tests, or analyzing performance regressions — enforces structured observation recording with evidence IDs, causality validation, and verification gates to prevent correlation-causation pollution. Use when an agent might otherwise summarize or speculate instead of reporting observed evidence.

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Use when facing unknowns, debugging without a clear cause, or making architecture decisions — enforces hypothesis-driven scientific reasoning through observation, hypothesis formulation, prediction, experiment design, and evidence-based conclusion. Use when previous attempts have failed or the problem space involves uncertainty.

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Use when writing Python scripts that must run on Windows, Linux, and macOS — especially when Rich or Typer output breaks on Windows, when dealing with Unicode/encoding errors, ANSI escape handling, terminal detection, path separators, or console color support. Provides verified cross-platform patterns covering stdout/stderr encoding guards, Windows console quirks, terminal capability detection, and portable I/O for CLI, TUI (Rich/Textual), and GUI environments.

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Use when independently assessing how bounded Python code could accomplish the same behavior with less maintained code, more Pythonic design, newer language/stdlib capabilities, or well-maintained ecosystem libraries.

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Use when independently hunting Python code smells, deviations from engineering standards, maintenance hazards, or suspicious design choices in a bounded review surface.

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Autonomous feature research and gap analysis. Use when starting /add-new-feature or analyzing existing architecture documents. Explores codebase patterns, identifies ambiguities, and produces feature-context-{slug}.md for orchestrator RT-ICA phase. Does NOT make technical decisions.

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Format and score completed plugin assessment findings without starting another assessment workflow. Use when a plugin assessment needs the standard report structure, scoring criteria, severity breakdown, or marketplace-readiness result.

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Orchestrate the full plugin development lifecycle from blank canvas to marketplace-ready. Use when creating a new plugin, improving an existing plugin, fixing validation errors, or taking a plugin through assessment, research, design, creation, debugging, optimization, and verification. Complements /plugin-creator:plugin-creator which provides the detailed new-plugin creation workflow with discussion capture, parallel research, and atomic implementation.

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Synchronize existing documentation with an observed code/configuration/API change while preserving each document's purpose and authority. Use after implementation changes can make documentation stale, incomplete, or internally inconsistent.

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Executes a groomed milestone with parallel kage-bunshin sessions in isolated worktrees. Use when a milestone has been groomed and /groom-milestone has produced a dispatch plan. Reads the dispatch plan, creates an integration branch, spawns one kage-bunshin (independent claude -p process) per wave item in its own worktree — each session is a full orchestrator with the Agent tool. Sequentially merges worktree branches, relays wave discoveries to subsequent waves, then lands the integration branch to main. Takes a milestone number as argument.

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Use when building, extending, or debugging FastMCP v4 (or v3) Python MCP servers. Activates on FastMCP tool/resource/prompt creation, provider and transform implementation (CodeMode, Tool Search), auth setup (MultiAuth, PropelAuth, KeycloakProvider), client SDK usage, FastMCPApp and Generative UI server building, fastmcp-slim client-only installs, nginx reverse proxy deployment, Prefab Apps, OTEL observability, testing, and v3→v4 migration. Grounded in official FastMCP v4 docs plus locally-verified v3 gotchas — zero speculation.

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Quantitative cost measurement for technical research — token injection costs, payload sizes, context window consumption, and file-level counts from actual repo files. Use when a technical-researcher orchestrator needs the cost dimension of adding or changing something: how many tokens will it inject, how big are the artifacts, what is the context window impact? This is NOT blast-radius analysis (which files break) — it covers size, tokens, and performance cost only.

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Fetch and report current API syntax, changelog entries, and breaking changes for a specific library or protocol version. One research angle within a parallel technical-research set — runs independently and returns a structured cited report. Invoke when a specific library name and version are the target.

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Reviews Node.js runtime behavior in JavaScript or TypeScript, including server entrypoints, route handlers, middleware, streams, processes, dependencies, and environment configuration. Loads alongside language and CLI checks when applicable; investigates blocking I/O, backpressure, injection, lifecycle leaks, and misleading failure signals.

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Create one-off outbound phone calls, web calls, scheduled calls, and simple batch calls using the Vapi API. Use when making or testing individual calls or initiating a bounded /call request programmatically. Use create-campaign for a persistent multi-contact Campaign with lifecycle, reporting, cancellation, duplication, or campaign webhooks.

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This skill should be used when the user runs /dd or /ㅇㅇ to act on the current OS clipboard (text or image) without pasting it into chat. It captures the live clipboard into a local cache and uses only the manifest plus a short preview as context, so error logs and reference images do not bloat the conversation. Korean triggers: "/dd", "/ㅇㅇ", "클립보드 보내줘", "이거 분석해줘", "방금 캡처한 거", "이 레퍼런스로", "스크린샷 드롭". English triggers: "/dd", "drop clipboard", "use what I copied", "this screenshot", "this reference".

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Reads or converts documents in a research workflow, including PDF, Word, PowerPoint, Excel, OpenDocument, RTF, EPUB, and CSV. Chooses direct reading or conversion, selects a converter from document structure, and decides whether the Markdown is tracked or temporary. Use when a document must be read, quoted, summarized, searched, extracted, or added to a source library. Scanned documents go to vlm-ocr and bulk intake to research-repo.

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Designs publication figures for social-science manuscripts. Covers chart choice, axes and scales, color and accessibility, legend order, captions that carry the title, and script-based reproducibility. Use when making, revising, or critiquing a figure for a paper, such as a coefficient plot, small multiples, a distribution, a trend, a map, or a network. Finished figure sets go to figure-table-audit for end-stage QA.

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Typesets a working paper or journal submission in house-style LaTeX from Markdown, Word (.docx), TeX, ODT, RTF, or HTML. Converts with pandoc, wraps the body in an EB Garamond template, builds the PDF with latexmk, and sets journal-specific spacing, page limits, anonymization, disclosures, and citation style. Use to format or convert a paper to LaTeX, make a working paper, or prepare a submission to a named journal.

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Scaffolds or audits a social-science replication package and evaluates the manuscript and archived research objects against FAIR principles. Scaffold mode writes the folder structure, README, master.R, figure-table crosswalk, codebook, LICENSE placeholder, and .gitignore; audit mode grades the package against a pre-release checklist and audits data, code, materials, identifiers, licenses, access restrictions, and availability statements. Use when setting up or repairing a package, checking one before submission, auditing FAIR compliance, or drafting availability statements. Adapted from Yusaku Horiuchi’s replication-package-guide.

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Fact-check manuscript claims against their cited sources in a per-source Markdown knowledge base. Use to audit claim support, overclaiming, direction, scope, and misattribution after source intake is complete.

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Operate or audit a live Qualtrics survey via the v3 APIs without breaking fielding: publish gating, quotas, flow routing, embedded data, panel-vendor redirects, read-back verification, and a read-only pre-fielding audit. Use when publishing or patching a fielding instrument, when a quota counts but never blocks, or before launch.

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。