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Builds production AI bots with Python for support, sales, and custom domains. Use when designing conversation flows, personas, escalation, or LangGraph 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。

Adds per-skill learnings loops for dated patterns, mistakes, and domain facts. Use when wiring skill memory, consolidation, or drift audits.

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 research on a topic — use when investigating unfamiliar domains, answering complex questions requiring multiple sources, or when an evaluation flags something for deeper 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。

Reviews a GitHub pull request or focused branch diff for correctness, regressions, security, compatibility, test gaps, and maintainability. Use when an implementation needs an independent source-aware review before merge. Produces prioritized findings with evidence and actionable fixes. Not for architecture-only audits, formatting preferences, or processing existing review feedback.

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。

Simplifies recently changed or explicitly scoped code while preserving observable behavior, error semantics, side effects, ordering, and public contracts. Use for readability cleanup, dead-code removal, reducing nesting, eliminating redundant wrappers, or making an implementation easier to maintain. Not for architecture redesign, new behavior, speculative cleanup, or a refactor without an adequate safety net.

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。

Validates a running application, CLI, API, service, or generated artifact as a user or operator against a prewritten observable behavior contract while remaining source-blind. Use for acceptance checks, runtime proof, anti-fake probes, release smoke tests, or an independent companion to code review. Not for source-quality findings, root-cause diagnosis, or visual design judgment outside the contract.

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。

Maintain HitKeep dashboard localization. Use whenever a contributor adds or changes user-visible dashboard text, Transloco keys, locale JSON files, OptimusUI locale behavior, language switching, translated labels, localized layout, date/number/duration formatting, or supported dashboard languages.

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。

Refresh HitKeep's checked-in bot/AI-agent, IP metadata, and spam-filter datasets through their canonical generators. Use for routine generated-data maintenance; do not use for hand-editing generated assets or changing detector policy.

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。

Track, categorize, and prioritize technical debt across the codebase. Scans for debt indicators, maintains a debt register, and recommends repayment scheduling.

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。

Detect non-deterministic (flaky) tests by reading CI run logs or test result history. Aggregates pass rates per test, identifies intermittent failures, recommends quarantine or fix, and maintains a flaky test registry. Best run during Polish phase or after multiple CI runs.

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。

Map test coverage to GDD critical paths, identify fixed bugs without regression tests, flag coverage drift from new features, and maintain tests/regression-suite.md. Run after implementing a bug fix or before a release gate.

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。

Maintain PlainTab runtime UI translations and i18n language packs. Use when adding, editing, formatting, validating, or reviewing UI text in js/i18n/*.js, js/languages.js, data-i18n markup, or t(key) calls; also use when onboarding/help copy or new settings need localized strings.

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。

Claude adapter for maintaining PlainTab runtime UI translations. Use when adding, editing, formatting, validating, or reviewing UI text in js/i18n/*.js, js/languages.js, data-i18n markup, or t(key) calls.

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。

Maintain the harness memory files (USER.md, MEMORY.md, AGENTS.md) so that long-term knowledge persists across conversations.

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。

Filter Shopify standalone stores by multiple dimensions (store name/domain, country, years since creation, product count, ad count, monthly visits, monthly orders, social media followers, etc.). Triggered when users mention Shopify stores, Shopify store analysis, standalone stores, Shopify sellers, competitor standalone stores, Shopify monthly visits, standalone store ad library, shopify stores, or Shopify store query. Even if the user does not mention the tool name, this skill should be triggered whenever searching for stores, filtering store data, or analyzing store performance on Shopify standalone stores.

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 Amazon product details by ASIN, including price, title, main image, listing date, material, weight, variant monthly sales, and up to 12 months of monthly sales history. Triggered when users query Amazon product details, ASIN lookup, product pricing, sales rank history, monthly sales trends, product dimensions, FBA fees, product specifications, batch ASIN query, Nexscope product details, ASIN detail lookup, monthly sales data, pricing info, product specifications, FBA fees, batch ASIN query. Even if users do not explicitly mention "Nexscope", this skill should be triggered whenever the task involves obtaining structured product data for one or more Amazon ASINs.

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 for every task that plans, writes, edits, debugs, tests, refactors, reviews, or maintains code. Apply to professional repositories, scripts, apps, services, prototypes, beginner work, vibe coding, and bounded code changes inside large systems. Choose the fewest clear lines, files, paths, states, and abstractions that correctly implement the requested behavior. Do not invent fallbacks, compatibility paths, defensive layers, or future scale without evidence. Do not use for ordinary conversation, emotional support, prose, translation, summarization, social-media content, or general research or technical discussion that is not part of planning, changing, or reviewing code. Never weaken real security, integrity, concurrency, compatibility, privacy, accessibility, legal, or regulatory 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。

Keep coding work complete, readable, and proportionate to the task. Use when planning, implementing, debugging, testing, reviewing, or maintaining code, including bounded changes in larger repositories. Avoid unnecessary design and verification overhead. Does not apply to non-code conversation or writing.

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。

Refracting thinking by challenging assumptions, combining cross-domain knowledge, and shifting perspectives to reframe problems. Use for stuck situations or paradigm shifts. Does not write code.

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。

Orchestrating multi-specialist task chains and scope-adaptive product delivery: classifies intent, selects and executes the minimum viable chain, aggregates results, and verifies acceptance criteria. For multi-domain tasks, build-first delivery, and product lifecycle execution.

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。

Run all remaining phases autonomously — discuss→plan→execute per phase

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。

Convert a vague mini-program idea, feature request, or partially documented product into an evidence-calibrated product specification with target users, core problem, version-one scope, main and exception flows, page responsibilities, state matrices, and testable acceptance criteria. Use when users ask to define an MVP, clarify requirements, organize product flows, specify page behavior, resolve ambiguous product states, or prepare a stable handoff before architecture or implementation. Separates facts, user decisions, assumptions, unknowns, and future ideas; never invents product logic merely to complete a screen or technical plan.

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 a sequence where students compare AI's handling of the same question across disciplines, developing a mental model of where AI is reliable vs. distorting based on knowledge type.

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 discipline-specific critical thinking tasks grounded in knowledge-contingent reasoning rather than generic skills. Use when embedding higher-order thinking into subject 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。