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概要と使いどころ
Typosquat and malicious-package detection across installed dependencies (or a single prospective addition) — cross-checks AI-malware family intelligence, package-name similarity to known popular packages, low maintainer-health signals. Use when auditing a freshly-onboarded repo, vetting a single suspicious package, gating CI on supply-chain risk, or investigating an incident.
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。
Hardcoded-secret detection — AWS keys, GitHub PATs, Slack tokens, Stripe keys, generic high-entropy strings. Pre-commit (`--staged-only`), explicit paths, or full repo. Use when guarding `git commit`, auditing a repo for leaked credentials, validating no secrets entered the diff before push, or producing a rotation list for an exposed-secret incident.
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。
Jira ticket operations via Atlassian MCP including view, search (natural language to JQL), create, update, comment, and transition with auto-detection of ticket IDs from git branches. Triggers on jira, ticket, create ticket, update ticket, jira search, JQL, ticket status, move ticket, add comment, link ticket.
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。
Drafts a blameless incident postmortem (summary, impact, timeline, detection and response, contributing factors as candidates for review, action items) from an incident channel export, ticket notes and any alert or change records, with UNKNOWN for every fact the inputs do not contain. Never assigns blame, names a root cause or re-rates severity. Use when the user asks to "write the postmortem for this incident", "build the incident timeline from the chat export", "draft the post-incident review" or "turn this ticket and channel log into an incident report". Do not use for a knowledge article from the resolved ticket, use knowledge-article-drafter instead; for project retrospectives, use lessons-learned-synthesis; for data-breach impact and notification questions, use data-incident-impact-brief. Drafts for human review; never approves, authorises or signs off.
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。
- Investigate a phone number — E.164 normalisation with libphonenumber, phoneinfoga scanning, carrier and line-type identification, VoIP and burner detection, messaging-app registration checks, and reverse-lookup and caller-ID sources.
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。
- Verify whether an image or video is authentic, original and correctly captioned — provenance checks, error level analysis, noise and JPEG compression analysis, clone and copy-move detection, lighting and shadow consistency, C2PA… Use when fact-checking a photo or video, checking for a deepfake or AI-generated image, spotting manipulation, or testing whether footage is recycled or miscaptioned
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 when a website needs rendered screenshot review, Playwright visual regression, multi-viewport checks, horizontal overflow detection, console-error checks, focus/state inspection, or a repeatable visual QA evidence set.
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 when adding CopilotKit to an existing project or bootstrapping a new CopilotKit project from scratch. Covers framework detection, package installation, runtime wiring, provider setup, and first working chat integration.
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。
Synthesize WooCommerce coupons at runtime without creating shop_coupon posts in the DB — via the woocommerce_get_shop_coupon_data filter, the hidden-but-powerful virtual-coupon mechanism. Filter fires inside WC_Coupon::__construct, and a non-false return becomes a fully functional coupon read through read_manual_coupon. Plus custom discount types via woocommerce_coupon_discount_types and woocommerce_coupon_get_discount_amount, and the validation triple woocommerce_coupon_is_valid / _is_valid_for_cart / _is_valid_for_product. Use when implementing rule-driven coupons (auto-apply X% to logged-in users, code-pattern detection like LOYALTY-42, external CRM integration that hands out codes), without populating wp_posts. The AI-deficient pattern; most LLMs don't know dynamic coupons exist. Triggers on woocommerce_get_shop_coupon_data, woocommerce_coupon_discount_types, woocommerce_coupon_get_discount_amount, read_manual_coupon, virtual coupon, dynamic coupon in WC 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。
Register a custom WooCommerce shipping provider — extend AbstractShippingProvider and add it via the woocommerce_fulfillment_shipping_providers filter (WC 10.1+). Distinct from shipping methods (rate at checkout) — a provider is a carrier identity (UPS, FedEx, DHL, your custom carrier) used by the Fulfillments system for tracking URLs, tracking-number pattern detection, country-pair support, and the orders-list filter. Required methods get_key / get_name / get_icon / get_tracking_url, with optional shipping-from / shipping-to / try_parse_tracking_number. Registration accepts class names (resolved via the DI container) or pre-built instances, keyed by get_key. Use when integrating a private carrier, fulfillment service, or any tracking-aware shipping identity. Triggers on AbstractShippingProvider, woocommerce_fulfillment_shipping_providers, FulfillmentUtils::get_shipping_providers, "shipping provider" in WooCommerce context, or carrier tracking integration.
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。
Programmatically clear WP Rocket cache from a third-party plugin / theme when data changes — the public rocket_clean_* function family (rocket_clean_post, rocket_clean_files, rocket_clean_term, rocket_clean_user, rocket_clean_home, rocket_clean_minify, rocket_clean_cache_busting, rocket_clean_domain, rocket_clean_cache_dir). Critical detection rule — WP Rocket is a PAID plugin not on Packagist; always feature-detect via function_exists('rocket_clean_post') OR defined('WP_ROCKET_VERSION') before calling, since not every site has it. Never raw-unlink the cache directory or call wp_cache_flush() expecting it to clear WP Rocket — wp_cache_flush is WP object cache, WP Rocket is FILE cache. The before_*_clean_* / after_*_clean_* action lifecycle hooks fire around every clean — useful for audit logging, monitoring, custom invalidation chains. Use when integrating cache invalidation in a companion plugin, WC integration, custom data plugin. Triggers on rocket_clean_, before_rocket_clean, af...
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。
Produces marketing performance reports and data-driven recommendations from WooCommerce sales data, social media metrics, and campaign analytics. Use when the task asks for "campaign performance", "ROI analysis", "monthly report", "sales trends", "product velocity", "audience insights", or "KPI tracking". Covers metric definition, reporting templates, slow-mover detection, and insight-to-action frameworks.
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。
Manage the NV oOS Pro Toolkit Security Operations system — audit events, security training, maintenance windows, and incident records. Covers the full security ops lifecycle: audit logging, incident detection/response, maintenance scheduling, and compliance training tracking. Use when working with security audit trails, investigating incidents, scheduling maintenance windows, managing security training programs, or troubleshooting security operations 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。
Complete operational guide for the NV oOS (Open Operator System) WordPress plugin in Docker/WSL2 — setup, assistant creation, credential tokens, MCP tool calling, API key auto-detection, env var bridging, common fixes, and IGCSE study configuration. Use when setting up the plugin for the first time, creating assistants programmatically, generating MCP bridge tokens, troubleshooting Docker path issues, bridging API keys, or calling tools via JSON-RPC over HTTP.
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。
Systematically rewrites AI-generated or AI-sounding text on web pages, documents, or pasted content to sound human. Use this skill whenever a user asks to "humanize", "de-AI", "make this sound less like AI", "rewrite to avoid AI detection", or "clean up AI writing". Also trigger when a user pastes text and asks to make it sound more natural, more human, or less robotic. The skill applies a strict deterministic rule pipeline — NOT creative rewriting — making it safe for agentic/automated use.
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 project-specific AGENTS.md and companion rules by analyzing a codebase. Supports full, minimal, update, and dry-run modes with package-manager detection, monorepos, backups, managed blocks, c
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 when the user asks to "triage our comments, DMs, and mentions", "draft replies to this thread", "can we repost this fan post", or "set up inbox SLAs and an escalation path"; produces a ranked triage queue with register detection (sincere / ironic / performative / parasocial, sentiment-inversion table included — "this is so bad" under a comedy register is praise), a commenter taxonomy (troll monitor-only / rager / misguided / unhappy-customer / advocate) with a response-tier ladder and per-channel SLAs, an escalation matrix ending at the crisis path, a moderation ladder plus house rules for owned spaces, and a UGC curation-and-rights mode whose dated permission entries route to the channel registry — every reply is a ranked DRAFT a human posts; nothing is ever auto-sent. Not for launch-window feedback triage — use launch-feedback-synthesizer. 评论私信提及分诊/语域识别/回复草稿/UGC授权
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。
GitHub operations using gh CLI for any repository. Use when the user asks to "create pr", "create pull request", "view pr", "check ci", "pr status", "list prs", "create issue", "view issue", "search code", or any other GitHub-related operations. Handles PR creation with proper base branch detection, template support, and project conventions.
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。
Diagnoses and prevents mobile performance regressions on every stack — Flutter, native Android, native iOS, React Native and Kotlin Multiplatform. Covers startup time, rendering and jank, memory, battery and background work, app size, the profiler and benchmark to use on each platform, and regression detection in CI. Use when an app is slow, janky, heavy or draining the battery, when setting performance budgets, or when a release must not regress.
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。
CloudFormation template optimization, nested stacks, drift detection, and production-ready patterns. Use when writing or reviewing CF templates.
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。
Azure AI Text Translation SDK for real-time text translation, transliteration, language detection, and dictionary lookup. Use for translating text content in applications.
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。
Azure AI Text Analytics SDK for sentiment analysis, entity recognition, key phrases, language detection, PII, and healthcare NLP. Use for natural language processing on text.
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。
Build image analysis applications with Azure AI Vision SDK for Java. Use when implementing image captioning, OCR text extraction, object detection, tagging, or smart cropping.
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。