テスト設計、脆弱性診断、品質保証（1.3万 件）
Audit an existing Coval evaluation setup for coverage, misleading scores, missing evidence, judge validation and execution risk. Read-only; use when inheriting an eval or deciding whether its results support a release decision.
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。
Select and configure a Coval metric for a concrete product criterion, choosing transcript, audio, trace or deterministic evidence and testing the result. Use for metric creation or scoring design, not for final human calibration.
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。
Validate one Coval judge against independent human labels, with grouped development/test separation, class-specific errors and uncertainty. Use for trust claims or calibration; without real labels, prepare review rather than inventing ground truth.
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。
End-to-end Coval accent testing workflow. Creates one persona per accent (each using a distinct accent voice and mirroring your Standard Customer behavior), launches one run per accent against the same voice agent + test set + metrics, polls for completion, builds a per-persona comparison table from the results, and creates the saved multi-run report (grouped by Persona) via the public API. Use when a user wants to follow the Testing Across Accents cookbook (https://docs.coval.dev/guides/testing-across-accents) without doing each step by hand.
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。
Configure an AI agent to send OpenTelemetry traces to Coval. Use when a user wants to add Coval tracing, instrument an agent for simulations or conversation monitoring, make traces show up in Coval, handle SIP/PSTN/WebSocket trace correlation, or replace the one-command wizard with a security-reviewable manual setup.
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。
Turn a large dataset (an existing oversized Coval test set, an export of past conversations, or a CSV/JSON of cases) into a small, high-signal Coval test set by removing duplicates, identifying unique scenarios, and selecting a representative, failure-weighted subset — then bulk-loading it with no row cap. Use when the user says "I have thousands of cases", "dedupe my test set", "my test set is too big", "turn this dataset into a test set", "pick representative scenarios", or "my CSV import only kept 10 / uploaded everything".
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。
Import datasets from HuggingFace and convert them to Coval test sets. Use when the user wants to create test cases from HuggingFace dataset or repository.
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 the pytest test suite with coverage. Accepts optional filter flags. Use after making changes to verify nothing broke.
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 wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a frontend interface. Covers websites, landing pages, dashboards, product UI, app shells, components, forms, settings, onboarding, and empty states. Handles UX review, visual hierarchy, information architecture, cognitive load, accessibility, performance, responsive behavior, theming, anti-patterns, typography, fonts, spacing, layout, alignment, color, motion, micro-interactions, UX copy, error states, edge cases, i18n, and reusable design systems or tokens. Also use for bland designs that need to become bolder or more delightful, loud designs that should become quieter, live browser iteration on UI elements, or ambitious visual effects that should feel technically extraordinary. Not for backend-only or non-UI tasks.
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。
Full website SEO audit with parallel subagent delegation. Crawls up to 500 pages, detects business type, delegates to up to 15 specialists (8 always + 7 conditional), generates health score. Use when user says audit, full SEO check, analyze my site, or website health check.
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 new cpp_tests entry for validating a C++ interface vtable layout against binary reference YAMLs. Creates the .cpp test file in cpp_tests/ and appends a configs/<GAMEVER>.yaml entry under cpp_tests:. Use when a user asks to add vtable layout validation for a new hl2sdk_cs2 interface class.
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 polished, validated architecture, workflow, sequence, data-flow, and lifecycle/state diagrams as explorable standalone HTML with inline SVG, dark/light themes, optional trace motion, and PNG/JPEG/WebP/SVG/WebM export. Accept plain-language requirements or pasted Mermaid flowchart, sequenceDiagram, and stateDiagram input; inspect repository evidence when the diagram must reflect real code. Use when the user asks to visualize system architecture, infrastructure, cloud/security/network topology, technical workflows, API call sequences, request lifecycles, data pipelines, ETL/ELT, data lineage, state machines, or to convert/beautify Mermaid.
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。
Tiered web access router. TIER 1 insane-search: headless extraction plus WAF bypass via curl_cffi, yt-dlp, Jina Reader, public APIs, Playwright fallback for blocked/403/Cloudflare pages, articles, YouTube subtitles. TIER 1.5 agent-reach: platform-native readers for Chinese/social platforms: Xiaohongshu, Douyin, Weibo, Bilibili, V2EX, WeChat, plus Twitter/Reddit/LinkedIn/GitHub. TIER 2 aside-browser FIRST (user's real logged-in browser: aside exec agent, aside repl Playwright), then CloakBrowser + agent-browser as the stealth fallback: source-patched stealth Chromium (CloakBrowser) driven over CDP by the agent-browser CLI for clicks, forms, screenshots, video, cookie login, QA/dogfood, Electron apps. Triggers: browse, stealth browser, CloakBrowser, agent-browser, cloak launch, bypass bot detection, blocked site, scrape, fill form, screenshot, import cookies, play youtube, podcast transcript. NOT for simple searches (web-search), fetches (webfetch), or messaging/workspace platforms — Slack/Discord/Notion/Gmail have Tier-1 CLIs in external-communication; come here only as its fallback.
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。
A test skill for e2e testing
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。
When the user wants to audit, review, or diagnose SEO issues on their site. Also use when the user mentions "SEO audit," "technical SEO," "why am I not ranking," "SEO issues," "on-page SEO," "meta tags review," or "SEO health check." For building pages at scale to target keywords, see programmatic-seo. For adding structured data, see schema-markup.
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 implementing features or fixing bugs with strict Test-Driven Development (TDD). Activate for coding tasks that require new functionality, refactoring, or comprehensive test coverage, especially when the user mentions TDD or Test Driven Development.
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。
Repository-grounded threat modeling that enumerates trust boundaries, assets, attacker capabilities, abuse paths, and mitigations, and writes a concise Markdown threat model. Use when the user explicitly asks to threat model a codebase or path, enumerate abuse paths, or perform AppSec threat modeling.
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。
Perform language and framework specific security best-practice reviews and suggest improvements. Use when the user explicitly requests security best practices guidance, a security review/report, or secure-by-default coding help. Trigger only for supported languages (python, javascript/typescript, go), and not for general debugging or non-security tasks.
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。
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction.
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。
Optimize content for AI search engines (ChatGPT, Perplexity, Claude, Gemini) and featured snippets. Covers Generative Engine Optimization (GEO), Answer Engine Optimization (AEO), E-E-A-T signals, and content structuring for maximum AI citation probability. Use when optimizing content for AI visibility or auditing AI search readiness.
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。
When the user wants to add, fix, or optimize schema markup and structured data on their site. Also use when the user mentions "schema markup," "structured data," "JSON-LD," "rich snippets," "schema.org," "FAQ schema," "product schema," "review schema," or "breadcrumb schema." For broader SEO issues, see seo-audit.
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 voice AI agents with LiveKit Cloud and the Agents SDK. Use when the user asks to "build a voice agent", "create a LiveKit agent", "add voice AI", "implement handoffs", "structure agent workflows", or is working with LiveKit Agents SDK. Provides opinionated guidance for the recommended path: LiveKit Cloud + LiveKit Inference. REQUIRES writing tests for all implementations.
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。
数十秒を超えて走るプログラム・スクリプト（学習・評価・データ処理・サーバ等）をログを残す形で起動し，異常の早期検知と完了報告まで担う．「実行して」「動かして」「学習を回して」など，明示の有無に関わらず自動適用する．数十秒で終わる実行（テスト・ビルド・lint 等）は対象外．既に動いているものの状況を知りたいだけなら /ops:progress，UI を見た目で確かめたいだけなら組み込みの run スキルを使う．
テストの棚卸し・削減依頼で、保証と維持コストを比較し、削除・統合候補を根拠付きで示す。既定は監査のみ。