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概要と使いどころ
Monitor competitor pricing, features, changelogs, dashboards, and product changes with Firecrawl. Use for recurring competitive intelligence, pricing tier extraction, feature change tracking, or structured competitor alerts.
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。
Ingest public or authenticated knowledge bases and docs portals with Firecrawl browser. Use for JS-heavy docs, login-gated portals, paginated help centers, support knowledge bases, or structured JSON/markdown extraction from documentation sites.
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。
Pull metrics from analytics dashboards and internal web tools with Firecrawl browser. Use when the user needs dashboard reporting, cross-platform metric summaries, authenticated analytics extraction, date-range reports, or structured metrics from web dashboards.
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 AI to write NEW test code from specs, PRDs, user stories, code diffs, bug reports, or OpenAPI specs. Staged pipeline: requirements extraction → risk analysis → coverage matrix → scenario generation → oracle design → test code → human review, with guardrails against hallucinated APIs and weak assertions. Use when: "generate tests from spec," "tests from PRD," "tests from user story," "auto-generate test cases," "AI write tests for me." Not for: testing AI/LLM features in your product — use ai-system-testing. Not for: auditing a pre-existing test suite you did not just generate — use ai-qa-review (Step 7 here only reviews tests THIS pipeline produced). Related: playwright-automation, unit-testing, api-testing, qa-project-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。
End-to-end testing of email-dependent flows — signup confirmation, password reset, magic-link login, OTP/MFA codes, and notification emails. Covers the capture-inbox decision tree (Mailpit, Mailosaur, MailSlurp, Ethereal), Playwright polling without fixed sleeps, regex extraction of links/OTPs from the email body, deterministic per-test addresses (plus-addressing, per-inbox), subject/from/header/link assertions, and SPF/DKIM/DMARC deliverability checks as a separate suite. Use when: "test the signup confirmation email," "password reset email test," "magic-link login test," "capture OTP from email," "Mailpit," "Mailosaur," "MailSlurp," "email arrives flaky in CI," "assert email subject/from/links." Not for: Sending transactional email from your app code, or API-only contract tests of an email provider — those are api-testing / app concerns. Email HTML rendering across clients (Outlook/Gmail dark mode) is out of scope (note it as a gap; use visual-testing or Litmus). Related: playwright-automation, api-testing, test-data-management, qa-project-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。
Google search via real Chrome browser (chromux). Use when the user asks to "search Google", "구글 검색", "구글에서 찾아줘", "find articles about", "search the web for", or needs web search results with full page content. Also trigger when: searching a specific site ("search dev.to for..."), finding recent articles/posts about a topic, extracting content from search results, or when WebSearch results are insufficient and real browser rendering is needed. Prefer this over WebSearch when the user wants site-specific search, time-filtered results, or full article body/comments extraction.
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。
Hoyeon deep research workflow for Codex. Use when the user invokes "$hoyeon-deep-research" or wants a multi-channel cited research report using web search, browser extraction, and optional Gemini. This adapter loads the canonical deep-research skill and follows its Codex runtime surface.
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 an extremely strict maintainability review for abstraction quality, giant files, and spaghetti-condition growth. Use for a thermo-nuclear code quality review, thermonuclear review, deep code quality audit, or especially harsh maintainability review.
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。
Apply when refactoring, evaluating diff size, or tempted to add abstractions, layers, or signal threading. Bias toward deletion and the smallest change that solves the problem.
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。
Extract structured data from any document format using unified document processing. Use when a user asks to extract data from a document, parse a PDF, pull structured data from files, convert documents to JSON or CSV, extract fields from invoices or forms, or scrape data from documents.
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。
Crossplane for infrastructure as code using Kubernetes CRDs. Use when the user needs to provision and manage cloud resources declaratively through Kubernetes APIs, compose custom infrastructure abstractions, or build internal platforms.
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。
Parse and extract data from HTML with Cheerio. Use when a user asks to scrape static web pages, parse HTML files, extract data from HTML, build a web scraper for server-rendered pages, extract text or links from HTML documents, parse RSS/XML feeds, transform HTML content, or process HTML emails. Covers jQuery-style selectors, DOM traversal, text extraction, attribute parsing, and integration with HTTP clients for web scraping pipelines.
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。
Extract text from complex documents using Chandra OCR — handles tables, forms, handwriting, and full page layouts with high accuracy. Use when: extracting data from scanned documents, reading complex tables from PDFs/images, processing handwritten forms.
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。
Patterns for building multimodal AI applications that combine text, images, audio, and video. Covers vision APIs, audio transcription, and unified pipelines. Use when "multimodal AI, vision API, image understanding, GPT-4V, Claude vision, audio transcription, Whisper, document extraction, image to text, " mentioned.
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。
Patterns for embedded software development including real-time systems, memory management, hardware abstraction, interrupt handling, and debugging techniques for resource-constrained environments. Use when ", " mentioned.
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。
Comprehensive expertise in cross-chain infrastructure, including LayerZero, Wormhole, Axelar, and custom bridge implementations. Covers omnichain token standards, message passing, bridge security, and cross-chain application architecture. Use when "cross-chain, bridge, LayerZero, Wormhole, Axelar, omnichain, multi-chain, chain abstraction, cross-chain messaging, token bridge, " mentioned.
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。
Detects and redirects scope creep during implementation — unrequested features, drive-by refactors, premature abstraction, and over-engineering. Use while implementing, especially when a change is growing beyond the original request.
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。
Extracts and records architectural decisions from diffs, conversation context, and explicit choices. Supports automatic extraction from staged changes, deduplication against prior decisions, and persistent ADR-format logging with spec traceability. Use after a significant design or architecture choice, before committing a diff that embeds implicit decisions, or when the same question keeps resurfacing.
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。
Family-G proof & derivation integrity forensics: does a THIRD PARTY's written proof/derivation actually establish its theorem, or does it skip an obligation, assume its own conclusion, take an invalid step, drift a symbol's meaning, or smuggle an unstated assumption? Decides from the WRITTEN proof/derivation — verdict-bearing at L1 (the LaTeX source; PDF-extracted math is unreliable, so an L0 PDF-only run surfaces info only) — never asserts 'fabricated', only that the step shown does not hold. A fresh cross-model reviewer reads the theorem/proof + an extraction-only obligation scaffold and proposes per-obligation findings, each span-anchored to the evidence ledger (claims.json); reviewer≠adjudicator. Emits proof-derivation-forensics.findings.json; NEVER computes the verdict. dimension=proof, can be critical. Triggers: "proof forensics", "check this proof", "derivation integrity", "audit the math", "证明审计", "推导有没有漏洞".
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 Content Understanding SDK for Python workflow skill. Use this skill when the user needs Azure AI Content Understanding SDK for Python. Use for multimodal content extraction from documents, images, audio, and video and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing 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。
Azure AI Content Understanding SDK for Python workflow skill. Use this skill when the user needs Azure AI Content Understanding SDK for Python. Use for multimodal content extraction from documents, images, audio, and video and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing 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。
AWS Penetration Testing workflow skill. Use this skill when the user needs Provide comprehensive techniques for penetration testing AWS cloud environments. Covers IAM enumeration, privilege escalation, SSRF to metadata endpoint, S3 bucket exploitation, Lambda code extraction, and persistence techniques for red team operations and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing 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。
AWS Penetration Testing workflow skill. Use this skill when the user needs Provide comprehensive techniques for penetration testing AWS cloud environments. Covers IAM enumeration, privilege escalation, SSRF to metadata endpoint, S3 bucket exploitation, Lambda code extraction, and persistence techniques for red team operations and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing 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。
Use this skill to operate the Aomi CLI for natural-language crypto and DeFi workflows, including balance checks, route simulation, transaction review, and explicit user-approved signing flows across EVM chains.
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。