「ema」の検索結果

Detect, validate, and generate Schema.org structured data. JSON-LD format preferred. Use when user says "schema", "structured data", "rich results", "JSON-LD", or "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。

Build evidence-labeled semantic cores for SEO, LLM-readable architecture, and site information design. Use this skill when the user asks for semantic core, query clusters, search intents, audience segments, entity/topic maps, language or locale priorities, competitor/source evidence, pillar-topic planning, or a structured handoff before URL architecture and internal linking. It must mark unknown search volume, difficulty, and ranking data as unknown instead of inventing metrics.

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 bounded Proto UI Contract, Prototype, Module, Host Capability, Adapter, schema, or ownership question and turn a genuinely unresolved direction into one semantic decision packet. Use for spec brainstorming, admission questions, competing semantic models, uncataloged behavior, or unresolved cross-layer boundaries.

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。

Analyze codebases using a graph database instead of reading source files. Use when understanding code architecture, finding functions or call patterns, tracing data flow, checking dependencies, or answering "where is X used?" questions. Grafema builds a queryable code graph from static analysis — prefer querying the graph over reading files manually.

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。

Systematic methodology for achieving 100% backward dataflow reachability in a new language. Create gauntlet fixture, write trace, diagnose gaps, fix analyzer/algorithm, iterate to 100%. Language-agnostic process. Use when: (1) adding a new language to Grafema, (2) auditing dataflow coverage for existing language, (3) user says "/dataflow-gauntlet".

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。

Pattern for adding derived edge types in the Grafema Rust orchestrator (main.rs). Use when: (1) need to create new graph edges derived from existing edges (e.g., MODULE-level DEPENDS_ON from IMPORTS_FROM), (2) Datalog joins on large edge sets time out, (3) adding enrichment steps that combine data from multiple resolver outputs, (4) need to understand how resolver outputs flow through the orchestrator pipeline.

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。

Grafema release procedure for publishing new versions to npm. Covers happy path, pitfalls, rfdb binary lifecycle, and rollback. Use when user says "release", "publish", "bump version".

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 and verify NowStack Mobile transactional email with Resend and Convex. Use for sender/domain setup, OTP delivery, password/reset messages, production email credentials, or deliverability failures.

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, plan site architecture, or implement schema markup. Use when someone says 'SEO audit', 'technical SEO', 'site architecture', 'schema markup', 'internal linking', 'why isn't my site ranking', 'site health check', 'crawl issues', 'fix my SEO', 'my traffic dropped', 'rankings fell', or 'site not ranking'. Also trigger when someone wants to plan URL structure, design navigation, add structured data, or review any website for search performance. Three modes: Full Audit (comprehensive health check), Architecture (URL structure and internal linking), Schema (JSON-LD structured data). Covers crawlability, indexation, Core Web Vitals, and on-page factors.

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。

Reorganize label-heavy or netlist-like electronic schematics into human-readable functional diagrams with visible local wiring while preserving proven connectivity. Use for schematic readability correction, overlapping wires or symbols, visually floating connectors or support logic, functional-sheet restructuring, bus presentation, layout annotations, or multi-EDA handoff across KiCad, Altium, EasyEDA, EAGLE/Fusion, gEDA/Lepton, SPICE/netlist, and PDF/image 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。

Provides a complete workflow for implementing verified email retrieval on Android Credential Manager API. Use this skill to integrate a secure, OTP-less email verification flow into an Android app. This skill solves the problem of high-friction sign-up processes by leveraging cryptographically verified credentials from trusted providers like Google.

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 AI demand forecasting systems. TRIGGERS - Use when user needs help with ai-demand-forecaster related 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。

Build AI email writing systems. TRIGGERS - Use when user needs help with ai-email-writer-system related 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。

Design AI email assistants with drafting and triage. TRIGGERS - Use when user needs help with ai-email-assistant related 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。

Investor Email Specialist. Use to write fundraising outreach, warm intro blurbs, follow-ups, investor updates, and close-the-loop emails with founder-grade brevity and specificity.

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 Stakeholder Theory (Freeman, 1984) and the Mitchell et al. (1997) salience model to identify, classify, and prioritize stakeholders based on power, legitimacy, and urgency. Use this skill when the user needs to map stakeholders for a project or strategy, determine which stakeholders demand immediate attention, balance competing stakeholder interests, or when they ask 'who are our key stakeholders', 'how do we prioritize conflicting demands', or 'which stakeholders can block this initiative'.".

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"Conduct a systematic literature review following the PRISMA framework with explicit search strategy, inclusion and exclusion criteria, quality assessment, and transparent synthesis. Use this skill when the user needs to design a reproducible literature search, apply PRISMA flow documentation, develop inclusion and exclusion criteria, assess study quality, or when they ask 'how do I do a systematic review', 'what is PRISMA', or 'how do I make my literature review reproducible'.".

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 supply and demand analysis to explain price determination, market equilibrium, and the effects of policy interventions. Use this skill when the user needs to analyze how prices are set in a market, predict the effect of taxes/subsidies/price controls, or understand shifts in supply or demand curves — even if they say 'why did the price go up', 'what happens if the government sets a price cap', or 'how does a tariff affect the market'.".

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。

"Implement Schema.org structured data markup in JSON-LD format for enhanced search results. Use this skill when the user needs to add rich snippets to web pages, implement FAQ/Product/Article schema, or validate structured data — even if they say 'rich snippets', 'structured data', or 'Google rich results'.".

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。

"Calculate text similarity using lexical and semantic methods for matching and deduplication. Use this skill when the user needs to find similar documents, detect near-duplicates, or measure semantic closeness between texts — even if they say 'how similar are these texts', 'find duplicates', or 'semantic matching'.".

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。

Draft clear, professional business emails from rough notes, context, or instructions. Handles cold outreach, internal updates, client responses, and escalation drafts. Use when the work is routine email writing with known context and audience. Do not use for sensitive legal notices, formal regulatory correspondence, or board-level communication without 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。

Identify and exploit Server-Side Template Injection (SSTI) vulnerabilities within web applications. Use this skill when testing web applications that render dynamic content using template engines like Jinja2 (Python), Twig (PHP), Freemarker (Java), or Pug (Node.js). Covers identification via mathematical evaluation, context mapping, escaping sandboxes, and achieving Remote Code Execution (RCE).

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Test for indirect prompt injection vulnerabilities where malicious instructions are injected through external data sources (websites, emails, documents, database records) that the LLM processes. Use this skill when assessing LLM-integrated applications that process user-generated content, retrieve web pages, parse emails, or read documents. Covers injection via web content, email bodies, user profiles, and database records that are fed to LLM 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。

Email infrastructure security audit. Tests SPF, DKIM, DMARC configuration, open relay, email spoofing resilience, S/MIME, MTA-STS, TLS-RPT, and SMTP security. Uses swaks, dnsrecon, nmap SMTP scripts, smtp-user-enum, and dig. Pure skill markdown — no new infrastructure needed.

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。