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Research competitor advertising strategies through Facebook Ad Library, Google SERP ads, and social media sponsored content. Covers creative analysis, messaging patterns, targeting signals, and campaign frequency for competitive marketing intelligence.
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 create SEO-driven pages at scale using templates and data. Also use when the user mentions "programmatic SEO," "template pages," "pages at scale," "directory pages," "location pages," "[keyword] + [city] pages," "comparison pages," "integration pages," "building many pages for SEO," "pSEO," "generate 100 pages," "data-driven pages," or "templated landing pages." Use this whenever someone wants to create many similar pages targeting different keywords or locations. For auditing existing SEO issues, use an available SEO audit capability. For content strategy planning, use an available content strategy capability.
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 TAM/SAM/SOM for market opportunities using top-down, bottom-up, and value theory methodologies. Use this skill when sizing markets, estimating addressable revenue, validating market opportunity for a new venture, or building investor-ready market analysis for a startup pitch or business 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。
Find and prioritize ethical backlink and digital-PR opportunities using SandBase DataForSEO backlink data. Use when asked to analyze backlink profiles, find referring-domain gaps, compare link competitors, or prepare a link-outreach target list.
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codeck entry point. Scans local files for materials, shows pipeline dashboard with diagnostic intelligence, guides user to the next step. Use when the user says "codeck", "new deck", "make a presentation", "make a deck", "new slides", "build a presentation", "export", "speech", "script", or wants to start or continue a presentation project. /codeck is the user-facing entry; legacy sub-skills are internal modules.
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。
Help fantasy players make start/sit, waiver, streaming, trade, draft and keeper decisions using current player news, usage, matchups and their league's scoring. Use for choosing between players, finding pickups, evaluating a deal or filling a roster need, even without an explicit research request. Excludes generic sports scores, betting and changing league rosters.
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。
Inspect a website's titles, meta descriptions, headings, canonicals, structured data and internal links for on-page and technical SEO issues. Use for SEO audits, page-source checks, broken-link reviews or comparing pages for target keywords, even without an explicit request to scrape. Return source-backed findings and prioritized fixes. Excludes generic SEO explanations, standalone copywriting and functional website 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。
Define, track, and act on QA metrics: test coverage percentage, flakiness rate, defect escape rate, MTTR, test execution time trends, automation ROI, quality gates, and SLAs for test suites. Includes metric formulas, realistic targets by company stage, and the action to take when each metric goes red. Use when: "QA metrics," "test metrics," "quality KPIs," "test health," "flakiness rate," "defect escape rate." Not for: building the dashboard UI (Allure/Grafana) — use qa-dashboard; measuring coverage gaps and mutation score — use coverage-analysis. Related: qa-dashboard, coverage-analysis, ci-cd-integration, release-readiness, quality-postmortem.
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。
Test AI/LLM features that ship in your product. Covers prompt regression testing, response quality evaluation, tool-call validation, hallucination and RAG grounding checks, nondeterministic-output strategies, red-team/safety scans, eval frameworks, and agent-as-target injection (indirect injection via tool output / RAG / scan reports, self-propagating payloads, data exfiltration via an agent) plus a bundled detector for untrusted content. Use when: "test our LLM feature," "prompt regression test," "eval framework," "hallucination test," "RAG grounding," "nondeterministic output," "AI feature testing," "red-team our chatbot," "indirect prompt injection," "agent reading untrusted tool output," "production AI quality." Not for: using AI to generate your own test code — use ai-test-generation. Not for: classifying CI failures with AI — use ai-bug-triage. Not for: EU AI Act / GDPR conformity of an AI feature — use compliance-testing. Not for: canary/flag rollout of an AI feature — use testing-in-production. Related: ai-test-generation, ai-qa-review, api-testing, compliance-testing, security-testing, risk-based-testing, test-data-management.
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 working on Formax code changes and you need a disciplined dev loop: keep a single mainline task, avoid scope drift, run only targeted tests (no coverage), avoid partial staging (MM), run mandatory review before commit, include an incremental optimization check, and keep commits small and reviewable.
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 or refactor Formax tool transcript UI using the Tool UI Blocks (C-lite) pattern (ToolUiBlocks renderer + blocks presenters) to avoid touching many tool presenter files; use when adjusting ⏺/⎿ spacing, indent rules, or migrating additional tools to blocks presenters with targeted Ink/Vitest tests and Codex review before commit.
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 starting creative work (features, components, functionality, or behavior changes) to clarify user intent, requirements, and design before implementation.
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。
Submit proof requests on the Boundless ZK proof marketplace. Covers wallet setup, CLI configuration, building or discovering guest programs, self-hosting via Cloudflare Quick Tunnels (no Pinata/S3 needed), submitting, and retrieving results. Use when a developer wants to request a ZK proof, submit a proof request, get started with Boundless, try Boundless, or learn the requestor workflow.
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 starting, continuing, updating, exporting, or sharing a running transcript of a MATLAB AI tutoring session, especially when the transcript will be shared with an instructor, attached to a learner session report, or passed to an evaluation workflow for quality 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。
Use when a learner or instructor asks for a report, summary, reflection, progress note, performance recap, activity metrics, instructor-shareable record of a MATLAB AI tutoring session, aggregate report across multiple MATLAB tutoring session reports, or instructor dashboard artifact with metric drilldowns. Supports optional start and end datetime arguments for multi-session report and dashboard ranges.
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。
Interview an instructor and generate a complete MATLAB and Simulink enabled course in IMS Common Cartridge format. Use when the user asks to create a MATLAB course, Simulink course, MATLAB and Simulink curriculum, MATLAB Course Designer-ready course shell, Common Cartridge course package, .imscc export, MATLAB Exercises with validated .m files, or Simulink Exercises with starter and solution model files. Coordinates IDStack, MATLAB Agentic Toolkit, Simulink Agentic Toolkit, matlab-create-course-activity, simulink-create-course-activity, and the existing matlab-generate-grader-assessments skill.
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。
Deploy AI models to embedded hardware using MathWorks tools (MATLAB, Simulink, Embedded Coder). Covers two workflow patterns: (1) MathWorks-native or 3P-imported models rebuilt as dlnetwork for lean hardware (Cortex-M, DSP), (2) direct C/C++ code generation from PyTorch and LiteRT models for high-performance hardware (Cortex-A, x86, GPU). Trigger when: user wants to deploy AI to embedded targets; generate C/CUDA from neural networks; compress AI models for MCU/DSP; integrate AI in Simulink for system-level simulation; import PyTorch/ONNX/TensorFlow models for embedded deployment; optimize AI for resource-constrained hardware; or use loadPyTorchExportedProgram, importNetworkFromPyTorch, dlquantizer, exportNetworkToSimulink, or Embedded Coder with AI models.
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。
Play Pokémon games via headless emulation. Start a game server, read game state, make strategic decisions, and send actions — all from the terminal.
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 when working with RenderDoc capture files (.rdc), analyzing GPU frames, tracing shaders, inspecting draw calls, or running CI assertions against GPU captures. Trigger phrases: "open capture", "rdc file", ".rdc", "renderdoc", "shader debug", "pixel trace", "draw calls", "GPU frame", "assert pixel", "export render target".
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。
Review and upgrade MetaX model support against a target vLLM revision and installed MACA components, recursively including model-dependent attention and kernels. Establish quantization and cache differences, preserve upstream structure, and distinguish shared upstream bugs from adaptation defects. Use for model support work, not standalone monkey-patch or registry 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。
Review and adapt vllm_metax/registry registrations, quantization configurations, CustomOps and kernel dispatch against a target vLLM revision and installed MetaX APIs. Verify registration, inherited state, weight layouts and actual backend selection. Use for registry compatibility work, not standalone attention algorithms or monkey patches.
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。
Audit and adapt monkey patches in vllm_metax/patch/ against a target upstream revision. Decide whether to retain, update, migrate, or remove each patch; maintain patch headers and explanations; validate changes and record evidence. Use only for compatibility work on this directory, not standalone attention backend, model, kernel, or other adaptations elsewhere in vllm_metax.
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。
Review and adapt MetaX attention backends, MLA, sparse indexers, cache layouts, and their kernel wrappers against a target vLLM revision and the actually installed MetaX component APIs. Verify dispatch, supported configurations, and GPU numerical behavior. Use for attention adaptations outside vllm_metax/patch/; do not apply monkey-patch headers or patch audit 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。
Establish the shared environment, source/runtime correspondence, target confirmation and validation evidence for MetaX vLLM upgrades. Use as the common prerequisite of the patch, attention, registry and model upgrade skills, or for an explicitly requested MetaX environment preflight; it does not perform domain-specific adaptations.
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。