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概要と使いどころ
Create, edit, analyze, and verify professional Excel workbooks for general business and operational use. Use when the deliverable is an .xlsx file; use a finance-specific modeling workflow for valuation or regulated financial models.
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Research and evaluate backlink analysis with current authoritative sources, provenance, and explicit evidence gaps. Use when the user asks for backlink analysis findings or verification.
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。
Research and evaluate github gem seeker with current authoritative sources, provenance, and explicit evidence gaps. Use when the user asks for github gem seeker findings or verification.
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 after receiving WebGPT or another LLM review on a Formax todo and before sending the todo back for another pass. Produce a concise handoff response that says what we adopted, what we reject or question, and what the reviewer should specifically re-evaluate in `docs/todolist.md`.
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。
Internal — for Boundless team members only. Query Boundless broker telemetry tables on Redshift for prod/staging operational data. Use when the user asks about broker health, request evaluations, request completions, proving times, skip rates, telemetry data, or wants to run SQL against the telemetry database on live networks. Also covers historical telemetry through 2026-04-24 stored as Parquet archives in S3 (queried via DuckDB). Do NOT use for debugging local code changes, reviewing PRs, or investigating issues in the codebase itself.
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Research products across the web with Firecrawl and produce a shopping recommendation or cart-ready summary. Use when the user wants to compare products, find the best option, evaluate reviews, respect budget/preferences, or shop with a saved browser session.
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。
AI SDK for Python (the `ai` package). Use for Python model calls (LLMs, image, video, speech, embedding, transcription, reranking, or evaluation), agents, model interaction tests, tool calling, subagents, approvals, durable execution, telemetry, AI SDK UI backends, and custom providers.
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 needs a professional marketer's operating mindset for any marketing, growth, or client-acquisition task — finding first customers, writing an ad or landing page, designing ad creatives and visuals (how the eye works: background, scene, hero, movement), evaluating an idea, deciding whether to do X to get Y, positioning or launching a B2B or SaaS product, running cold outreach, setting up ads, or writing copy — not a tactical template.
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。
CesiumJS spatial math - Cartesian3, Cartographic, Matrix4, Quaternion, Transforms, Ellipsoid, BoundingSphere, projections, coordinate conversions. Use when converting between coordinate systems, computing positions on the ellipsoid, performing spatial intersection tests, building model matrices, or working with geographic projections.
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 reviewing, auditing, scoring, or improving a real or synthetic MATLAB AI tutor transcript, tutoring prompt, generated lesson, exercise, feedback sequence, or skill behavior for MATLAB accuracy, active learning, assignment guardrails, feedback quality, debugging support, transfer prompts, and instructor-facing quality recommendations.
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Use when prompting a learner to complete hands-on MATLAB coding exercises, guided practice, debugging drills, code tracing, small MATLAB projects, or MATLAB-script assessment during tutoring. Use when the tutor should create a complete runnable MATLAB script, execute it through MATLAB tools, compare the produced outputs with expected outputs, and evaluate MATLAB programming style.
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.
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Test for regulatory compliance: GDPR/CMP consent verification, Google Consent Mode v2, Global Privacy Control (GPC), CCPA/US state opt-out, EU AI Act Article 50 transparency, Better Ads Standards, and cookie-inventory auditing. Covers automated consent-flow testing, third-party script blocking before consent, and cookie drift detection. Use when: "GDPR test," "compliance," "CMP test," "cookie consent," "consent mode," "CCPA," "GPC," "AI Act," "Better Ads," "privacy banner." Not for: WCAG/axe-core test authoring — use accessibility-testing. Not for: OWASP/vuln scanning — use security-testing. Not for: evaluating your LLM feature's quality or safety — use ai-system-testing. Related: accessibility-testing, security-testing, ai-system-testing, ci-cd-integration.
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Author and maintain MANUAL and hybrid test cases and suites in TestRail, Xray (Jira), Zephyr Scale, and Qase. Covers test-case anatomy (title, preconditions, steps, expected results, test data), suite/section organization, bulk authoring from user stories and acceptance criteria, ambiguous-step linting, CSV/API import-export payloads per tool, requirement traceability and coverage gaps, review hygiene, and when a manual case should graduate to automation. Use when: "write a test case," "manual test case," "TestRail case," "Xray test," "Zephyr Scale case," "Qase case," "import CSV into TestRail," "lint these steps," "traceability report," "should this be automated." Not for: Generating automated TEST CODE — that is ai-test-generation. Sprint-level WHAT-to-test selection — that is test-planning. Related: ai-test-generation, test-planning, exploratory-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。
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。
Goal-driven E2E testing where a browser agent (Playwright MCP / computer-use) reads a natural-language goal and explores the app via the accessibility tree to assert outcomes — no pre-written script. Covers when intent-driven beats scripted, making agent runs deterministic (pinned model, temperature 0, seeded data, bounded steps, explicit success assertion, snapshot-not-pixel), cost/latency control, the accessibility-tree-first interaction model, CI gating, and graduating a stable run into a scripted Playwright test. Use when: "agentic browser test," "goal-driven browser test," "let an agent explore the app," "natural-language E2E," "browser agent smoke test," "Playwright MCP test." Not for: Writing/maintaining deterministic scripted Playwright tests — that is playwright-automation. Testing your product's OWN LLM features — that is ai-system-testing. Related: playwright-automation, ai-system-testing, exploratory-testing, test-reliability, 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。
Ask which skill or flow fits your situation. A router over the skills in this repo.
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。
Trim large MetaX model directories for dummy smoke tests or real-checkpoint loading on limited GPUs. Preserve target execution paths while reducing depth; use the real-weight workflow when a loadable checkpoint is requested. Not for accuracy evaluation or model compatibility upgrades.
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。
Finds and analyzes 5-10 comparable recent sales to estimate fair market value, calculate price adjustments, and score the property's value proposition
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。
60-Second Property Snapshot — quick assessment without subagents for fast property evaluation with signal, key factors, and CTA for full analysis
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。
Evaluates a property across three investment strategies — Buy & Hold, BRRRR, and Fix & Flip — with feasibility scores, multi-year projections, tax benefits, and break-even analysis
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Reviews and adjusts PRD plans based on research findings. Use after completing research to evaluate story modifications. Triggers on: review plan, adjust stories, update prd based on research.
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Construct a high-conversion offer using Alex Hormozi's value equation and Grand Slam Offer mechanics during PRD v0.9 Go-to-Market. Triggers on requests to design the offer, set up the pitch, build bonuses or guarantees, or when user asks "how do we package this?", "what's the offer?", "build a grand slam offer", "Hormozi", "$100M offers", "guarantee", "bonus stack". Outputs GTM-* entries with Type=Offer / Type=Guarantee and BR-PRICING-* updates.
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。
Validates gate criteria before PRD lifecycle advancement by delegating to the readiness scoring pipeline (scripts/readiness.py). Returns a graduated PASS / WARN / BLOCK verdict with top blockers and their causal chain. Triggers before advancing from v0.X to v0.Y or explicit `/ghm-gate-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。