4,758 件 ・ 関連度順
概要と使いどころ
Autonomous, report-only documentation review for Mastra docs. Use when auditing changed docs against source, validating contextual code examples or API coverage, checking canonical mastra-docs guidance, or running narrow deterministic checks.
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。
REQUIRED and PRIMARY testing approach for packages/playground and packages/playground-ui. Triggers on: adding or modifying hooks, pages, route components, data-fetching code, React Query interactions, or any test work in these packages. Generates Vitest tests that drive the real @mastra/client-js + React Query stack through MSW handlers and typed fixtures derived from @mastra/client-js response types. This is the #1 way to test the playground packages — ABOVE Playwright E2E. Use Playwright only for cross-page user journeys that MSW cannot model.
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。
Reproduce and measure a Mastra Code runtime bug against a real model by driving the built TUI headlessly in tmux while every layer (network, provider stream, run engine, TUI) appends timestamped JSONL. Use when a bug depends on real provider timing, streaming order, or event flow that mocked tests and fixtures may not reproduce — e.g. wrong token rates, flicker, stuck status, ordering races, or "only happens sometimes with a real model".
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。
Query the authoritative global Herdr Review and Work Manager inventory, recommend actionable PR work, and explicitly materialize confirmed remote-only Review Manager records through the plugin's stable public CLI.
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 early when debugging a medium or hard bug, especially when tests alone may not reveal the real runtime failure. Trigger this before extended TDD iteration when a bug involves runtime state, ordering, persistence, streaming, concurrency, UI/manual reproduction, external services, or when a red or newly passing test may not model the real issue. Skip only when the root cause is already directly proven by a stack trace or deterministic test that exercises the real runtime path.
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。
Fallback authoring playbook for building general-purpose personal assistant agents that do not fit a more specific archetype. Use this only after checking the other archetype skills (coding, spreadsheet, research, customer-support, content-writer, ops-automation). Examples include summarizing emails, drafting short answers, capturing notes, or generic personal-helper agents.
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 migrate Table v8 to v9. Inventory affected APIs, follow the shared checklist, and read only the required architecture, state, feature, and TypeScript mappings.
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。
Add or debug Table v9 features: registration, row-model slots, prerequisites, sorting, filtering, pagination, selection, spanning, and column layout. Read only task-relevant feature references.
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。
Coordinate a small team of Qwen Code teammates with enforced read-only workers, an optional worktree-pinned writer, shared tasks, peer messages, and existing Agent View tabs. Invoke explicitly with /coordinate.
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 for a periodic repo-wide sweep of qwen-code for accumulated excess surface — dead components and files, orphaned locale keys, exports nothing consumes, added-then-removed scaffolding — filing candidates on a tracking issue and landing only what a maintainer has said yes to. Repo-wide and evidence-first; every consumer is named before anything is deleted. Not for tidying a diff you just wrote (that is bundled `/simplify`) and not for defects (that is `/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。
Turn a fuzzy intention into a /goal objective the Goal verifier can actually judge - one outcome, numbered binary "Done when" checks that leave evidence in the transcript, guardrails, a budget, and a block protocol. Use when the user wants to set or define a goal, asks whether a goal is good enough, or says "keep going until X". Usage - /goal-draft <what you want done>, or /goal-draft <existing goal> to tighten it. This skill only writes the objective; it never starts the work.
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 managing the money for an internal R&D program or portfolio — building a multi-period program budget with the F&A (indirect) split, tracking burn rate and runway against value-inflection milestones, or routing R&D cost items to a capitalize-vs-expense determination. Every budget output surfaces its assumptions block; capitalize-vs-expense is decision-support only and routes to a named finance owner — it never books an entry or decides accounting treatment. Distinct from finance/financial-analysis (corporate DCF, close, valuation) and research/grants (funding discovery — this manages money already won).
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。
Converts a markdown PR writeup or code review (one with ```diff fenced blocks and severity-tagged > [!BLOCKER]/[!MAJOR]/[!MINOR]/[!NIT] callouts) into a single-file 2-column HTML review — unified-diff on the left, severity-tagged annotation cards on the right, top jump-nav listing every finding, mandatory named reviewer footer. Triggers when the markdown-html-orchestrator classifies an input as REVIEW, or when invoked directly via /cs:md-review. Refuses without explicit --reviewer (a code review must name a human), refuses if no diff hunks present (route to md-document instead), and refuses to encode severity in color only (every badge ships color + icon + aria-label per WCAG 1.4.1). Use after orchestrator routing.
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。
Answer Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings. Use when planning content for AI-first search audiences, auditing existing content for E-E-A-T signals, tracking which pages get cited by which LLMs, or building a citation-friendly content strategy. Triggers — 'AEO audit', 'optimize for ChatGPT', 'get cited by Perplexity', 'LLM citation strategy', 'answer engine optimization', 'content for AI search', 'E-E-A-T audit'. Output is a markdown audit report (default) or JSON for pipeline integration. Stdlib-only Python tools.
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 content production pipeline — takes a topic from blank page to published-ready piece. Use when you need to execute content: write a blog post, article, or guide end-to-end. Triggers: 'write a post about', 'draft an article', 'create content for', 'help me write', 'I need a blog post'. NOT for content strategy or calendar planning (use content-strategy). NOT for repurposing existing content (use content-repurposing). NOT for social captions only.
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。
Produce a rigorous, sector-relative, multi-factor fundamental analysis of a publicly listed company — Indian (NSE/BSE) or US/global. Use when the user asks to analyse, research, evaluate, or value a stock, ticker, or listed company; asks whether a business is fundamentally strong, cheap, or expensive; compares companies or benchmarks one against its sector; or mentions OPM, ROCE, ROE, ROIC, P/E, EV/EBITDA, free cash flow, NIM, GNPA, CASA, promoter holding or pledging. Use it for accounting-quality and forensic questions — "is the profit real", "why is profit rising but cash isn't", auditor qualifications, related-party concerns — which route to the forensic-only mode, and for IPOs and not-yet-listed companies — "should I apply to this IPO", DRHP/RHP or S-1 questions, price band, grey market premium — which route to the IPO mode. Use it even when the request sounds casual ("is Infosys any good?"). Do not use it for personalised investment advice, portfolio allocation, or trading signals.
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。
Converts long-form markdown (specs, RFCs, reports, plans, explainers) into a single-file, lightly-interactive HTML document with sticky TOC, scrollspy, search filter, code-copy buttons, and design-system-driven brand tokens. Triggers when the markdown-html-orchestrator classifies an input as DOCUMENT, or when invoked directly via /cs:md-document. Reads the design-system config via config_loader.py and inlines the user's 12 derived CSS custom properties; refuses to render if onboarding hasn't run. Single-file output — Google Fonts + Prism.js CDN are the only externals; no framework runtime, no build step. Use after orchestrator routing or after design-system onboarding is confirmed.
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。
Chief Data Officer advisory for startups: AI training data rights and consent provenance, data product strategy (warehouse vs lakehouse vs mesh, build-vs-buy), B2B customer-data-as-asset valuation and M&A readiness, data team org evolution. Use when deciding whether to train models on customer data, choosing data architecture, valuing data for fundraising or M&A, sequencing data hires, or when user mentions CDO, chief data officer, data strategy, data mesh, lakehouse, training data, data product, data monetization, or customer data asset. NOT a tactical data engineering skill — strategic decisions only.
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。
Chief Customer Officer advisory for startups: retention decomposition (gross retention vs NRR honesty, churn root-cause taxonomy), customer segmentation strategy (differential investment across tiers + ICP fit scoring), CS team coverage model (pooled vs named CSM thresholds + ratio math), and CS team org evolution (CS vs Support vs AM distinctions). Use when designing retention strategy, segmenting customers for differential investment, sizing CS team, or sequencing CS hires. Strategic only — does not duplicate engineering/business-growth tactical skills.
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 Head of Ops, Knowledge Manager, or TPM-Internal needs to author, validate, or clean up company SOPs and internal runbooks (procurement intake, vendor offboarding, incident-comms cascade, employee onboarding) — including 5W2H completeness checks (Who-What-When-Where-Why-How-HowMuch), cross-link and orphan-page validation across a sprawling Notion/Confluence/Obsidian wiki, KB ingestion + hygiene reporting, and runbook step verification (named owner, expected duration, observable success signal, rollback path, escalation contact). Pairs Ishikawa's 5W2H method, Gawande's *The Checklist Manifesto*, ISO 9001, ITIL v4, and Google SRE Workbook runbook discipline with deterministic stdlib-only Python tools that score completeness, detect anti-patterns, and emit prioritized cleanup lists (e.g., "validate this runbook before it goes into rotation", "audit our Confluence wiki for stale and orphaned SOPs").
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。
Chief AI Officer advisory for startups: model build-vs-buy decisions (API vs fine-tune vs in-house), AI risk classification under EU AI Act + US state patchwork, AI cost economics (API-to-self-hosted breakeven), and AI team org evolution. Use when deciding whether to call an API or fine-tune, classifying AI use cases for regulatory risk, calculating when self-hosting pays off, sequencing AI hires, or when user mentions CAIO, AI strategy, model selection, foundation model, fine-tuning, EU AI Act, NIST AI RMF, AI governance, model risk, or AI economics. Strategic only — does not duplicate engineering AI/ML skills.
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 BizOps lead, COO, or process-improvement owner needs to document an end-to-end business process (procurement, employee onboarding, incident handoff, customer-onboarding, claims adjudication) in BPMN-style notation, measure cycle times by stage, surface where work spends most of its time waiting vs. being worked, and quantify the gap between processing time and total elapsed time. Pairs Lean / Six Sigma / Theory-of-Constraints canon with deterministic stdlib-only Python tools to produce a process map, a ranked bottleneck list (with severity + root-cause hypothesis), and a cycle-time analysis (P50, P90, value-add ratio, Little's-Law throughput). Distinct from sales-pipeline, system-reliability (SLO), and strategic-OKR work — this is tactical process documentation for internal operations.
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 when and why tests broke on main: bisect a Swift package suite on CI across old commits, read the failure matrix, and decide per test whether the test went stale or the code regressed. Use when a suite is red on main, when PR CI only ran filtered tests and the full suite has drifted, or when asked which PR broke a test.
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 only after the user explicitly asks for cmux Computer Use through the cmux-cua skill: drive real macOS apps from a cmux agent session via the bundled engine (accessibility tree + screenshots, click/type/scroll/drag, branded cursor), or explain its user-directed permission setup. Reading or discovering this skill is not consent.
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。