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概要と使いどころ
Deep game design health assessment across five dimensions: review core loop and progression systems, analyze in-game economy for inflation and sink/source balance, mathematically simulate combat balance and drop rates, audit analytics event coverage, and evaluate monetization ethics..
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Audit a codebase for product-market fit readiness -- evaluate startup PMF signals, core value loop tightness, feature scatter vs focus ratio, user activation funnel friction, retention hook infrastructure, pricing flexibility, analytics event coverage, CI/CD iteration speed, and growth-stage maturity for pre-seed through Series A products.
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 game design documents and implementations for core gameplay loop clarity and depth, XP leveling curves and unlock pacing, difficulty curve spikes and plateaus, skill tree viability, player motivation via Self-Determination Theory.
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Capture learnings from a campaign, launch, sprint, or initiative using Five Whys (Taiichi Ohno, Toyota Production System) for root cause analysis and Start-Stop-Continue for action planning. Append reusable insights to knowledge/learnings.md so future runs of the OS get smarter. Use when the user asks for a retro, retrospective, post-mortem, "what did we learn", "campaign wrap-up", "lessons learned", or after a campaign or launch ends. Critical feedback loop that makes the OS compound over time. For the numbers review itself, see kpi-review. For planning the next campaign, see campaign-brief.
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 marketing KPIs and produce an executive summary with insights, anomalies, and recommended actions using the Storytelling with Data framework (Cole Nussbaumer Knaflic). Every review starts with a Big Idea - one sentence capturing the insight, its implication, and why it matters. Use when the user asks for a KPI review, monthly metrics review, marketing dashboard review, "review last month's numbers", quarterly review, performance review, or wants to analyze marketing data. Reads kpis.md and any uploaded data files. For planning a campaign against these numbers, see campaign-brief. For the post-campaign learning loop, see retro. For setting the baseline itself, see brand-context.
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Master skill — set up the full agent harness for any repo so an agent can work it reliably: legible (map-not-manual docs + custom lints), executable (one-command dev stack), verifiable (e2e gate + a verify-before-ship loop), plus commit hygiene and entropy control. Use when onboarding a new/unfamiliar codebase to agent-driven development — "set up the harness", "make this repo agent-ready", "harness this codebase".
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。
Scaffold an isolated CLOUD dev box per agent (via crabbox + Daytona) for any codebase — the parallel-safe counterpart to dev-local-setup. Each agent gets its own full stack (own DB + dev server) and an in-box browser for e2e, so concurrent loops never collide on ports/state. Sets up the snapshot image, .crabbox.yaml, an idempotent setup.sh (also boots the stack locally), and a cbx.sh wrapper. Use when the user says "set up crabbox", "give each agent its own box", "add cloud testing", "make this repo testable in the cloud / on Daytona", "parallel-test this", or when setup-codebase-harness needs true per-agent isolation.
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。
FLOW framework integration: evidence-led SEO using the Find → Leverage → Optimize → Win loop. Surfaces stage-specific AI prompts from the FLOW knowledge base (41 prompts, CC BY 4.0). Use when user says "FLOW", "FLOW framework", "seo flow", "evidence-led SEO", "find leverage optimize win", or wants stage-specific SEO prompts.
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 the Fable-derived 10-stage OBSERVE, OBSERVE, LISTEN, THINK, CONNECT, CONNECT, FEEL, ACCEPT, CREATE, GROW loop to consequential or ambiguous reasoning and decisions. Use for think this through, deep think, architecture review, postmortem, tradeoff analysis, or challenges to assumptions. This is a reasoning review, not a deterministic vault health check or repair workflow.
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Run a bounded, source-grounded research loop, draft a cited dossier, and optionally propose a separately reviewed canonical vault merge. Use when the user wants autonomous or deep research that may access the public web. Triggers: /autoresearch, autoresearch, research this topic, deep dive into, investigate, find everything about, research and file, go research, build a wiki on.
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。
FLOW framework integration for bloggers. Evidence-led content workflow using the Find, Optimize, Win loop with stage-specific AI prompts from the FLOW knowledge base (30 blog-applicable prompts, adapted from FLOW under CC BY 4.0). Use when user says "FLOW framework", "blog flow", "/blog flow", "evidence-led blogging", or "find optimize win", or asks for stage-specific blog prompts. Not for generic prompt writing.
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 paid-ad landing pages for message match, mobile experience, performance, accessibility, trust, forms, consent, tracking, security, and conversion friction. Use for landing-page audit, post-click experience, LP audit, conversion-rate optimization, form optimization, ad-to-page message match, redirects, blocked navigation, or requests involving private, loopback, link-local, or metadata IP destinations.
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。
Plan, implement, audit, and review C#/.NET SwiftlyS2 plugins. Use when working with Commands, Core Events, Game Events, GameHooks Pre/Post, raw native hooks, Modules, Workers, Services, high-frequency runtime loops, NetMessages, Schema access, entity handling, thread safety, performance, or IPlayer lifecycle behavior.
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Diagnose Yuzu bugs and performance regressions with a disciplined reproduce-minimize-hypothesize-instrument-fix-regression-test loop. Use when the user says `/diagnose`, "debug this", reports a failure, flake, broken behavior, or performance regression.
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。
Diagnoses GKE workload failures (CrashLoopBackOff, OOMKilled, ImagePullBackOff, Pending, etc.) via logs and events. Use when pods fail to start or crash repeatedly. Don't use for GKE cluster infrastructure provisioning, node pool creation, or non-Kubernetes Google Cloud services.
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Configures alerting policies in Terraform for Google Kubernetes Engine (GKE) clusters, workloads, and services using PromQL and Google Cloud Managed Service for Prometheus. Use when writing, analyzing, validating, or deploying Terraform alerting policies to monitor GKE service latency, traffic, error rates using Multi-Window Multi-Burn-Rate SLO alerts, memory saturation, and cluster health such as CrashLoopBackOff and Node NotReady conditions. Don't use for non-GKE compute runtimes such as standalone Compute Engine VMs or standalone Cloud Run services without GKE.
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 and debug OAuth 2.0 DPoP (RFC 9449) refresh token sender-constraining for WebCrypto, Node.js ES6, and browser runtimes integrating with Google's OAuth platform. Use when configuring non-extractable asymmetric key pairs (P-256), generating DPoP Proof JWTs for authorization code exchange and token refresh, or handling 400 use_dpop_nonce challenge retry loops at oauth2.googleapis.com/token. Don't use for unconstrained OAuth 2.0 flows (where refresh tokens are not bound to a client key pair), or for Google Cloud IAM / service account authentication.
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。
Diagnoses GKE JobSet interruptions, restarts, and preemptions for AI/ML training workloads autonomously. Use when troubleshooting JobSet restart loops, spot VM preemptions, node readiness failures, host VM issues, or coordinator worker crashes. Don't use for general GKE cluster creation, basic workload deployment, or non-JobSet application issues.
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。
Close the loop on a Caveman learn report — review the ranked token sinks and apply cost-lowering fixes (trim config, offload recurring context to cavemem) with per-edit consent. Use when the user runs "caveman learn", asks to lower their agent's token cost, wants to trim a heavy CLAUDE.md, or wants to offload context they re-paste every session into cavemem.
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。
Designing or reviewing Agentforce actions: Flow actions, Apex invocable actions, prompt-template actions, action naming, input and output contracts, confirmation requirements, and safe error behavior. Triggers: 'agent actions', 'build an agent that can look up an order', 'make the agent able to check order status', 'give the agent a capability', 'agent that can retrieve a record', 'flow action for agent', 'agent invocable action', 'action schema design'. NOT for writing the Apex class itself — use agentforce/custom-agent-actions-apex. NOT for the reason_code error envelope that stops an agent retry loop — use agentforce/agent-action-error-handling.
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Use when designing how an Agentforce agent transfers the conversation to a human agent (Omni-Channel), to another bot/agent, or to an alternate workflow — including context package, deflection, escalation triggers, and user messaging. Triggers: 'agent to human handoff', 'agentforce escalate to omni channel', 'agent to agent handoff', 'transfer conversation with context', 'agent deflection fallback'. NOT for topic scope and selector design — use agentforce/agent-topic-design. NOT for the overall Einstein Bot plus Agentforce estate topology — use architect/conversational-ai-architecture.
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。
Configuring Salesforce Email-to-Case: Standard vs On-Demand mode selection, routing address setup, email threading via Lightning tokens, auto-response rules, attachment limits, and per-address case field defaults. Trigger keywords: email-to-case, routing address, on-demand email-to-case, email threading, case from email, email agent, routing address setup, Case.settings-meta.xml, EmailToCaseRoutingAddress, emailServicesAddress, isVerified, caseOrigin, enableThreadTokenInBody, useEmailHeadersForThreading, overEmailLimitAction, unauthorizedSenderAction, enableE2CAttachmentAsFile, saveEmailHeaders, newEntityRecordType, casePriority, EmailMessage.EmailRoutingAddressId, auto-response loop. NOT for the wider case layer (queues, escalation rules, case teams, entitlements) - use admin/case-management-setup. NOT for routing case work items to agents after creation - use admin/omni-channel-routing-setup. NOT for the email templates or letterheads themselves - use admin/email-templates-and-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。
Use this skill when defining AI governance policies, designing human oversight workflows, documenting bias mitigation strategies, or meeting regulatory transparency requirements for Salesforce AI features. Trigger keywords: responsible AI, AI bias, AI audit trail, AI transparency, human-in-the-loop, AI risk inventory, AI disclosure. NOT for the MLOps pipeline or Policy-as-Code engine architecture — use architect/ai-governance-architecture. NOT for Trust Layer technical configuration — use agentforce/einstein-trust-layer.
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 Growth Hacker — north star metric, AARRR funnel, viral loop, experimentation framework, retention hook สำหรับ startup และ e-commerce ไทย
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。