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概要と使いどころ
Cross-functional organizational health check combining signals from all C-suite roles. Scores 8 dimensions on a traffic-light scale with drill-down recommendations. Use when assessing overall company health, preparing for board reviews, identifying at-risk functions, or when user mentions org health, health check, or health dashboard.
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/cs:cpo-review <plan> — JTBD-driven interrogation of product roadmap, PMF signal, and portfolio focus. Use when committing a quarter's roadmap, deciding whether to kill a feature, or claiming PMF without a retention curve.
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。
Build, review, refactor, or architect ASP.NET Core web applications using current official guidance for .NET web development. Use when working on Blazor Web Apps, Razor Pages, MVC, Minimal APIs, controller-based Web APIs, SignalR, gRPC, middleware, dependency injection, configuration, authentication, authorization, testing, performance, deployment, or ASP.NET Core 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。
Summarize a customer interview transcript into a structured template with JTBD, satisfaction signals, and action items. Use when processing interview recordings or transcripts, synthesizing discovery interviews, or creating interview summaries.
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。
Generate time-windowed product pulse reports from configured 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。
Find link prospects, discover contact paths, and draft outreach from SERPs and backlink 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。
Configures best-practice, high-signal alerting policies for Cloud Run resources on Google Cloud (services, jobs, and worker pools) based on seasoned SRE practices. Use when analyzing, recommending, writing, or deploying Terraform PromQL alerting policies to monitor Cloud Run error rates (4xx/5xx), request latency, container instance saturation (warning/critical), container CPU/memory utilization and allocation, billable instance time, job execution status, and worker pool queue backlog. Don't use for GKE workloads (use gke-alert-configuration) or Compute Engine VMs.
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。
Runbook for turning a HubSpot renewal date into a prepped packet and a drafted outreach email for {{projectName}}. Covers the 90/60/30-day windows, what belongs in a renewal packet, at-risk signals, Google Calendar cross-checking, and the human approval gate before anything sends or discounts.
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。
Scoring rules for daily churn-risk detection — usage decline, support friction, billing/payment risk, and renewal proximity — plus how to combine them into a ranked at-risk list with a reason and a suggested next step per account.
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 curated cybersecurity market and company intelligence. USE WHEN security market data OR cybersecurity funding OR cyber M&A OR security-company/investor research OR market maps OR deal activity OR Return on Security OR The Signal. NOT FOR vulnerability research, security news, or investment advice.
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 improve content for AI Overviews and answer engines, including citability, entity clarity, crawler access, brand signals, and passage structure.
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 a site's backlink profile, anchors, toxic signals, competitors, gaps, and disavow candidates. Use only when links or referring domains are the requested focus.
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 ecommerce SEO across product pages, product schema, Shopping visibility, marketplace signals, and keyword gaps. Use only for stores, catalogs, or product listings.
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。
Expert knowledge for AI forecasting — superforecasting principles, signal taxonomy, confidence calibration, reasoning chains, and accuracy tracking
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。
Codex-only writing and layout overlay for AI Berkshire investment research reports. Use whenever Codex creates, rewrites, revises, or critiques company/industry/fund research reports, especially long-form Markdown reports that need financial rigor, readable business mechanics, contrarian analysis, valuation-to-action guidance, investor-specific recommendations, restrained typography, and clear buy/hold/sell signals. Do not use this to modify Claude Code slash-command 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。
First-pass triage of a possible production incident for Superset's hosted services. Gathers signals (recent deploys, Sentry spikes, health checks), proposes a severity and a status.superset.sh message, then stops for human approval. Use when someone reports an outage, errors spike, or asks "is something down?".
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。
Writes a new Scenic scenario (.scenic) from a natural-language description — "a car cuts in from the right lane", "a pedestrian crosses in front of the ego", "negotiate an unsignalized four-way" — by turning the request into a spec, generating the file from idioms proven on this build, and validating it by compiling and sampling before any simulator is involved. Use when the user asks to "write a Scenic scenario", "create a .scenic file", "turn this description into Scenic", "generate a scenario from text", or wants a Scenic scenario authored or repaired.
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。
Runs a single ScenarioRunner Python scenario (or a whole group) against a running CARLA server — FollowLeadingVehicle, ControlLoss, DynamicObjectCrossing, SignalizedJunctionLeftTurn and the rest of srunner/examples — in sync or async mode, with result output, recording and manual/agent control of the ego. Lists what is runnable on the checked-out branch and on the loaded map. Use when the user asks to "run a scenario", "test FollowLeadingVehicle", "list scenarios", "run a CARLA traffic scenario", or reports a scenario that is "not supported".
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。
Generates Angular 17+ standalone components, configures advanced routing with lazy loading and guards, implements NgRx state management, applies RxJS patterns, and optimizes bundle performance. Use when building Angular 17+ applications with standalone components or signals, setting up NgRx stores, establishing RxJS reactive patterns, performance tuning, or writing Angular tests for enterprise apps.
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 building C# applications with .NET 8+, ASP.NET Core APIs, or Blazor web apps. Builds REST APIs using minimal or controller-based routing, configures database access with Entity Framework Core, implements async patterns and cancellation, structures applications with CQRS via MediatR, and scaffolds Blazor components with state management. Invoke for C#, .NET, ASP.NET Core, Blazor, Entity Framework, EF Core, Minimal API, MAUI, SignalR.
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。
Scan recent merges to main (and noteworthy direct commits) since a given window (last tag or date in state). Extract titles, labels, types, linked issues, and signals for breaking changes or security. Produces structured input for a release notes drafter. Use in changelog-drafter loops.
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。
Scan merged PRs and commits since a given reference, extract titles, labels, types, and signals. Produces structured input for release notes drafting.
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。
Findings-driven targeted code review. Understands the patch via patch-explainer and surrounding code via codebase-analysis, then consults a catalog of bug patterns (~11 categories under references/categories/). Picks only categories whose diff-signals match the patch and items whose "look for" hints match actual code shapes, groups them by review focus (function/file/feature), and spawns parallel subagents — each with a tight scope and selective checklist. Use for medium-to-large patches (50-1000 LOC) as a focused alternative to whole-patch ensemble or whole-file deep review. Triggers on: "review this patch/diff/change", "find bugs in this change", "scoped review", "what could go wrong here", "review using findings", or proactive PR 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。