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概要と使いどころ
/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。
/cs:cdo-review <plan> — Decision-driven Chief Data Officer interrogation of any plan that touches training data, data architecture, data productization, or data team hiring. Use when validating training-data rights before model work, choosing warehouse vs lakehouse vs mesh, or valuing data assets for productization or M&A.
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。
Phase 1 of building a Claude Managed Agent — interview the founder about the one job the agent should do, then produce a build sheet (CMA primitives table + v1/v2 deferrals + eval plan) WITHOUT needing their API key yet. Use when the user says "help me scope an agent", "I have an idea for an agent", "what should this agent be", or when the orchestrator routes phase=interview. Drives the six intake slots (job, trigger, inputs, actions, definition-of-done, recurrence) via AskUserQuestion, maps them to primitives with interview_planner.py, assembles build-sheet.json with build_sheet_builder.py, and validates limits with primitives_validator.py. Connectors are mockable in v0 (schema-true custom tools); real MCP servers become v1 deferrals. Distinct from stage-launch (which turns the sheet into payloads).
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。
Phase 4 of building a Claude Managed Agent — make it run without you. Turn a graded agent into a recurring scheduled deployment (POSIX-cron), an event-driven curl trigger, or confirmed on-demand use, then finalize the versioned roadmap. Use when the user says "run it every morning", "put it on a schedule", "nightly", "weekly", "automate this", "make it recurring", or when the orchestrator routes phase=run-without-you. deployment_builder.py builds the POST /v1/deployments payload (initial_events must include user.message; optionally nests a user.define_outcome so each firing self-grades); cron_validator.py validates the 5-field cron + IANA timezone and prints the wall-clock DST note; next_directions_writer.py writes NEXT-DIRECTIONS.md. No tool makes API calls — the deployment is created via BYOK curl. Distinct from grade-iterate (the in-session loop) and wrap-up (closeout).
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。
Orchestrate the narrative team — narrative-director, writer, world-builder, level-designer — for story, world lore, narrative-driven levels.
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。
Hands-off, diff-scoped browser QA of the active branch: maps user flows, drives a real browser, autonomously fixes small breakages with regression tests and commits, judges experience against product personas, and writes a durable dogfood report. Manual invocation 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。
Review a PR against the Pascal architectural rules — package boundaries (core/viewer/editor/nodes), the registry-driven composition model (def.geometry / def.renderer / def.system), legacy-dispatch regressions, the slots + world-scale-UV convention for new nodes/geometry, hook hygiene (useEditor/useScene/useViewer), and selector performance. Use when the user asks to review a PR, audit a branch, or check that changes respect the codebase's 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。
Cut an OpenWork release, release the app, publish a new version, rerun or recover a release tag, verify release assets. Tag-driven GitHub Actions release that makes zero commits to the 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。
Drive Archon through its CLI: run AI workflows on a repo, manage those runs (inspect, approve, reject, cancel, resume), set up Archon or change its config, author new workflows, and improve workflow prompts. Use when the user says "use archon", "run archon", "archon workflow", "fix issue #N with archon", "have archon review this PR", "what's running / check run <id>", "approve/reject/cancel/resume that run", "set up archon", "configure archon", "change my archon config", "create a workflow", "author a workflow", or asks how to write a better Archon prompt. NOT for: doing the coding work yourself — Archon delegates it to isolated runs.
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。
Discovers requirements and designs a borderless open data lakehouse using Lakehouse for Apache Iceberg and BigQuery data agents. Use when architecting multi-cloud storage infrastructure (Cloud Storage, AWS S3, Azure Blob), establishing ingestion and AI serving subsystems, configuring Cross-Cloud Interconnect, or deploying Gemini Enterprise Agent Platform and BigQuery data agents. Don't use for single-cloud data warehouses, or when the focus is on Knowledge Catalog metadata governance and Spark-driven IDE analytics workflows (use google-cloud-solution-agentic-analytics-spark-knowledge-catalog instead).
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。
Mounts Cloud Storage buckets on Google Cloud as a POSIX file system using Cloud Storage FUSE (and the gcsfuse CLI tool). Use when interacting with the gcsfuse CLI or Cloud Storage FUSE mounts: decide whether FUSE, native gs:// reads, or Filestore/Managed Lustre fits a workload, deploy tuned mounts on GKE, Compute Engine, or Cloud Run, enable and size file, stat, and list caches, tune mount flags (--implicit-dirs) or config-file settings, apply workload profiles, keep ML checkpointing safe (rename atomicity, hierarchical namespace/HNS, close-time finalization, concurrent writers), or diagnose slow training, low throughput, or bill spikes with gcsfuse metrics. Covers mount semantics, gcsfuse CLI and config files, GKE gcsfuse CSI driver (Workload Identity principal:// bindings, profile StorageClasses, sidecar sizing), and Cloud Run volume mounts. Don't use for bucket administration or data management without a mount (google-cloud-storage-basics) or fully POSIX-compliant shared file systems (Filestore, Managed Lustre; use gke-storage).
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 guidance for deep security incident and entity investigations in Google SecOps. Use when investigating cases, analyzing entities (hosts, IPs, domains, hashes, users), extracting and searching UDM events, performing asset and user timeline analysis, and detecting lateral movement across enterprise networks. Don't use for detection rule authoring or YARA-L tuning (use secops-detection-engineering), proactive hypothesis-driven hunting (use secops-hunt), initial alert triage (use secops-triage), or basic case status updates (use secops-cases).
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 natural language questions and perform analysis on GKE cluster and workload costs using BigQuery billing exports, cost allocation data, and live cluster monitoring metrics. Use when querying GKE costs across projects, namespaces, or workloads, analyzing billing reports in BigQuery (`bq`), checking cluster cost budgets (`gcloud billing`), or diagnosing cost drivers like pod requests vs. actual utilization (`kubectl top`). Don't use for applying cost optimization changes, creating rightsizing manifests (VPA/MPA), or selecting ComputeClasses (use gke-cost-optimization instead).
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。
Leverages BigQuery's built-in machine learning and GenAI capabilities for advanced data analytics. Use when you need to write SQL queries that perform time-series forecasting, predict values, detect outliers or anomalies, find key drivers, perform semantic search or vector search, classify text, calculate similarity, summarize content, translate language, evaluate models, filter by semantic conditions, measure the causal effect of an intervention, compute correlations between columns, detect change points or structural breaks, extract trend or seasonality components, or leverage generative AI capabilities in BigQuery. Do not use for general BigQuery dataset, table, or job management requests.
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。
Weekly NPS/CSAT theme analysis for {{survey_sheet}}. Reads survey responses from Google Sheets, clusters them into themes with sentiment and detractor drivers, tracks score movement week over week, and posts a summary with representative quotes to {{report_channel}}. Read-only on the sheet and report-only — no writes to any survey system, no respondent contact.
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。
Drive OpenAI's Codex CLI (`codex exec`) as a non-interactive coding sub-agent from inside Claude Code. Load WHENEVER you want to delegate a coding/analysis/refactor task to Codex, get a second opinion / adversarial review from another model, fan out parallel agents across files or worktrees, or run a long mechanical job while you stay the planner. Covers the exact `codex exec` flags, sandbox tiers, output capture, JSON/schema modes, session resume, parallel fan-out, and the mandatory "delegate → capture → independently verify, never trust the self-report" supervision loop.
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。
Employee offboarding runbook for {{projectName}}. Detects newly marked departures via the {{hris_group}} group in Okta, then works one independent case per departure across Okta, Google Workspace, Google Drive, and GitHub — revoking access, transferring ownership, and reclaiming licenses — holding the ownership transfer for a human approval gate (never deleting an account) and posting the completed checklist to {{notify_channel}}.
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。
Drive Anthropic's Claude Code CLI (`claude -p`) as a non-interactive coding sub-agent from inside Codex. Use when you want to delegate a coding/analysis/refactor task to Claude, get a second opinion / adversarial review from another model, hand off long-context planning, or fan out parallel agents. Covers the exact `claude -p` flags, permission modes, output capture, JSON/stream-json modes, session resume, parallel fan-out, and the mandatory "delegate → capture → independently verify, never trust the self-report" supervision loop.
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。
Fresh-session RFP and proposal-questionnaire response for {{projectName}}. Parses a new inbound RFP from {{rfp_label}} in Gmail and its attached document in Google Drive, matches each question to our vetted past-answers library and product docs, drafts the full response in a new Google Doc inside {{drafts_folder}}, flags anything it can't answer confidently, and leaves an unsent Gmail draft linking it — holding the completed response for a human to review and submit.
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。
How to reach a user's CONNECTED COMPUTER (their laptop, desktop, or server) from a Kortix session — read/write files, run shell commands, and drive the desktop (click/type/screenshot) on that machine. A connected computer is an ACCOUNT of the project's `computer` connector, called through the same `kortix connectors` path as every other connector, with `--account` picking the machine. Load this when the task is about acting ON a specific physical computer the user has connected ("on my laptop…", "read ~/Downloads on my machine", "run this on my desktop", "click the button on my screen"), or when the user asks how the agent reaches their computer. ALSO load it when the user mentions their own machine or its files and no computer may be connected yet ("my local files", "my Desktop", a path like `~/…`, `/Users/…`, "the repo on my laptop") — this sandbox cannot see their disk, and the skill says how to tell them to connect it. For files INSIDE this sandbox, use normal shell/fs — not this.
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。
The per-call runbook for turning a finished sales call's transcript — found in the connected Google Drive folder — into an updated HubSpot deal (or a flagged handoff when no deal matches), filed follow-up tasks in {{linear_team}}, and a drafted recap email — held for the rep's approval before it sends.
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。
Drive Kortix itself from the terminal with the `kortix` CLI — preinstalled and pre-authenticated in every session sandbox. Use whenever a task means acting on THIS project's Kortix control plane rather than just editing files: manage secrets, list/spawn/watch/talk-to sessions, open or inspect change requests to land work on main, fire or manage triggers, set a reminder to check back on this task later (`kortix remind "…" --in 24h --every 1h`, when the project's `reminders` feature flag is on), call connectors, connect Slack, or read project info. This is a discovery stub — the full, always-current reference is served live via `kortix skills get kortix-system` and its reference files.
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。
Daily reusable-session runbook for cloud-spend anomaly detection on AWS. Maintains a per-service/per-account spend baseline from Cost Explorer, flags spend that breaks out of that baseline, attributes the likely driver (a new resource, a traffic surge, a region), and alerts {{alert_channel}} with the delta. Read-only and alert-only — never modifies or deletes a resource or changes a budget.
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。