「cis」の検索結果

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.

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Systematic competitor tracking that feeds CMO positioning, CRO battlecards, and CPO roadmap decisions. Use when analyzing competitors, building sales battlecards, tracking market moves, positioning against alternatives, or when user mentions competitive intelligence, competitive analysis, competitor research, battlecards, win/loss, or market positioning.

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。

Repository-grounded threat modeling that enumerates trust boundaries, assets, attacker capabilities, abuse paths, and mitigations, and writes a concise Markdown threat model. Trigger only when the user explicitly asks to threat model a codebase or path, enumerate threats/abuse paths, or perform AppSec threat modeling. Do not trigger for general architecture summaries, code review, or non-security design 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。

Help the user define a concrete, measurable goal before starting work, especially when they ask to use the goal tool, create a goal, set an objective, clarify success criteria, or turn a fuzzy intention into a quantitative outcome. Use this skill for goal creation and goal refinement only; it does not manage durable snapshots, decision logs, or long-running execution artifacts.

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。

Prioritize a backlog of feature ideas based on impact, effort, risk, and strategic alignment with top 5 recommendations. Use when prioritizing a feature backlog, making scope decisions, or ranking product ideas.

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。

Create refined user personas from research data — 3 personas with JTBD, pains, gains, and unexpected insights. Use when building personas from survey data, creating user profiles from research, or segmenting users for product decisions.

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 and prioritize a list of feature requests by theme, strategic alignment, impact, effort, and risk. Use when reviewing customer feature requests, triaging a backlog, or making prioritization decisions.

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 meeting transcript into structured notes with date, participants, topic, key decisions, summary points, and action items. Use when processing meeting recordings, creating meeting notes, writing meeting minutes, or recapping discussions.

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。

Create a lean, decision-ready Product Requirements Document with an 8-section template: summary, contacts, background, objective, market segments, value propositions (6-part JTBD template), solution, and release. Lean by default (about one to two pages), with a fuller spec on request. Use when writing a PRD, documenting product requirements, preparing a feature spec, or reviewing an existing PRD.

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。

Create professional, dark-themed SVG diagrams of any type — architecture diagrams, flowcharts, sequence diagrams, structural diagrams, mind maps, timelines, illustrative/conceptual diagrams, and more. Use this skill whenever the user asks for any kind of technical or conceptual diagram, visualization of a system, process flow, data flow, component relationship, network topology, decision tree, org chart, state machine, or any visual representation of structure/logic/process. Also trigger when the user says "画个图" "画一个架构图" "diagram" "flowchart" "sequence diagram" "draw me a ..." or uploads content and asks to visualize it. Output is always a standalone .svg file.

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。

Fast, concise sprint snapshot — burndown and emerging risks for situational awareness. 'How is the sprint going?'

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。

A GDD changed — scan ADRs and the traceability index for now-stale architectural decisions. Impact report, guides resolution.

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。

Author the architecture blueprint before code is written. Validates decisions against the pinned engine, flags knowledge gaps.

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。

Write or extend an E2E journey spec in evals/specs that proves a PR's change to a human reviewer. Use when a PR changes user-visible behaviour, when a reviewer asks "show me", or when the coverage decision says a journey is missing.

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 writing Playwright visual regression tests or adding a demo route for one - covers the decision rule (what earns a capture, and that a visual spec is in addition to a Playwright assertion), the custom tablePage fixture, the screenshot workflow, reg-suit comparison, and the demo routes in examples/next/visual-tests/

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 writing or modifying real-browser Playwright E2E / functional tests for Handsontable (specs in tests/e2e/, core, wrappers, or walkontable). Covers the Page Object Model, hooking in by data-testid, deterministic web-first waits, wrapper-specific gotchas, recording via the CLI, and the new-vs-modify / E2E-vs-unit decision. NOT for screenshot/visual tests (use visual-testing) or the legacy Jasmine suite (use handsontable-e2e-testing).

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 changed or staged code, a branch, or a PR in the Handsontable monorepo across architecture, code quality, performance, accessibility, and tests. Covers SOLID / Law of Demeter / plugin decoupling / breaking-changes policy, custom ESLint rules / JSDoc / naming / cognitive complexity, large-array and render-batching performance, WCAG 2.1 AA + keyboard navigation, and the tests a change ships (a named exercising test per new path, scoped mutation, a ticket on every weakened assertion). Trigger when asked to review changes, check a diff against Handsontable conventions, assess architectural correctness, spot performance regressions, verify accessibility, or judge whether a change's tests prove it, and as the design lens before or while implementing any core change.

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 a past decision, document, or conversation in Screenpipe with source links.

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。

Google platform decision and setup guidance, loaded on demand from Google's skill catalog. Use when a developer is choosing or setting up part of their stack, such as where to run a service, a database, storage, messaging, authentication, analytics, ads, or AI model serving, and a Google product is a reasonable candidate - whether or not a vendor is named - or when a request names a Google product or API. Brings in the matching Google skill so the answer can weigh Google options, their trade-offs, and when they are not the right fit. Skip when the stack is already settled on another provider and no Google product is named, or the task involves no platform choice.

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 nodes reporting NotReady or Unknown status by inspecting node conditions, events, kubelet/containerd logs, and node metrics, then proposing safe remediations. Use when nodes show NotReady, when the kubelet stops posting node status, or when workloads are evicted or stuck Pending due to node health. Don't use for pod-level application failures (use gke-workload-troubleshooting), autoscaler scale-up/scale-down decisions (use gke-cluster-autoscaler), or non-GKE compute.

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 Apache ShardingSphere or user-authorized downstream pull requests and PR discussions from public or authorized repository evidence. Use for code-correctness or mergeability decisions, CI-focused review, root-cause and regression analysis, complete consolidated findings, copy-ready committer feedback, challenged findings, multi-round review, and formal review of local implementation candidates in the repository completion 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。

Daily interview-scheduling sweep for {{projectName}}. Reads {{greenhouse_pipeline}} for candidates ready to schedule and each one's interview plan, checks {{interviewer_calendars}} for panel availability, and drafts a candidate slot proposal plus a calendar invite per interviewer to {{coordinator_review_inbox}}. Never sends a proposal or invite and never makes a hiring decision; a coordinator confirms and sends everything.

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。

Persistent-session, per-sweep QA runbook for {{target_repo}}. Discovers PRs since the last check, then for each one checks out the branch, runs the full verification suite, deploys the change to the test environment, exercises it through the edge, and posts a pass/fail result as a GitHub check and comment. Test environment only — no production access, no merge.

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。

Scores an inbound application against the role's written rubric with supporting quotes as evidence, writes a structured screen back to {{applications_source}}, and proposes interview slots on {{hiring_manager_calendar}} for strong matches. Never advances or rejects a candidate — every decision stays with the hiring manager.

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。