「cis」の検索結果

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/

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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).

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

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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.

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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.

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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.

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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.

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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.

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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.

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P10 CTO mode — define strategic direction, design org topology, manage P9 teams. Use when user says 'CTO模式', 'P10', '战略规划', '架构委员会', or when facing cross-team architectural decisions. Produces: strategic input templates + org design.

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Owns the Ideal State Artifact — the primitive holding a project or task's articulated ideal state; scaffolds, interviews, grills a half-formed idea into shape via checkpointed discovery, scores completeness, reconciles feature excerpts to master, seeds from a repo, and appends decisions/changelog/verification across a locked seventeen-section order. USE WHEN ISA, ISC, ideal state, ideal state criteria, project specification, hill-climb, articulating done, fog, not yet specified, grill me, discovery interview. NOT FOR creating new skills (use CreateSkill).

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Curated aphorism collection with CRUD — content-based matching, themed search, thinker research, DB maintenance. Quotes organized by author/theme/context/usage to prevent repetition. Four workflows: FindAphorism, AddAphorism, ResearchThinker, SearchAphorisms. Themes: Stoicism, Wisdom, Truth-seeking, Excellence, Resilience, Curiosity. USE WHEN aphorism, quote, find a quote, research thinker, add aphorism, quote for newsletter, what did X say about, quote bank. NOT FOR creative writing or social posts.

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This skill should be used for the tool-interface layer of an agent system specifically: writing tool descriptions agents can route on, designing tool schemas and response formats, naming conventions, actionable error recovery messages, MCP server design, tool-set consolidation, and deciding when to add or remove an individual tool. Use this when the unit of work is a single tool or a set of tools. Route project-shape, pipeline architecture, and task-model-fit decisions to project-development; route deciding whether to introduce sub-agents to multi-agent-patterns.

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This skill should be used when long-running agent sessions need context compression, structured summarization, compaction, token-per-task optimization, or durable handoff summaries that preserve decisions, files, risks, and next actions.

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This skill should be used for project-level decisions about LLM-powered systems: whether an LLM is the right primitive for the task at hand, the shape of a multi-stage batch or agent pipeline, token and cost estimation, choosing between single-agent and multi-agent at the project level, structured output design for downstream parsing, and structuring agent-assisted iteration. Use this when the unit of work is a whole project or a multi-stage pipeline. Route individual tool design to tool-design and individual skill-loading or context-budget tactics to context-optimization.

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This skill should be used when designing multi-agent systems that need context isolation, supervisor or swarm coordination, explicit handoffs, parallel execution, or a decision on whether multiple agents are justified.

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The normative Design Guides for GPUI Kit desktop applications. Read in full before designing or changing any screen, layout, spacing, visual hierarchy, color, density, component choice, interaction state, overlay, motion, data-heavy view, or interface copy in a GPUI Kit (gpui-kit / gpui-component) application, and before reviewing UI work for design quality. Also use when asked what the design rules are, or whether a UI decision follows them.

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How to build desktop applications with GPUI Kit, the Rust framework published as the gpui-kit crate (GPUI plus gpui_kit::component, gpui_kit::base, gpui_kit::assets). Use when setting up a gpui-kit app, choosing or using a component (Button, Input, Select, Dialog, Sheet, Tabs, Sidebar, List, DataTable, Tree, Chart, etc.), handling component state, theming, or window overlays, and for GPUI mechanics: actions and keybindings, async tasks, contexts, custom elements, entities, events, focus, global state, layout and styling, ElementId, and tests including UI integration testing. Holds the normative Coding Guides: read them before any architecture, state-ownership, public API, naming, or testing decision. Pairs with the gpui-kit-design-guides skill for the Design Guides.

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Build a working artifact from a plain "implement X for me" request: a running end-to-end spine first, then one feature per rung, with every under-determined decision written to an assumption ledger BEFORE the code that depends on it and a sweep for the ones that slipped through undeclared (same-family provisional in the base Codex mirror). Use when user says "给我实现", "implement X", "帮我做一个能跑的", "先搭个原型再加功能", "build this feature", "prototype then extend", or hands over a capability description rather than an experiment plan.

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Build a working artifact from a plain "implement X for me" request: a running end-to-end spine first, then one feature per rung, with every under-determined decision written to an assumption ledger BEFORE the code that depends on it and a cross-model sweep for the ones that slipped through undeclared. Use when user says "给我实现", "implement X", "帮我做一个能跑的", "先搭个原型再加功能", "build this feature", "prototype then extend", or hands over a capability description rather than an experiment plan.

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Generate deterministic publication-quality architecture, workflow, and pipeline diagrams from structured JSON (FigureSpec) into editable SVG. Use when user says "架构图", "workflow 图", "pipeline 图", "确定性矢量图", "figure spec", "draw architecture", or needs precise, editable, publication-ready vector diagrams. Preferred over AI illustration for formal architecture/workflow figures.

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Ambient Memori long-term memory for Claude Code via local Bash. MUST TRIGGER on essentially every non-trivial user turn: run recall before drafting any substantive response, whether or not the user mentioned memory, a prior session, or past work. The default is to check memory first; do not wait to be asked. Always fire before external lookups (WebSearch/WebFetch) and before answering any question about preferences, prior decisions, constraints, project history, status, past work, or any non-trivial coding/research task. Skip only for trivial acknowledgements/closings, purely self-contained turns where prior context cannot possibly help, or when the user explicitly opts out. Use Advanced Augmentation after drafting the final response for every non-trivial turn, as the last memory step. Use recall.summary only for broad session summaries/orientation, compaction after context loss, feedback for memory quality, quota for limits, and signup only when explicitly requested.

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Agent-native memory for OpenClaw that structures memory from agent trace, execution history, decisions, tool calls, and conversations into durable long-term memory primitives.

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Create a concise plan. Use when a user explicitly asks for a plan related to a coding task.

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。