「default」の検索結果

[Process] Use when the developer wants something explained or taught, or wants to know HOW TO REVIEW a change — by default the current working tasks + changes in context, or whatever the prompt names (a plan, a subsystem, a decision, a concept, a bug). AI derives WHAT to explain from the prompt and ALWAYS delivers a teacher-and-coach review guide, high level first then detail: what was done, a VISUAL MAP (system flowchart, domain ERD, sequence diagrams), the user stories and business rules with their REAL test-case IDs, a REVIEW PATH naming which files to open first and in what order with an exit criterion per stage, the technical concepts needed to follow it, how it works, why THIS solution, every alternative option with pros/cons, the trade-offs accepted, the blast radius, HOW TO TEST AND DEMO the change, the developer's decision levers, and written challenge prompts that provoke the reader to pressure-test the work. Works at ANY scope — from one line of code to the whole project: it SIZES the target into a scope tier, DECOMPOSES a large one into understanding groups, BREAKS the work into tasks before the first deep read, and ACCUMULATES the report group by group on disk so a huge explanation stays complete instead of thinning out. Writes the full report to a single combined markdown file (plans/reports/understand-*.md) that opens with a detailed summary section — or delivers it in full in chat when Step 3 finds no git-ignored directory for it — and always summarizes it in chat. Regardless of coding level. Never interrogates, never quizzes, never blocks.

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[Testing] Use when you need to drive an integration-test suite to fully green — each round runs /integration-test-verify (whole system by default, or the target named in the prompt), and on ANY failure combines /debug-investigate + /integration-test-review (report-only) to adjudicate the fault (test wrong · test not optimal · source wrong), then /fix to resolve it at the owning layer, then /changes-review on that round's fix diff, then re-runs a FRESH full verify — looping until the whole suite passes its 2-consecutive-green-runs gate with zero failures.

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[Code Intelligence] Use when you need to export the code review knowledge graph. Flag: --format={json|mermaid} (default json); --format=json dumps the full graph to a JSON file, --format=mermaid renders a single file as a Mermaid flowchart diagram.

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[Code Intelligence] Use when you need to build, update, or sync the code review knowledge graph. Flag: --scope={full|update|sync} (default auto-detect); --scope=full forces a full rebuild, --scope=update re-parses uncommitted working-tree changes, --scope=sync syncs committed git changes then updates the working tree.

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[Implementation] Use when you need to start coding & testing an existing plan. Flags: --approval=off (auto/trust mode, no approval gate), --tests=off (skip the test step), --parallel={auto|on|off} (default off — sequential; --parallel/=on opts in to parallel sub-agent waves; =auto fans out only when the plan declares PAR/SEQ tags and write sets).

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[Planning] Use when you need intelligent plan creation with prompt enhancement. Flag: --mode={ci|cro} (default none — standard planning); --mode=ci plans a fix from a GitHub Actions CI run/log, --mode=cro plans conversion-rate optimization (25-item CRO framework).

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[Skill Management] Use when enhancing, compressing, or expanding prompts, docs, or skills with attention anchoring [INTELLIGENT ROUTING]. Flag: --op={compress|expand|enhance} (default enhance); --op=compress strips token bloat, --op=expand reconstructs compressed text.

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Use when adding or reviewing keyboard shortcuts in a macOS app — in-app vs global, RegisterEventHotKey vs the KeyboardShortcuts package vs NSEvent monitors, user-configurable recording, conflict handling, and which defaults won't collide with the system.

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Create an aggregated Epic PR from a `cc-epic-<Issue number>` branch to the default branch. Takes the Epic Issue number as argument, assumes sub-PRs have already been merged into the epic branch, and automatically generates the PR title and description from the commit log against the base branch (including sub-PR / sub-Issue references). The PR is posted via `gh pr create` with no label and `Closes #<Epic Issue>`.

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Discover ordinary public webpages with LLM-optimized Tavily search results. Use when no specific URL is known and the user needs a quick public-web lookup, articles, recent news, sources, or domain-filtered results. This is the default Tavily leaf for simple public-web discovery. Do not use when the user already supplied the target URL (use tavily-extract), wants URLs under one known site (tavily-map), wants many pages' content (tavily-crawl), needs a full cited synthesis (tavily-research), or needs named platforms, login state, platform-native content, or cross-platform collection (Agent Reach).

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Forces the laziest solution that actually works, simplest, shortest, most minimal. Channels a senior dev who has seen everything: question whether the task needs to exist at all (YAGNI), reach for the standard library before custom code, native platform features before dependencies, one line before fifty. Supports intensity levels: lite, full (default), ultra. Use on ANY coding task: writing, adding, refactoring, fixing, reviewing, or designing code, and choosing libraries or dependencies. Also use whenever the user says "ponytail", "be lazy", "lazy mode", "simplest solution", "minimal solution", "yagni", "do less", or "shortest path", or complains about over-engineering, bloat, boilerplate, or unnecessary dependencies. Do NOT use for non-coding requests (general knowledge, prose, translation, summaries, recipes).

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AI image generation with OpenAI GPT Image 2, Azure OpenAI, Google, OpenRouter, DashScope, Z.AI GLM-Image, MiniMax, Jimeng, Seedream, Replicate and Agnes APIs. Supports text-to-image, reference images, aspect ratios, and batch generation from saved prompt files. Sequential by default; use batch parallel generation when the user already has multiple prompts or wants stable multi-image throughput. Use when user asks to generate, create, or draw images.

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Posts content to WeChat Official Account (微信公众号) via API or Chrome CDP. Supports article posting (文章) with HTML, markdown, or plain text input, and image-text posting (贴图, formerly 图文) with multiple images. Markdown article workflows default to converting ordinary external links into bottom citations for WeChat-friendly output. Use when user mentions "发布公众号", "post to wechat", "微信公众号", or "贴图/图文/文章".

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All animation knowledge for HyperFrames — atomic motion rules, multi-phase scene blueprints, scene transitions, broader motion-design techniques, AND the seven runtime adapters (GSAP default, plus Lottie, Three.js, Anime.js, CSS keyframes, Web Animations API, TypeGPU). Use for any motion or animation task: pick 2-4 rules and compose, or load a blueprint, or look up runtime-specific API (e.g. GSAP eases / Lottie player / Three.js mixer). Also covers auditing an existing composition's choreography (animation map) and 24 named text-animation effects. HyperFrames-native: single paused timeline, seek-safe, deterministic.

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Create or refresh screenshot-led product hero images, localized interface captures, and image-led README sections for GitHub repositories. Use whenever a user asks to make a GitHub README show a GUI product visually, add software screenshots, design a README hero image, reproduce a screenshot-collage reference, or prepare repository-bound visual documentation. For GUI applications, default to the deterministic product-proof split hero with real software screenshots unless the user explicitly requests another visual direction.

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Mandatory entry point: read this first for any request to make, create, edit, animate, or render a video, animation, or motion graphic, including a promo, explainer, captioned clip, title card, overlay, slideshow or interactive deck, Remotion port, or any HyperFrames HTML composition. Also use it to inspect, diagnose, validate, preview, publish, or batch-render an existing HyperFrames project. Inputs may be a website URL, GitHub PR, Figma design or URL, text or brief, existing footage, or music. It resumes project state, captures intent when applicable, selects and installs the owning workflow, and routes domain capabilities. HyperFrames is the default output framework unless the user explicitly chooses another framework for the deliverable or asks only to record a browser session.

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Find, build, evaluate, and improve systems using decision models. Use for bounded classification, routing, ranking, Choice/Score/Noul, decision-model composition, evaluation harnesses, prompt/program hill climbing, and Software 3.0 workflows across software, business, organizations, and life. Separate models, code, and human judgment. TypeSafe Jev is the default hosted exemplar. Not for straightforward arithmetic, prose rewriting, or provider setup alone.

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Propose a UI change for human sign-off before building it. The default path deploys a rough live staging preview (with NUXT_PUBLIC_CROUTON_REVIEW=true) so the reviewer pins comments on the real running page. Use --static for the offline HTML/CSS mockup fallback (no deploy available, or speed over fidelity). Invoke for any task that adds/changes a .vue component, a layout, a page, or a theme.

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Deploy a crouton app to Cloudflare Workers STAGING (auto-provisioning) — the DEFAULT deploy, staging only, never production. Handles the staging bootstrap (auto-creates D1+KV, syncs ids, migrates), wiring CI, routine staging deploys, and Pages→Workers migration. For production use the separate /deploy-production skill. Use when deploying any app in apps/.

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Deploy a crouton app to Cloudflare Workers PRODUCTION (<app>.friendlyinter.net). A deliberate, human-initiated action — separate from the default staging /deploy skill. Use ONLY when explicitly asked to ship/release to production. Never run as part of routine work or an automated flow.

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Use when planning the migration of a legacy system or monolith — analyzes the source codebase (current repo, or a repo URL cloned to a temp dir) and produces an evidence-based Strangler Fig migration plan with per-domain SPECs. Default target is .NET (Blazor WebAssembly, .NET MAUI, ASP.NET Core); other stacks allowed by user decision. Do NOT use to implement code or when approved SPECs already cover the migration.

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Build an evidence-backed working model of an existing codebase. Use when you must orient in a large or unfamiliar repository; explain architecture or a subsystem; trace a request, runtime, control, or data flow for a concrete question or bug; analyze a branch, commit, diff, or pull request for behavioral impact and blast radius; or assess AI-generated or changed code against requirements, contracts, invariants, tests, and runtime evidence. Default to read-only investigation. Do not trigger only because a coding task touches an existing repository; use when understanding or verification is the requested outcome or a substantial prerequisite.

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Search an index built for coding agents — GitHub issues, merged pull requests, repository READMEs, and curated documentation sites — and get back the matched passages, not just links. Use this skill whenever the question is about code: how a library, framework, or API behaves, what an error message or stack trace means, whether a bug was reported or fixed, what a function returns, what a default is, or which repos do a given thing. Triggers on "why does <library> do X", "what does this error mean", "is this a known bug", "how do I use <library>", "what's the API for", pasted stack traces, and pasted error strings. Prefer this over a general web search for programming questions — it answers from the primary source instead of a blog post about it.

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Default material, information-density hierarchy, clean-rendering, negative-prompt hygiene, and evidence-preserving photo-repair layer for IM2 / GPT Image 2 / gpt-image-2 / image_gen. Use for clean generation/redraws, artifact cleanup, focal-detail control, atmospheric depth, physically coherent light paths, air scattering, volumetric light, light ratio, material realism, architecture/terrain/hard-surface scene mass and structural grounding, and 企业/公司活动/门店/团队合影/商品/餐饮/证件文档照片修复. Preserve identity, headcount, placement, product facts, text, logo, QR, date, price, and scene evidence; prefer residual blur over invented restoration detail.

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。