「code mode」の検索結果

Suede Labs cross-surface canon discipline: hold one canonical answer across every surface that states it (web, iOS, Android, docs, a second service) by generating a contract from the reference surface and making the others assert against it, so a divergence fails a test instead of reaching a user or an answer engine. Consistent canon is what makes a product citable: generative search quotes sources that do not contradict themselves, and a number that differs between your site and your app is a contradiction a model can see. Use when the same rule, threshold, ladder, or stated fact lives on two or more surfaces; when values are copied by hand out of a handoff; when two surfaces already disagree; or when auditing where they drifted. NOT FOR: deciding which surface is correct (a product call this skill records rather than makes); reviewing a diff (use suede-code-review); wiring merge gates (use suede-ci-gate); a generative-search audit of a page (use suede-seo-audit).

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。

Suede Labs Android app factory: plan, build, test, and release a production-grade native Android app from a product idea through Google Play. Use for requests to create, ship, submit, monetize, or modernize an Android app. Covers Kotlin and Jetpack Compose architecture, API-level policy verification, accessibility, privacy and Data Safety, Play Billing, Play Integrity, account deletion, testing, performance, store assets, signing, staged rollout, and a release evidence gate. NOT FOR: iOS work (private Suede Labs companion, not in this pack: ios-app-factory), a review-only pass on an existing app (use suede-code-review), or live Play listing and keyword audits on a shipped app (use suede-aso).

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。

Authoritative reference for the neo4j-agent-memory Python package — a graph-native memory system for AI agents built on Neo4j — and for the hosted service (NAMS) at memory.neo4jlabs.com. Use this skill whenever the user mentions neo4j-agent-memory, agent memory with Neo4j, context graphs, the POLE+O model, MemoryClient/MemorySettings, the memory MCP server, or any of the framework integrations (LangChain, PydanticAI, CrewAI, AWS Strands, Google ADK, Microsoft Agent Framework, OpenAI Agents, LlamaIndex). Also use when the user mentions the hosted service at memory.neo4jlabs.com, NAMS, the Neo4j Agent Memory Service, the `nams_` API key prefix, or the hosted MCP endpoint. Also use when writing documentation, blog posts, tutorials, PRDs, or code samples for the project, when comparing agent memory approaches, or when positioning graph-native memory against vector-only approaches — even if the user doesn't explicitly name the package.

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。

UI/UX design intelligence. 67 styles, 96 palettes, 57 font pairings, 25 charts, 13 stacks (React, Next.js, Vue, Svelte, SwiftUI, React Native, Flutter, Tailwind, shadcn/ui). Actions: plan, build, create, design, implement, review, fix, improve, optimize, enhance, refactor, check UI/UX code. Projects: website, landing page, dashboard, admin panel, e-commerce, SaaS, portfolio, blog, mobile app, .html, .tsx, .vue, .svelte. Elements: button, modal, navbar, sidebar, card, table, form, chart. Styles: glassmorphism, claymorphism, minimalism, brutalism, neumorphism, bento grid, dark mode, responsive, skeuomorphism, flat design. Topics: color palette, accessibility, animation, layout, typography, font pairing, spacing, hover, shadow, gradient. Integrations: shadcn/ui MCP for component search and examples.

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 tests for data or ML code — what to test at each layer (transforms, schemas, properties, pipeline smoke, model-quality thresholds, determinism and parity), how to build small synthetic fixtures, and what not to test

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 data crosses a boundary into your code (file, API, warehouse table, upload, model input or output), when a join or cleaning step can change row counts, or when adding pandera, Great Expectations, Pydantic or dbt checks

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 you meet a new dataset, table, file or source, before building features or a model on it, or when a number looks wrong — profile grain, keys, nulls, cardinality, time coverage and target balance, and persist what you learn as code

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 native iOS with SwiftUI - small composable views, observable state models, atomic components, Swift 6.2+ concurrency, and system styling over hardcoded values

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 before and after calling rollback_release - what a rollback restores per delivery mode, when a schema change makes a code rollback unsafe, how to work and report manual_steps, and what to do when rollback_release refuses

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 native Android with Jetpack Compose - stateless composables, state hoisting, ViewModel-owned state, edge-to-edge, predictive back, adaptive layout, atomic components, and Material theming over hardcoded values

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。

Comprehensive GLSL shader techniques for creating stunning visual effects — ray marching, SDF modeling, fluid simulation, particle systems, procedural generation, lighting, post-processing, and more.

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 and render OpenSCAD 3D models. Generate preview images from multiple angles, extract customizable parameters, validate syntax, and export STL files for 3D printing.

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 and refactor Bash scripts following conventions (strict mode, fct_ naming, quoting). Includes shellcheck linting. Use when creating shell scripts, refactoring existing scripts, debugging shell errors, or linting scripts.

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。

This skill should be used when the user sends an image and asks to "analyze this image", "describe this picture", "what's in this image", or any request requiring visual understanding of images. Provides image analysis using Zhipu GLM-4.6V multimodal model.

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。

Tmux skill for running background tasks. Use this skill when: 1. Before any tasks that can potentially run for more than 2 minutes (e.g.: package install, many tests, model training) 2. Before any tasks that are expected to run indefinitely in background (e.g.: web servers, port forwarding) 3. Bash tool reports `timeout after 120000ms` 4. User request to run tasks in background

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。

Evaluate any agent skill against a merged framework — Anthropic's Claude Code best practices plus Matt Pocock's writing-great-skills methodology — across 4 axes (Trigger, Structure, Steering, Pruning). Produces an evidence-cited scorecard (0–100), a weighted overall score, and diagnosed failure modes with prioritized fixes. Use when the user asks to evaluate, rate, or audit a skill ("evaluate this skill", "skill scorecard", "review SKILL.md"), or to compare two skills.

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 tests in red or expand mode and run-and-heal new tests. Use when the user says "lets expand test coverage", "I want to automate tests", "lets write acceptance tests", or "I want to do ATDD"

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。

UI/UX design intelligence for web and mobile. Includes 50+ styles, 161 color palettes, 57 font pairings, 161 product types, 99 UX guidelines, and 25 chart types across 10 stacks (React, Next.js, Vue, Svelte, SwiftUI, React Native, Flutter, Tailwind, shadcn/ui, and HTML/CSS). Actions: plan, build, create, design, implement, review, fix, improve, optimize, enhance, refactor, and check UI/UX code. Projects: website, landing page, dashboard, admin panel, e-commerce, SaaS, portfolio, blog, and mobile app. Elements: button, modal, navbar, sidebar, card, table, form, and chart. Styles: glassmorphism, claymorphism, minimalism, brutalism, neumorphism, bento grid, dark mode, responsive, skeuomorphism, and flat design. Topics: color systems, accessibility, animation, layout, typography, font pairing, spacing, interaction states, shadow, and gradient. Integrations: shadcn/ui MCP for component search and examples.

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, redesign, repair, validate, preview, and package lightweight standalone HTML deliverables. Use for marketing experiences, product prototypes, infographics, motion narratives, documents, browser tools, dashboards, guided workflows, knowledge interfaces, mini-games, presentations, reusable style and brand systems, and local-model text, vision, image, or code tools that must run without a build chain or hosted service.

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。

Creates or improves an Agent Skill by defining a precise trigger boundary, concise procedural instructions, progressive-disclosure references, deterministic helpers, routing evals, and validation evidence. Use when authoring or reviewing SKILL.md, skill metadata, bundled references, or skill packaging. Not for ordinary project documentation or application code.

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。

Reviews a GitHub pull request or focused branch diff for correctness, regressions, security, compatibility, test gaps, and maintainability. Use when an implementation needs an independent source-aware review before merge. Produces prioritized findings with evidence and actionable fixes. Not for architecture-only audits, formatting preferences, or processing existing review feedback.

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。

Explores ambiguous product or engineering intent before implementation, clarifies users, constraints, alternatives, risks, and acceptance criteria, and turns the result into an approved design direction. Use for a new feature, subsystem, creative UI, architecture change, or unclear request. Not for an already-scoped bug fix, an existing implementation plan, or a narrow code 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。

Writes a detailed, executable implementation plan from an approved design or multi-step requirement, including exact files, interfaces, tests, commands, checkpoints, and rollback considerations. Use before touching code for a substantial feature, migration, refactor, or cross-cutting change. Not for a one-file bounded fix or for executing a plan that already exists.

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。

Validates a running application, CLI, API, service, or generated artifact as a user or operator against a prewritten observable behavior contract while remaining source-blind. Use for acceptance checks, runtime proof, anti-fake probes, release smoke tests, or an independent companion to code review. Not for source-quality findings, root-cause diagnosis, or visual design judgment outside the contract.

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。