「layers」の検索結果

Use when adding a use case, port, repository or adapter in a Go service — where each piece goes, how errors and transactions cross layers, and how to check the import direction

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Comprehensive brand messaging, positioning, and value proposition development using proven frameworks including Peep Laja Message Layers, Osterwalder Value Proposition Canvas, Geoffrey Moore positioning, April Dunford's Five Components, StoryBrand SB7, Andy Raskin Strategic Narrative, and Messaging House. Use when developing brand identity, brand messaging architecture, positioning statements, value propositions, messaging hierarchies, brand pillars, taglines, one-liners, elevator pitches, brand guidelines, visual identity systems, or creating Positioning & Messaging Packs. Triggers include: messaging framework, brand positioning, value prop, messaging architecture, brand pillars, brand identity, StoryBrand, positioning statement, brand guidelines, design system, messaging house, corporate identity, brand voice, visual standards.

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Canonical guide to Genesis browser automation - layers (Camoufox, Chromium, the user's Chrome over CDP, TinyFish, desktop), per-tool timeouts, safety gates, verify-after-act, what a click checks (scroll, hit test, covered targets), overlays, iframes, tabs, and failure diagnosis

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Designs risk-based test strategy for software delivery. Use when defining coverage, setting CI gates, managing flaky tests, choosing test layers, or establishing release criteria.

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Builds analytics engineering layers for metrics, contracts, and BI-ready models. Use when shaping dbt or SQLMesh marts, metric governance, lineage, or data quality.

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Generates placeholder .aseprite files from asset specs using the Aseprite MCP. Reads asset specs and art bible, creates sprites with correct dimensions/palette/layers, exports PNGs. Run after /asset-spec has produced specs and /art-bible exists.

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Use for every task that plans, writes, edits, debugs, tests, refactors, reviews, or maintains code. Apply to professional repositories, scripts, apps, services, prototypes, beginner work, vibe coding, and bounded code changes inside large systems. Choose the fewest clear lines, files, paths, states, and abstractions that correctly implement the requested behavior. Do not invent fallbacks, compatibility paths, defensive layers, or future scale without evidence. Do not use for ordinary conversation, emotional support, prose, translation, summarization, social-media content, or general research or technical discussion that is not part of planning, changing, or reviewing code. Never weaken real security, integrity, concurrency, compatibility, privacy, accessibility, legal, or regulatory requirements.

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 code changes adversarially using parallel review layers (Blind Hunter, Edge Case Hunter, Acceptance Auditor) with structured triage into actionable categories. Use when the user says "run code review" or "review this 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。

Verify mini-program implementations and fixes with risk-calibrated evidence across static checks, unit tests, integration tests, state matrices, simulators, real devices, cloud environments, and release artifacts. Use when users ask to test, validate, accept, regression-check, quality-check, confirm readiness, or determine whether a mini-program feature is actually complete. Binds results to a source and build fingerprint, records commands and observable evidence, separates passed, failed, blocked, and not-run layers, prioritizes the next highest-information check, and never converts local success into device, cloud, release, or formal acceptance claims.

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 WeChat Mini Program platform constraints and evidence layers for an idea or existing repository, including source configuration, build output, WeChat DevTools loading, device behavior, permissions, privacy declarations, cloud or backend linkage, upload, trial, review, and production status. Use when users ask about WeChat-specific configuration, project roots, compile paths, capability permissions, privacy compliance, official platform rules, upload or release readiness, or why source and platform behavior differ. Performs read-only evidence classification by default, verifies time-sensitive or high-risk claims against current official WeChat sources, redacts identifiers and secrets, and never upgrades one evidence layer into another.

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Builds a talk as a beatdeck - a fixed 1920x1080 React stage where one click is one beat, with authored motion, presenter view, overview, and an offline self-contained build. Covers turning source material (PPTX, Keynote/PDF export, Markdown, notes) into a content audit and a scene/beat map, choosing or applying a theme, writing scene layers, automatic beats, and verifying every beat with npm run verify. Use whenever the task is creating, porting, restyling, rehearsing or fixing a presentation, talk, keynote, slide deck or conference session that should feel like a live system rather than slides, or whenever the repo contains src/beatdeck/.

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。

Detailed testing standards for persistence, service, and controller layers

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Design guardrail layers — input classifiers, output validators, PII scrubbers, policy rule engines. Use when asked to "add safety filters", "scrub PII from LLM output", or "design content moderation".

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。

Development harness plugin documentation index. Use when looking up SAM pipeline, backlog lifecycle, SDLC layers, task/plan schema, plan artifacts, quality gates, impact analysis, or dispatch schema documentation.

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Evaluate and iterate on the SDLC Layer Separation Architecture implementation. Runs validation checks (cross-references, doc completeness, layer metadata, integration points), produces a findings report, and supports iterative fixes. Use when validating first-pass implementation, before claiming layer work is complete, or when improving layer docs/schema.

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Validate and iterate on the SDLC Layer Separation Architecture implementation across the check categories defined in its Evaluation Checklist — cross-references, doc completeness, knowledge-explorer layer filters, research entry metadata, integration points, and plan consistency. Produces a structured findings report and optionally applies safe fixes. Use when validating a first-pass implementation, before claiming layer work complete, auditing layer docs or schema, or running --dry-run to preview findings without changes.

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。

Manage Alibaba Cloud Function Compute (FC) 3.0 using the @alicloud/fc20230330 TypeScript SDK. Use when working with serverless functions on Alibaba Cloud, including function CRUD, invocation, versions, aliases, triggers (HTTP/Timer/OSS/CDN/MNS), async invocation, concurrency and scaling configs, provisioned instances, custom domains, layers, VPC bindings, sessions, and resource tagging. Covers all 67 APIs of the FC 20230330 version.

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。

OCRs scanned or image-only corpora with vision-language models in three phases. Evaluate selects a system by measured CER or WER against stratified human ground truth. Run builds the production pipeline. Clean corrects raw OCR with LLM and rule-based passes, quality diagnostics, and span-level provenance. Use when choosing an OCR model for a corpus, measuring OCR accuracy, transcribing scans at scale, or correcting raw OCR. Born-digital documents with text layers go to doc-to-markdown.

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。

Multi-step reasoning engine for complex analysis and systematic problem solving. Use when: (1) Complex debugging scenarios with multiple layers, (2) Architectural analysis and system design, (3) Problems requiring hypothesis testing and validation, (4) Multi-component failure investigation, (5) Performance bottleneck identification. Triggers: "--think", "--think-hard", "--ultrathink", "analyze step by step", "break down this problem", "systematic analysis". IMPORTANT: Do NOT use for simple single-step tasks.

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Test an uncertain Anabasis v2 path at the smallest real layer that can decide it, before a paid round is asked to: one fact from a log or a dry run, a walk of one value across the layers a change touches, a replay of every recorded log through this tree's readers, a whole round rehearsed on a seeded copy of a campaign with a script in each model seat (`bun run runs seed`, `bun run runs rehearse`), or one live seat from a seed. Use when the operator asks whether a change will work in the real system, to simulate or rehearse a round, to stand again in a position a past run stood in, what a campaign of a given size can read, or what a kill or a stop leaves behind. Not for reading a finished run (whole-run-investigation) or starting a paid one (launch-run).

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。

Progressive Disclosure skill index — efficient skill loading in 3 layers. Layer 1 (index, ~100 tokens) always loaded. Layer 2 (summary, ~300 tokens) loaded on context need. Layer 3 (full SKILL.md) loaded on execution only.

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Create and manage Architecture Decision Records (ADRs). Use when making architectural changes (refactoring, API versioning, new domain layers, schema changes), when reviewing recent commits for documentation needs, or when the user mentions ADR, architecture decisions, or documenting technical decisions.

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Generates social media graphics and OG images dynamically via Bannerbear REST API. Manages template modifications, font layers, and signed URL generation for on-the-fly image personalization.

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AgentQL is TinyFish's web automation and extraction toolkit that layers a natural-language query system on top of live sites. Its Python and JavaScript SDKs integrate with Playwright and let agents target data and interface elements without depending on brittle CSS selectors alone.

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。