「stable」の検索結果

Write clear, testable acceptance criteria in Given-When-Then format following INVEST principles and BDD best practices.

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。

Protocol-based dependency injection for testable Swift code — mock file system, network, and external APIs using focused protocols and Swift 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。

Master of defensive Bash scripting for production automation, CI/CD pipelines, and system utilities. Expert in safe, portable, and testable shell 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。

Master proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design to build maintainable, testable, and scalable systems.

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。

Guardian Angel gives AI agents a moral conscience rooted in Thomistic virtue ethics. Rather than relying solely on rule lists, it cultivates stable virtuous dispositions— prudence, justice, fortitude, temperance—that guide every interaction. The foundation is caritas: willing the good of the person you serve. From this flow the cardinal virtues as practical habits of right action and sound judgment. v3.0 introduced virtue-based disposition as the primary evaluation layer, providing deeper coherence than checklists alone. The agent's character becomes the safeguard. v3.1 adds: Plugin enforcement layer with before_tool_call hooks, approval workflows for ambiguous cases, and protections for sensitive infrastructure actions.

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。

Master of defensive Bash scripting for production automation, CI/CD pipelines, and system utilities. Expert in safe, portable, and testable shell 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。

Close out a completed workstream by updating README artifacts when needed, producing a commit message, and applying changelog-ready content when the work is ready for commit. Use after review when a stable checkpoint is ready.

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。

Plan and sequence complex implementation work into deterministic, testable steps for this repository. Use when the user asks for planning, roadmap breakdown, execution strategy, effort estimation, or multi-step delivery coordination across domains.

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。

Plan and sequence complex implementation work into deterministic, testable steps. Use when the user asks for planning, roadmap breakdown, execution strategy, effort estimation, or multi-step delivery coordination across domains.

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。

Close out a completed workstream by updating README artifacts when needed, producing a commit message, and applying changelog-ready content when the work is ready for commit. Use after review when a stable checkpoint is reached.

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, testing or previewing site-os — the BUILD_TARGET=stable dist directory, the four verification gates (build, test, env:check, brand:check), the preview servers on :3000 and :3100, and the failure modes that look like bugs but are not.

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。

Build a systematic threat hunt hypothesis framework that transforms threat intelligence, attack patterns, and environmental data into testable hunting hypotheses.

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。

Guides stable API and interface design. Use when designing APIs, module boundaries, or any public interface. Use when creating REST or GraphQL endpoints, defining type contracts between modules, or establishing boundaries between frontend and backend.

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 stable mocks for APIs, services, and UI components using MSW (Mock Service Worker), fixture conventions, and example patterns. Use for "API mocking", "MSW", "test mocks", or "service mocking".

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。

Build backend AI with Vercel AI SDK v6 stable. Covers Output API (replaces generateObject/streamObject), speech synthesis, transcription, embeddings, MCP tools with security guidance. Includes v4→v5 migration and 15 error solutions with workarounds. Use when: implementing AI SDK v5/v6, migrating versions, troubleshooting AI_APICallError, Workers startup issues, Output API errors, Gemini caching issues, Anthropic tool errors, MCP tools, or stream resumption failures.

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 high-quality images via Stability AI API (SDXL, SD3, Stable Image Core). Use when user asks to "generate image", "make a picture", or "draw this".

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 for sensemaking, frame revision, anomaly handling, questioning strategy, and ambiguity diagnosis with the Data/Frame model before action is chosen. NOT for deterministic classification, settled execution planning, or situations where the frame is already stable.

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。

Audit whether a "current product focus receipt" (Decision/Now/Why-now/Evidence/Not-now/Cut-suspend/First-visible-proof/ Acceptance-gate/Kill-revisit-trigger/Owner/Review-date) and the work order it gates state a real, testable decision and a real agent launch — not planning-placeholder prose that sounds like an assignment but can't be proven. Use when deciding whether to launch an agent chain against a milestone or a stated "focus," when a work order lacks explicit input/output/owner/proof-gate, or when an acceptance gate would only prove a cached UI state instead of daemon truth. NOT for distinguishing roadmap items from energy-driven sidequests (legible-roadmap-with-sidequests), reviewing a PRD/prototype for product-completeness gaps like cold start or pricing (product-reality-reviewer), or accepting already-finished work against a personal acceptance bar (port-daddy-user-surrogate-pm-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。

Digital equity and trauma-informed design for marginalized populations. Activate on "accessibility", "offline-first", "trauma-informed", "reentry", "recovery population", "shared device", "unstable phone", "digital equity", "design justice", "low-literacy", "intermittent access". NOT for general UX, marketing optimization, or enterprise SaaS design.

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Produces a narrow-interval quality signal for a WinDAGs skill that a third party can verify without inspecting model weights or method internals. The mechanism is attribution-kNN over a tamper-evident outcome log (task→skills→accept/reject store), combined with a conformal-prediction calibration certificate that provides a formal coverage guarantee. Thompson/Beta sampling is explicitly rejected as a category error: it produces a selection signal for stationary i.i.d. rewards, not an attestation signal for context-dependent LLM output quality. The resulting bundle — outcome-log Merkle root + conformal threshold + optional TEE guardrail signature — constitutes a "narrow-interval trust" credential: verifiable, bounded, and internals-opaque.

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Design decisions for when and what sounds to use in web apps, mobile apps, and desktop native apps — plus how to create them with AI. Covers UI sound vocabulary (confirms, alerts, transitions, errors), platform conventions (iOS, macOS, Material), creating sounds with AI tools (ElevenLabs, AudioCraft, Stable Audio), Web Audio API for procedural sounds, haptic-audio pairing, and the psychology of satisfying vs. annoying UI sounds. Activate on 'app sounds', 'UI sounds', 'notification sound', 'sound design app', 'earcons', 'sonic branding', 'interaction sounds', 'feedback sounds', 'app audio', 'satisfying sounds'. NOT for professional audio engineering (use sound-engineer), Windows 3.1 retro sounds specifically (use win31-audio-design), music composition (use sound-engineer).

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。