「engine」の検索結果

Writes, refactors, and evaluates prompts for LLMs generating optimized templates, structured output schemas, evaluation rubrics, and test suites.

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。

Defines service level objectives, creates error budget policies, designs incident response procedures, develops capacity models, and produces monitoring configurations.

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。

Designs chaos experiments, creates failure injection frameworks, and facilitates game day exercises for distributed systems with runbooks, experiment manifests, and rollback procedures.

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 Dockerfiles, configures CI/CD pipelines, writes Kubernetes manifests, and generates Terraform/Pulumi infrastructure templates for deployment automation and GitOps.

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。

Track the brand narrative in AI engines against desired positioning, flag misstatements. "is AI misrepresenting our brand"

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 brand entity facts AI engines trust: Wikidata, Knowledge Panel. "is our Knowledge Panel accurate"

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。

Monitor GEO visibility over time across six AI answer engines, with a 0-100 trend. "is ChatGPT mentioning us"

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。

Run a one-time AEO audit of six AI answer engines, scored per surface. "check our AI search visibility"

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。

Load shared marketing context: brand profile, benchmarks, compliance rules, specs. "load my brand context"

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。

Draft content in brand voice through a gated pipeline: blogs, ads, emails. Brief only → content-brief. "write a blog post about X"

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 technology stack evaluation and comparison tool with TCO analysis, security assessment, and intelligent recommendations for engineering teams

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 Test Driven Development guide for engineering subagents with multi-framework support, coverage analysis, and intelligent test generation

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 coordinating multiple AI agents with Agent Relay's workflow engine and need to pick the right orchestration pattern - covers the 10 core patterns (fan-out, pipeline, hub-spoke, consensus, mesh, handoff, cascade, dag, debate, hierarchical) plus 14 specialized ones, with decision framework and accurate SDK/YAML 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。

Reviews animation and motion code against a high craft bar derived from Emil Kowalski's design engineering philosophy. Default to flagging; approval is earned.

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。

Design a learning sequence teaching prompt quality — comparing vague vs. refined prompts to show why specificity and context transform AI output. Use when students use AI without understanding why output quality varies.

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 the user has evaluation principles or a dataset but needs help choosing the right graders, designing evaluation metrics, creating LLM-as-judge prompts, combining multiple metrics into a composite score, or building an automated evaluation pipeline. Also use when the user mentions grader selection, metric design, judge prompt engineering, rubric design, evaluation pipeline code, or "how to evaluate [X] automatically." Outputs executable OpenJudge pipeline 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。

Restructure any rough build, research, or legal-drafting request into an IRAC-shaped prompt — Issue, Rule, Analysis, Conclusion — optimized for a frontier model. It's the bar-exam framework, repurposed as prompt engineering. The skill leads with the issue and ends with the conclusion (where models weight attention most), forces you to name your constraints and non-goals, and specifies what "good" looks like before a single token is generated. Use it before any non-trivial build, or whenever a vague ask deserves a precise brief.

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。

EU AI Act Knowledge Engine — authoritative regulatory Q&A grounded in 70 official EU source documents (including the 2026 Commission draft guidelines on Art. 6 high-risk classification — general principles, Annex I, Annex III). Answers any question about the AI Act with article-level citations from the full regulation text, Commission guidelines, EDPB/EDPS opinions, codes of practice, harmonised standards, FRIA guides, incident templates, and sector-specific guidance. This skill should be used when the user asks about the "EU AI Act", "AI Act regulation", "Regulation 2024/1689", "KI-Verordnung", asks to "explain an AI Act article", "what does Article X say", "AI Act requirements", "AI Act penalties", "AI Act timeline", "GPAI obligations", "high-risk AI", "prohibited AI practices", "AI Act and GDPR", "fundamental rights impact assessment", "AI Act standards", "AI Act national implementation", "AI literacy", "serious incident reporting", "Digital Omnibus", "AI regulatory sandbox", or any question about EU artificial intelligence regulation. Also triggers on German terms: "KI-Gesetz", "KI-Verordnung", "Hochrisiko-KI", "KI-Kompetenz", "Grundrechte-Folgenabschaetzung", "GPAI-Verhaltenskodex". For assessment workflows that produce a classification decision rather than a knowledge answer, offer to run a structured risk-tier classification in-session.

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。

Capture, structure, and recycle lessons from active and closed legal matters. Three modes: in-flight capture (triggered by scope changes, risk events, status updates — highest value), mid-matter review (phase gates or quarterly), and matter close retrospective (full structured findings). Lessons are formatted for immediate reuse, not filed and forgotten. Use when a risk materialises, a scope change lands, a phase completes, or a matter closes and you want to convert what happened into something useful for the next matter. Trigger on: 'capture a lesson', 'what did we learn', 'matter close', 'retrospective', 'lessons learned', 'what went wrong', 'what worked', 'phase gate review', 'debrief', 'extract the learning', 'close the matter', 'what should we do differently', 'pattern from this matter', 'improve the next one'.

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。

When the user wants to create, generate, or produce video content using AI tools or programmatic frameworks. Also use when the user mentions 'video production,' 'AI video,' 'Remotion,' 'Hyperframes,' 'HeyGen,' 'Synthesia,' 'Veo,' 'Sora,' 'Runway,' 'Kling,' 'Seedance,' 'Hailuo,' 'MiniMax,' 'Pika,' 'Hunyuan,' 'Wan,' 'video generation,' 'AI avatar,' 'talking head video,' 'programmatic video,' 'video template,' 'explainer video,' 'product demo video,' 'video pipeline,' 'copy this edit,' 'match this video style,' 'reverse-engineer this video,' 'edit like this reference,' or 'make me a video.' Use this for video creation, generation, and production workflows. For video content strategy and what to post, see social. For paid video ad creative, see ad-creative.

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。

Full-history Godot version migration hub. Use when upgrading any Godot project toward the library target (currently 4.7)—from 1.x/2.x legacy eras through 3→4 and hop-by-hop 4.x minors—or finding Domain Skill-scoped migration notes. Trigger keywords: upgrade Godot, migrate Godot version, Godot 3 to 4, Godot 2 to 3, upgrading_to_godot, version migration, breaking changes, convert-3to4, TileMapLayer, AnimationMixer, engine.cfg, project.godot.

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。

AI movement decision router for chase, patrol, crowd, and bake choices on top of NavigationAgent/Server. Use when deciding node agent vs RID server, bake vs obstacle, layer masks, or retarget policy — not for engine navmesh recipes. Keywords: AI navigation, chase retarget, patrol, crowd RVO, bake vs obstacle, NavigationAgent decision tree.

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。

Android APK unpacking and resource extraction tool for reverse engineering. Use when you need to decode APK files, extract resources, examine AndroidManifest.xml, analyze smali code, or repackage modified APKs.

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 digital and analog captures from Saleae Logic MSO devices. Decode protocols like UART, SPI, I2C from exported binary files. Use when analyzing logic analyzer captures for CTF challenges, hardware reverse engineering, or protocol decoding.

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。