「engine」の検索結果

Independent re-derivation gate for proposed changes during autonomous execution. Evaluates a change (code diff, config edit, engine evolution, schema change) against the work's authority matrix and a 4-check protocol: invariant compliance, scope authorization, detection narrowness, and regression risk. Returns structured JSON with pass/fail per check, an overall verdict (APPROVE / REQUEST_CHANGES / REJECT), and blockers. Profile-driven thresholds (strict for unattended runs, lenient for desk supervision). Use when an autonomous agent proposes an engine change, when reviewing a Claude Code-authored modification before merge, or when an unattended profile requires independent re-derivation. Trigger on: "review/approve this proposed change," "critic-gate this," "is this change safe," "is this safe to merge," "should I let this land," "run the critic." Do NOT use for handoff-readiness checks (use rootnode-handoff-trigger-check), general code review, or design-time correctness. Authority matrix required.

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Specialized agentic and context engineering prompt methodology for Claude. Use when designing AI agents — system prompts, tool definitions, context window architectures, failure mode planning, multi-agent coordination, agent evaluation. Trigger on: "design an agent," "build a system prompt for an agent," "architect an agent's context," "design tool definitions," "agent system prompt," "context window architecture," "tool interface design," "evaluate agent behavior," "multi-agent coordination," "context engineering for agents," "ACI design." Provides 12 tested approaches for agent architecture (designing agents, not using them). Do NOT use for evaluating or scoring individual non-agent prompts (use rootnode-prompt-validation if available). Do NOT use for general software architecture that hosts agents (use standard technical reasoning for that).

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Use when asked to review learning progress after a milestone. Not for engineering retrospectives, use engineering-retrospective. For agent-environment ones, use agent-environment-retrospective.

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Use when a user requests an engineering retrospective for a period. Not for agent-environment retrospectives: use agent-environment-retrospective. For learning milestones: use learning-retrospective.

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Use when planning an end-to-end fuzzing program for a project: engine and target selection, corpus management, and CI or nightly wiring. Not for harness design: use fuzz-harness-writing. Not for engine operation or triage: use libfuzzer. Not for sanitizer flag reference: use sanitizers.

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Use when a completed session needs an agent-environment retrospective. Not for an engineering retrospective from telemetry: use engineering-retrospective.

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Use when Odylith context-engine packets, retrieval, anchor resolution, or routing support flows need operation-level work.

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。

Work with Odylith context-engine packets, retrieval, and routing support flows.

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Weekly engineering retrospective. Analyzes commit history, work patterns, and code quality metrics with persistent history and trend tracking. Team-aware: breaks down per-person contributions with praise and growth areas. Use when asked to "weekly retro", "what did we ship", or "engineering retrospective".

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Turn an electronic product idea, behavior description, reverse-engineering note, or incomplete circuit concept into a decision-ready engineering brief and architecture. Use before schematic design or audit when functions, operating states, power, interfaces, mechanics, production quantity, cost, or measurable requirements are missing or ambiguous.

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Menu engineering analysis — item placement, description quality, pricing psychology, upsell opportunities, photo presence, Kasavana matrix (Stars/Plowhorses/Puzzles/Dogs)

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Build an organic-traffic operating system for any site or app: a multi-phase, resumable engine that plans and ships a BATCH ROADMAP of landing pages (use-cases, playbooks, /for/ pages, roundups) on top of real keyword research, with a persistent phase tracker. Use when the user says 'SEO machine', 'build organic traffic', 'rank on Google', 'we need traffic', 'SEO roadmap', 'batch of pages', 'pages roadmap', or 'build an SEO engine'. Distinct from `seo-audit` (one-off diagnostic) and `seo-content` (single-article writer): this skill owns the multi-page roadmap, run sequencing, page generation, internal linking, and the persistent phase tracker across the whole run. For a single alternatives or vs/comparison page, see /competitor-alternatives; for programmatic SEO templates and content at scale, see /seo-content; for off-page link building, see /off-page-seo. Resumable — every invocation continues the last run.

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Plans free tools, calculators, generators, and interactive widgets that attract target audience through search and social sharing. Engineering as marketing — build something useful, capture leads. Use when someone wants to build a free tool for marketing, says 'free tool', 'calculator', 'generator', 'engineering as marketing', 'side project marketing', 'interactive widget', 'lead generation tool', 'SEO tool', 'growth hack', 'build something to attract users', or wants to attract users through utility rather than content. Also use when someone asks 'how do I get more signups', 'what should I build for marketing', or 'content marketing alternative' — a free tool is often the best answer. Includes search volume validation, UX flow templates, and conversion hook design.

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Builds, measures, and improves an Answer Engine Optimization program across ChatGPT, Perplexity, Claude, Gemini, Google AI Overviews, and Copilot. Use for AEO, GEO, LLMO, AI search visibility, AI citations, brand mentions, answer-engine monitoring, llms.txt, AI crawler audits, or questions like 'what does AI say about us?' and 'why are competitors cited instead of us?'. Produces a durable evidence report and prioritized gameplan, not a one-shot content checklist.

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Design and review autonomous or semi-autonomous agent loops — systems where an agent prompts itself, runs on a schedule or event, and iterates toward a goal without a human at the keyboard. Use this whenever the user wants to build, design, harden, or audit any kind of self-running agent, agentic workflow, control loop, background worker, cron/heartbeat/webhook-driven agent, "Ralph loop", auto-PR-fixer, monitoring agent, or any long-running task that should keep going on its own until done. Trigger this even when the user says "agentic workflow", "automation that runs itself", "an agent that keeps trying until X", or "make my agent stop running away / burning tokens" without using the word "loop". Also use it to review an existing loop's stopping conditions, guardrails, verification, and escalation paths.

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Use when designing, optimizing, testing, or deploying robust prompt systems for AI agents. This skill provides frameworks for structured prompt engineering, meta-prompting, and automated optimization workflows.

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Deploy and manage cloud infrastructure on Cloudflare (Workers, R2, D1, KV, Pages, Durable Objects, Browser Rendering), Docker containers, and Google Cloud Platform (Compute Engine, GKE, Cloud Run, App Engine, Cloud Storage). Use when deploying serverless functions to the edge, configuring edge computing solutions, managing Docker containers and images, setting up CI/CD pipelines, optimizing cloud infrastructure costs, implementing global caching strategies, working with cloud databases, or building cloud-native applications.

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。

Generative Engine Optimization for AI search engines (ChatGPT, Claude, Perplexity).

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Master context engineering for AI agent systems. Use when designing agent architectures, debugging context failures, optimizing token usage, implementing memory systems, building multi-agent coordination, evaluating agent performance, or developing LLM-powered pipelines. Covers context fundamentals, degradation patterns, optimization techniques (compaction, masking, caching), compression strategies, memory architectures, multi-agent patterns, LLM-as-Judge evaluation, tool design, and project development.

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Use when writing Swift code for Apple platforms - iOS, macOS, visionOS. Covers Swift 6 concurrency, actors, Sendable, async/await, SwiftUI, MVVM architecture, Metal GPU programming, RealityKit ECS, visionOS scenes, interpolation/animation, advanced collection types, property wrappers, Combine bridging, error handling, testing, and API design patterns.

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Use when legacy prompts or older framework flows reference `qa-automation-engineer` and you need the modern runtime to resolve that capability cleanly.

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Use when legacy prompts or older framework flows reference `remotion-engineer` and you need the modern runtime to resolve that capability cleanly.

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Use when legacy prompts or older framework flows reference `growth-engineer` and you need the modern runtime to resolve that capability cleanly.

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。

Consolidated Galyarder Framework Engineering intelligence bundle.

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。