「engine」の検索結果

Comprehensive SEO analysis for any website or business type. Performs full site audits, single-page deep analysis, technical SEO checks (crawlability, indexability, Core Web Vitals with INP), schema markup detection/validation/generation, content quality assessment (E-E-A-T framework per Dec 2025 update extending to all competitive queries), image optimization, sitemap analysis, and Generative Engine Optimization (GEO) for AI Overviews, ChatGPT, and Perplexity citations. Analyzes AI crawler accessibility (GPTBot, ClaudeBot, PerplexityBot), llms.txt compliance, brand mention signals, and passage-level citability. Industry detection for SaaS, e-commerce, local business, publishers, agencies. Triggers on: "SEO", "audit", "schema", "Core Web Vitals", "sitemap", "E-E-A-T", "AI Overviews", "GEO", "technical SEO", "content quality", "page speed", "structured data".

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 debugging and system comprehension. Triggers: debug this, root cause, how does X work, reverse engineer, 왜 안 돼, 디버깅, 원인 분석, 로직 파악, 뜯어봐.

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。

Render `.claude/canvas/opportunities.yml` as a diagram or structured list. Read-only. Default format Mermaid mindmap. Consults the attribution registry per `${CLAUDE_PLUGIN_ROOT}/engine/render-conventions.md` consent + privacy gate. Second specialist in the render fleet.

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。

Central dispatcher for the canvas/state render fleet. Routes intent to a specialist (`/mycelium:diamond-render`, `/mycelium:ost-render`, `/mycelium:cycle-render`) OR lists what's renderable. Cross-cutting views spanning multiple canvases (opp→sol→cycle traceability, confidence-over-time, cluster-graduation-flow) are research-gated and NOT emitted by default. See `${CLAUDE_PLUGIN_ROOT}/engine/render-conventions.md` for shared conventions.

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。

Render a completed diamond's work as a shareable, factual one-page "work receipt" (problem framed, assumptions tested, what was killed vs kept) carrying an origin line + a volitional onward-handoff data flow. Read-only. Consults the attribution registry per `${CLAUDE_PLUGIN_ROOT}/engine/render-conventions.md` consent + privacy gate. Follows the render fleet's shared conventions + consent gate (Check 43 applies via the `-render` name); a standalone shareable-artifact generator, NOT a `/render` dispatcher target. Audience fixed `external` (a hand-it-over artifact).

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。

Render `.claude/canvas/cycle-history.yml` as a Gantt chart + outcome distribution pie (or ascii / json). Read-only. Consults the attribution registry per `${CLAUDE_PLUGIN_ROOT}/engine/render-conventions.md` consent + privacy gate. Third specialist in the render fleet.

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。

Render `.claude/diamonds/active.yml` as the decisions each diamond has recorded, in order, grouped by mode (discovery, delivery). Read-only. Default format Mermaid stateDiagram-v2, drawn by a script. Recommended at the end of `/mycelium:diamond-assess` so every assessment closes with a visual state-of-play. See `${CLAUDE_PLUGIN_ROOT}/engine/render-conventions.md` for shared render fleet conventions.

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。

Measure Mycelium's own scaffold token cost (CLAUDE.md + engine + harness + canvas + memory) and surface a structured estimate. One-shot audit; pair with /framework-health for trend tracking.

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。

Interactive code simulation engine that spawns ghost personas to review your code before it ships. Each ghost walks through your code line-by-line from a different failure perspective: The Adversary (race conditions, double-submit, idempotency), The Enterprise Edge (business logic gaps, multi-currency, B2B tax exemptions, locale), Chaos Monkey (timeouts, partial failures, network partitions, OOM). Produces a chaos timeline, structured bug report with severity ratings, and three resolution tiers (quick fix, robust architecture, generated test suite). Use this skill whenever the user mentions code review, PR review, "find bugs", "what could go wrong", "will this break", "is this safe", "stress test", "edge cases", design review, pre-merge check, dry run, chaos test, or wants a second opinion on any code — even if they don't explicitly ask for "ghost personas" or "simulation". Also trigger when someone pastes a code snippet with any question about its correctness or safety.

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 whenever writing, reviewing, or debugging .NET/C# code that uses the BallisticCalculator NuGet package (namespace BallisticCalculator) or its Gehtsoft.Measurements unit types — even when the user doesn't name the package explicitly but is clearly doing external/interior ballistics in .NET (bullet drop, velocity, energy, windage, spin drift, time of flight, zeroing a scope) for rifles, air rifles, bows, or artillery. Covers the full public API (Ammunition, Rifle, Atmosphere, Wind, ShotParameters, TrajectoryCalculator, TrajectoryPoint); standard, custom, .drg, and multi-BC drag tables; saving/loading data and building your own file format around it (BXml and System.Text.Json serialization); and building or rendering scope reticles (e.g. to SVG). Also use it for this library's Gehtsoft.Measurements Measurement<T> unit types (DistanceUnit, VelocityUnit, AngularUnit, WeightUnit, PressureUnit, …). Skip for: generic physics or projectile-motion exercises, a different ballistics library (e.g. GNU/JBM), game-engine or Unity projectile motion, plain unit conversions, and non-code ballistics questions (load-data or forensics advice). Self-contained: no access to the library source or binaries is required — prefer this over scanning the package to rediscover the API. Describes 1.1.13+, including the named ZeroRangeCantBeReachedException / TrajectoryCannotBeCalculatedException 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。

Analyze web traffic, SEO performance, and search engine trends using Google Search Console, Google Analytics 4, and Bing Webmaster Tools. Use when the user asks to analyze traffic, check SEO, or find keyword opportunities.

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 Codex must make a high-quality judgment on any complex problem, not only technical problems. Trigger when a question has high complexity, uncertainty, cost of being wrong, long-term impact, value tradeoffs, possible structural causes, or when the user asks for 全面思考, 深度理解, 系统性分析, 帮我想清楚, 从根上看, 这件事到底怎么看, 方法论, 复杂问题拆解, 系统方案, 架构判断, 策略判断, or 根因分析. Also trigger for project and engineering high-risk scenarios such as multi-module, multi-environment, multi-data-source, multi-state, financial truth, live trading, ledger, revenue, customer assets, order governance, broker interfaces, online availability, major solution selection, and system closure.

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 benchmarking, profiling, or A/B-testing CUDA kernels or end-to-end perf in the imp inference engine on RTX 5090 (sm_120), including refreshing tests/perf_baseline.json or publishing numbers to docs/BENCHMARKS.md and the README. Triggers on "benchmark kernel", "profile cuda", "ncu", "nsys", "kernel timing", "kernel sum", "occupancy", "bandwidth bound", "compute bound", "roofline", "perf baseline", "is this regression real", "decode dropped", "aggregate throughput", "two-image A/B", "prefill kernel A/B". Do NOT use for writing/optimizing kernel code (sm120-cuda-expert) or output-quality checks (check-degeneration).

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 verifying that a model in the imp inference engine produces coherent output without repetition loops, token-stuck states, or state corruption across turns or streams. Triggers on "degenerates", "check degeneration", "repetition loop", "own own own", "stuck token", "empty content", "multi-turn regression", "does it still work", "NIAH", and after enabling CUDA graphs / changing forward pass / MoE routing / KV cache or KV dtype / GDN state or scan / sparse attention / PDL / speculation (MTP, n-gram) / ragged prefill / batched-decode kernels (smallm, producer quantize) / FA2 softmax.

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, reviewing, or optimizing CUDA kernels targeting sm_120a (RTX 5090 / Consumer Blackwell, GB202) in the imp inference engine. Triggers on CUDA/PTX kernel code, shared-memory layout, bank conflicts, tensor-core MMA (mxf4nvf4, tf32, 3xTF32/3xFP16), GEMV/GEMM/attention/quantization/GDN-scan kernels, decode tok/s under expected, kernel emitting HMMA instead of mxf4nvf4, occupancy, register pressure, __launch_bounds__, spills, PDL. Pair with `benchmark-cuda` for measurement and `check-degeneration` after hot-path 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。

Use when imp outputs differ between two paths that should agree - chunk sizes, prefill vs decode, prefix-cache resend, two engines (imp vs llama.cpp vs HF), two builds or kernels - "output changes with chunk size", "first token differs", "logprobs moved", "which layer diverges", "who is right, imp or llama.cpp", "near-tie". Do NOT use for repetition loops or garbage output (check-degeneration), a new arch that loads wrong (add-model-arch), or perf (benchmark-cuda).

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。

Apply MCAF documentation guidance for docs structure, navigation, source-of-truth placement, and writing quality. Use when a repo’s docs are missing, stale, duplicated, or hard to navigate, or when adding new durable engineering guidance.

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。

Apply MCAF agile-delivery guidance for backlog quality, roles, ceremonies, and engineering feedback. Use when defining how the team plans, tracks work, and turns feedback into durable improvements.

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Visual vocabulary, design terminology, and prompt engineering strategy for Stitch. Reference this when you need layout pattern names, aesthetic style terms, color structure formulas, or device guidelines.

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。

Structures technology diligence with architecture review, code quality assessment, scalability analysis, and technical debt evaluation. Use when evaluating startup technology, assessing engineering teams, or reviewing technical infrastructure.

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Evaluates demand forecasts for toll roads, airports, and ports with independent engineer review and scenario sensitivity analysis. Use when analyzing traffic studies, validating demand forecasts, or stress testing revenue projections.

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。

Evaluates ML applications in investment with feature engineering, model selection, and implementation considerations for alpha generation. Use when evaluating ML for investing, designing ML pipelines, or assessing ML strategy feasibility.

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, reverse-engineer, or update living feature specs following the spec-anchored methodology.

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。

GEO+SEO audit of asdlc.io — analyzes the codebase for Generative Engine Optimization across AI citability, technical infrastructure, content quality, schema markup, and knowledge base discoverability. Produces a composite GEO Score (0-100) with prioritized action plan.

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。