「cac」の検索結果

Use when the user says 'why is my checkout broken', 'audit my woocommerce store', 'cart problems woocommerce', or 'losing sales woocommerce'. Diagnoses checkout and cart failures, AJAX mismatches, caching conflicts, payment gateway setup, and SSL enforcement.

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Migrate a Mycelium project from legacy install (npx-degit, framework files in .claude/) to plugin install (framework lives in plugin cache, .claude/ holds project state only). Detects current install form, walks the user through plugin installation, runs the migration script, and verifies project state survived. Idempotent — safe to invoke on already-migrated projects.

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Use for APIs, servers and app databases. Triggers: REST, GraphQL, migration, query optimization, middleware, caching, queues, 백엔드, API 작업, 마이그레이션, 쿼리 최적화.

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Session-start lifecycle hooks for cross-plugin dependency resolution. Creates symlinks so cross-package references work in cached plugin environments.

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.

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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).

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Deconstructs unit economics with CAC, LTV, payback period, gross margin, and contribution margin analysis. Use when analyzing unit economics, validating SaaS metrics, or assessing business model efficiency.

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Evaluates sales efficiency with CAC payback, sales cycle analysis, channel economics, and pipeline conversion metrics. Use when assessing GTM efficiency, analyzing sales productivity, or evaluating distribution strategy.

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GraphQL expert including schema design, Apollo Client/Server, and caching

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。

Compress large context before reasoning to reduce token usage while preserving evidence. Use this whenever the user mentions huge files, long prompts, RAG payloads, prompt caching, expensive sessions, codebase context, chat history compaction, or wants the same answer quality with fewer tokens.

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Render a dashboard of the current Claude Code session — message counts, token usage, cache read/write, dollar cost, context %, lines changed, cost-per-turn sparkline, and a tool-call histogram — parsed from the session transcript JSONL by a bundled bash script. One bash call, zero analysis, zero confirmation. Optionally pass a session id to inspect a different session. Use when the user asks for session stats, usage, token or cost breakdown, or a session dashboard.

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 a GitHub Actions workflow for Android — JDK + Gradle caching, build, unit + instrumented tests, Android Lint, run the Android pre-flight checks, and (optionally) sign + upload a signed app bundle to a Play track. Use when setting up CI for an Android app.

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Generate a GitHub Actions workflow for iOS — macOS runner, SPM/DerivedData caching, build, test on a simulator matrix, run the iOS pre-flight checks, and (optionally) sign + upload to TestFlight. Use when setting up CI for an iOS app.

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Run GGUF models directly, load LoRA adapters, benchmark inference speed, and serve models via llama-server using llama.cpp. Includes Qwen 3.5 serve scripts (9B dense + F16, 35B MoE) with asymmetric KV cache and thinking mode. Secondary to Ollama; use when needing direct model control or LoRA hot-loading. Triggers on 'llama.cpp', 'GGUF', 'LoRA adapter', 'benchmark inference', 'llama-server'.

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This skill enables cross-model dialogue between Claude and Gemini with shared visual memory. Use when the user wants to generate images, have visual dialogues with AI, create scientific illustrations with continuity, or have multiple AI perspectives respond to the same prompt. Key trigger phrases: "generate an image", "visual dialogue", "ask the daimones", "resonance field", "Minoan tarot", "cross-model", "KV cache", "MESSAGE TO NEXT FRAME".

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Search and download images from 15 APIs in parallel (Openverse, Pexels, Pixabay, Met Museum, Cleveland Museum, Art Institute of Chicago, Getty Museum, NASA, Rijksmuseum, Wikimedia Commons, Unsplash, Smithsonian, Europeana, Iconify, Pollinations AI). Supports license filtering, presets (military, museum, stock), cached results, and bulk download with metadata sidecars. Triggers on find images, stock photos, public domain artwork, museum images, CC-licensed, NASA photos, icons.

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Analyze and tune a running llama.cpp server for local coding agents: propose config (ctx, prompt cache, MTP), benchmark before/after, report deltas. Use to optimize llama-server for Claude Code or Aider. Don't use for vLLM, ollama, or non-llama.cpp.

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Summarize videos, audio files, and podcasts into key takeaways. Give it a YouTube/podcast URL or a local audio/video file — it transcribes locally (cached; repeat requests reuse the transcript) and distills the transcript into key points, summaries, or translations. Use when the user asks to summarize, get key points from, translate, or TL;DR any audio or video content.

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Use when the user wants to get found in Google or AI engines (ChatGPT, Perplexity, AI Overviews) — keyword and content strategy, technical or content SEO audits, schema markup, link building, topical clustering, E-E-A-T, AI citation (AEO), or deciding whether SEO is even the right channel for their startup. Calibrated for early-stage, low-authority sites. Not for paid acquisition or channel-mix/CAC analysis (use channel-expert), or writing the page copy itself (use copywriter-skill).

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Use when a SaaS founder is deciding on or building a product-led growth motion — assessing PLG readiness, choosing freemium vs free trial vs reverse trial, defining activation and PQLs, designing self-serve onboarding and conversion triggers, building viral loops, or setting up PLG metrics (NRG, activation rate, TTV, NDR, CAC payback) and cohort analysis. Also for hybrid product-led-sales design and PLG-vs-sales-led GTM decisions. Not for paid acquisition channel mix (use channel-expert) or the mechanics of price points and tiers (use pricing).

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Use when the user is deciding, reviewing, or second-guessing a software architecture or tech-stack choice — monolith vs microservices vs serverless vs event-driven, backend language, database type, caching, CQRS/event sourcing, polyglot, or a migration like Strangler Fig. Triggers on "should I use microservices", "which database", "tech stack for", "system design", or "architecture review". For how to deploy, operate, and scale that design (CI/CD, cloud, Kubernetes, IaC), use devops.

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Use when the user needs to find, prioritize, or engage B2B prospects — defining an ICP, tiering accounts, building or enriching lists, reading buying signals, designing multi-channel cadences, cold calling, LinkedIn outreach, routing inbound leads, or diagnosing weak pipeline generation. Also for live prospecting coaching. Not for closing (use sales-skill), copy (use copywriter-skill), or CAC analysis (use channel-expert).

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Probe the runtime state of a named feature flag in a CLI binary. Covers the four-pronged evidence protocol (binary strings, live invocation, on-disk state, platform cache), the four-state classification (LIVE / DARK / INDETERMINATE / UNKNOWN), gate-vs-event disambiguation, conjunction-gate handling, and skill-substitution scenarios where a flag appears DARK but the capability is delivered by other means. Use when verifying whether a documented or inferred capability has rolled out, when auditing dark-launched features, or when a prior probe's conclusions need refreshing against a new binary version.

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Achieve distributed agreement without central authority using bee democracy, threshold voting, and quorum sensing. Covers proposal generation, advocacy dynamics, commitment thresholds, deadlock resolution, and consensus quality assessment. Use when a group must decide between options without a designated leader, when centralized decision-making is a bottleneck, when stakeholders have different perspectives to integrate, or when designing automated systems that must reach consensus such as distributed databases or multi-agent AI.

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。