「decision」の検索結果

World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.

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This skill should be used at the start of any computationally intensive scientific task to detect and report available system resources (CPU cores, GPUs, memory, disk space). It creates a JSON file with resource information and strategic recommendations that inform computational approach decisions such as whether to use parallel processing (joblib, multiprocessing), out-of-core computing (Dask, Zarr), GPU acceleration (PyTorch, JAX), or memory-efficient strategies. Use this skill before running analyses, training models, processing large datasets, or any task where resource constraints matter.

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Access ClinPGx pharmacogenomics data (successor to PharmGKB). Query gene-drug interactions, CPIC guidelines, allele functions, for precision medicine and genotype-guided dosing decisions.

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Ingest external plans with conflict detection against project decisions before writing anything.

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Extract decisions, lessons, patterns, and surprises from completed phase artifacts

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Convene an AI executive board of directors (CEO, CFO, COO, CLO, CISO sub-agent personas) to vet a business idea, product concept, new service offering, M&A target, or operational initiative — and deliver an integrated board memo with a Go/No-Go recommendation. Use this skill whenever the user wants an idea vetted, stress-tested, or reviewed from multiple executive perspectives; asks to "present this to the board," "run this by the boardroom," "vet this idea," "poke holes in this plan," or "prep me for a board meeting"; or shares a business plan, pitch, proposal, or initiative document and asks for structured executive feedback. Also trigger when the user asks for a Go/No-Go decision, risk review across finance/legal/security/operations, or preparation for presenting an initiative to real leadership.

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Look up official macroeconomic releases, central-bank policy rates, release calendars and FX rates for 22 currencies through the FXMacroData MCP server. Use for questions about CPI, GDP, jobs data, rate decisions, bond yields or what data is due next, when the answer should come from the published number rather than news coverage. Remote server at https://mcp.fxmacrodata.com/mcp; USD works without a key.

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Install or repair the FoundationPose perception pipeline and build its FoundationStereo TensorRT engines. Use for SAM3/TAO dependency conflicts, CUDA library failures, and depth-engine shape or precision decisions.

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Hardware compression offload with DOCA Compress on a BlueField DPU, ConnectX NIC, or host: DEFLATE compress and decompress, plus LZ4 decompress (stream and block; LZ4 encode is NOT supported), instead of CPU zlib / zstd. Trigger on "offload this gzip", "decompress incoming network data on the NIC", "compress task returns INVALID_VALUE on alloc_init", "submitted a task but no completion arrives", or "decompress LZ4 on the BlueField", even without naming DOCA Compress. Covers enabling compress-deflate, decompress-deflate, decompress-lz4-stream and decompress-lz4-block tasks on a doca_compress context, sizing source / destination doca_buf against the per-task cap query, mmap permissions, offload vs CPU decisions, a round-trip smoke, and DOCA_ERROR_* from Compress calls. Refuse and route elsewhere for zstd / Snappy / brotli, LZ4 encode (a CPU LZ4 library), pure mmap-to-mmap copies (doca-dma), or DOCA Core lifecycle internals.

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LP, MILP, QP — concepts, problem-text parsing, and formulation patterns (parameters, constraints, decisions, objective). Concepts only; no API.

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Recall past facts, preferences, and decisions from ReMe; inspect memory delivery, service health, and the consolidation schedule; or consolidate memory when requested.

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Recall past conversations, preferences, project history, and decisions from ReMe in Claude Code, or check the ReMe connection.

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Explain and apply the foundational LLM Wiki architecture and knowledge-management strategy. Use for setup, architecture, schema, or organization decisions; operational ingest/query/lint work uses dedicated skills.

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Diagnose and repair CCB ask/job/message/reply/artifact/callback lineage. Use for missing replies, incomplete artifacts, pending callbacks, retry/resubmit/ack decisions, reply delivery problems, or work-chain resume advice.

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Get research-backed architecture advice for your knowledge system. Describe your use case, constraints, and goals — get specific recommendations grounded in TFT research with rationale for each decision. Triggers on "/recommend", "what would you recommend", "architecture advice", "knowledge system for".

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Idiomatic Golang design patterns — functional options, constructor APIs, `init()` and global-state avoidance, enums, panic vs error decisions, resource management and lifecycle, graceful shutdown, timeouts and retries, streaming and iterators, and architecture styles (clean, hexagonal, DDD, flat). Apply when choosing between architectural patterns, implementing functional options, designing constructor APIs, setting up graceful shutdown, applying resilience patterns, or asking which idiomatic Go pattern fits a specific problem. Not for wiring a DI container or comparing DI libraries (→ See `samber/cc-skills-golang@golang-dependency-injection` skill), nor for error wrapping, `errors.Is`/`As`, or logging mechanics (→ See `samber/cc-skills-golang@golang-error-handling` skill).

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Go (Golang) naming conventions — covers packages, constructors, structs, interfaces, constants, enums, errors, booleans, receivers, getters/setters, functional options, acronyms, test functions, and subtest names. Use this skill when writing new Go code, reviewing or refactoring, choosing between naming alternatives (New vs NewTypeName, isConnected vs connected, ErrNotFound vs NotFoundError, StatusReady vs StatusUnknown at iota 0), debating Go package names (utils/helpers anti-patterns), or asking about Go naming best practices. Also trigger when the user mentions MixedCaps vs snake_case, ALL_CAPS constants, Get-prefix on getters, or error string casing. Do NOT use for general Go implementation questions that don't involve naming decisions.

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Turns an answer into a one-page visual HTML explainer: the model writes a short Markdown draft and the bundled CLI builds one page. Use it proactively and liberally, without being asked, whenever a page would help the reader more than plain text, even if plain text would also work: any explanation of how something works or how parts relate (flow, request path, architecture, code or folder structure, state machine, lifecycle, history); any comparison, trade-off or decision with pros and cons; any diagnosis, review or investigation, especially with several causes or findings to rank; any answer with a table, a numbered or ranked list, steps, branches or several sections; or when the user says "I don't get it / draw it / explain visually / 讲讲原理 / 没看懂 / 画个图". When in doubt, use it: a page is quick and cheap to make. Also for explainer videos ("make a video", "做个视频") and for changing its settings. Skip only for small talk, a trivial one-line answer, or when the user asks for plain text.

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[omh] Recurring market or topic research: research operations department - coordinate Scout, Analyst, and Briefer work with source-inbox and status boundaries; for one decision brief use research-brief, and for typed candidates before research starts use source-finder. Use when the user says: research-department, research department, research ops department, research operations department, scout analyst briefer, scout analyst brief, daily research department, competitor research department.

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[omh] Gathering candidate papers, datasets, or repos: source candidate inventory - prepare typed source candidates and acquisition status before downstream work; use ulw-research to fetch and cite them, or research-brief to turn them into a decision-ready brief. Use when the user says: source-finder, source finder, source acquisition, source intake, find papers and datasets, find datasets and repos, find papers, find arxiv link.

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[omh] App idea headed for launch: shape an app idea into decisions, delivery handoff, verification, release, and monitoring status. Use when the user says: idea-to-deploy, idea to deploy, from idea to deploy, plan to deploy, idea to launch, ship this idea, ship this feature, launch this feature.

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[omh] Churn or weak activation among users: turn an observed onboarding, activation, retention, re-engagement, referral, or monetization problem into one consent-safe in-app journey or growth experiment plan with a bounded readout and an explicit decision. Use when the user says: lifecycle-growth, lifecycle growth, lifecycle marketing, lifecycle messaging, in-app journey, in-app message campaign, onboarding journey, onboarding nudge.

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[omh] Self-hosted LLM serving on GPUs: choose the serving engine and quantization from decision tables, prepare deployment as an idempotent runbook with observed-only verification, and measure the endpoint with the standard TTFT/TPOT/goodput protocol. Use when the user says: inference-serving, inference serving, serve this model, serve the model, model serving, serving endpoint, vllm, llama.cpp.

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[omh] PRD or roadmap priorities to decide: turn product evidence into a decision-ready PRD, prioritization frame, and roadmap brief. Use when the user says: product requirements document, PRD, roadmap prioritization, 제품 요구사항 문서, 제품 기획서, 로드맵 우선순위.

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。