「tradeoff」の検索結果

Golang library and framework selection — vetted production-ready options by category (web, database, testing, logging, messaging), new and experimental stdlib packages, standard-library-first tradeoffs, and maturity signals (maintenance, license, importer counts). Apply when the user asks for library suggestions, wants to compare alternatives, needs to choose a library for a specific task, or when a new dependency is being added to the project. Not for a specific library's API once chosen (→ See that library's dedicated skill, e.g. `samber/cc-skills-golang@golang-samber-lo`), nor for go.mod mechanics, upgrades, or vulnerability audits (→ See `samber/cc-skills-golang@golang-dependency-management` skill).

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[omh] Team or business decision between options: tradeoffs, a recommendation, and a decision note you can act on. Use when the user says: strategy-brief, strategy brief, strategy memo, product strategy, strategic options, decision note, leadership strategy, next strategy.

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Write an MVP technical design from agreed requirements, including architecture choices and relevant tradeoffs.

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Explores the problem and the range of possible approaches before any code is written — clarifying what is actually being asked, surfacing options with their tradeoffs, and converging on one. Use this at the start of any feature, component, or behavior change, when a request is ambiguous, when the first idea is the only idea on the table, or when a design decision needs pressure-testing before commitment.

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Owns architecture, engineering delivery, infrastructure, data platform, and internal systems. Use this for build-versus-buy calls, technology selection, architectural direction, engineering capacity and delivery risk, technical debt tradeoffs, platform and tooling decisions, or when a technical choice has business consequences that need stating in business terms. Also use to judge whether a technical plan is sound before it is committed to.

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Use for 'why does X work this way', 'why we picked Y', design rationale, regressions, postmortems, or data-backed thresholds. Discovers available MCPs and queries each evidence category (source control, issue tracker, long-form docs, real-time chat, infrastructure observability, error tracking, product analytics warehouse) in parallel, then returns a cited read on decisions and tradeoffs. Use how for runtime behavior.

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Apply when product, UX, or feature-scope tradeoffs come up. Choose user delight over implementation convenience; ship fewer polished features over more rough ones.

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Redesign GitHub README homepages or create project-native pure SVG, hybrid SVG-composed PNG/WebP, and opt-in animated GIF assets. Use when a user asks to beautify, redesign, rebrand, visually upgrade, simplify, or audit a GitHub README; create only a hero, section headers, diagrams, badges, motion graphics, showcase modules, or other README assets; or turn a repository homepage into a cohesive visual story. If whole-README work versus asset-only work is unclear, ask which scope the user wants. For hero-like assets where pure SVG and generated raster material are both viable, explain the tradeoffs and confirm the implementation before creating the asset.

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Teaches how to reason from a transaction protocol’s rules to a working model. Use when analyzing a concurrency-control paper, spec, or algorithm to identify state, metadata, invariants, operation rules, examples, counterexamples, guarantees, false aborts, unsafe commits, or tradeoffs before inspecting concrete traces.

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Resolve model profile (quality/balanced/budget) at orchestration start and map agents to specific models. Enables cost/quality tradeoffs by selecting appropriate AI models for each agent role.

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Use for 'why does X work this way', 'why we picked Y', design rationale, regressions, postmortems, or data-backed thresholds. Discovers available MCPs and queries each evidence category (source control, issue tracker, long-form docs, real-time chat, infrastructure observability, error tracking, product analytics warehouse) in parallel, then returns a cited read on decisions and tradeoffs. Use how for runtime behavior.

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Apply when product, UX, or feature-scope tradeoffs come up. Choose user delight over implementation convenience; ship fewer polished features over more rough ones.

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 an Architecture Decision Record (ADR) for any technical decision. Use when asked to document a technical decision, write an ADR, record an architecture choice, or capture why a technology or approach was selected. Produces a structured ADR with context, decision, consequences, and tradeoffs.

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Help users navigate complex product decisions by weighing competing priorities and calculating long-term value. This skill covers build-vs-buy logic, technical debt evaluation, and communicating tradeoffs to senior leadership.

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Evaluate system boundaries and architectural tradeoffs. Use for architecture decisions, design reviews, and ADRs.

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Architecture specialist for software/system design, module and service boundaries, tradeoff analysis, and stakeholder synthesis. Uses context-aware methods such as diagnostic routing, design-twice comparison, ATAM-style risk analysis, CBAM-style prioritization, and ADR-style decision records.

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Calculates statistical power for high-dimensional genomics experiments (bulk RNA-seq, scRNA-seq, ATAC-seq, ChIP-seq, methylation, proteomics) under negative-binomial count models using RNASeqPower, PROPER, and simulation via powsimR, distinguishing per-gene from marginal (transcriptome-wide) power, the role of mean expression and dispersion, and the sequencing-depth-versus-replicate tradeoff. Covers simulation as the honest default for overdispersed counts, FDR-aware average power versus single-test power, observed/post-hoc power as an anti-pattern, and the winner's-curse / Type-S / Type-M consequences of underpowering. Use when planning replicate number for a sequencing experiment, deciding whether to add depth or samples, choosing closed-form versus simulation power, estimating power from pilot dispersions, or justifying replication in a grant. For clinical-trial power see clinical-biostatistics/power-and-sample-size; for the inverse sample-size question see experimental-design/sample-size.

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Running experiments out of the data warehouse instead of via dedicated experiment platforms. SQL-based assignment, exposure logging discipline, metric definitions in dbt models, statistical analysis in SQL or Python, variance reduction with CUPED, sequential testing, and the operational tradeoffs vs platforms like Statsig and Optimizely. Triggers on warehouse-native experimentation, run experiments in BigQuery, run experiments in Snowflake, dbt experiments, SQL t-test, CUPED variance reduction, exposure log, sample ratio mismatch, sequential testing, mSPRT, doubly robust estimation, build vs buy experimentation. Also triggers when the team is choosing between platform and warehouse, building warehouse-native experiment infrastructure, auditing one, or running an experiment with a custom metric the platform cannot handle.

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Upscale and restore video in ComfyUI. Both the quick local path (per-frame ESRGAN like 4x_foolhardy_Remacri via ImageUpscaleWithModel + 4x→2x supersample, with its temporal-flicker tradeoff) and temporal-aware super-resolution (SeedVR2, the newer FlashVSR) with the downscale-first restore pipeline; RIFE/FILM frame interpolation via the BUILT-IN ComfyUI 0.26 FrameInterpolate (rife_v4.26 in models/frame_interpolation/) or the ComfyUI-Frame-Interpolation pack; 2x/4x scaling, VRAM tiers, VHS encode. Captures the classic downscale→SeedVR2→RIFE recipe and the current 2026 recommendation.

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Expert guidance for designing, assessing, and optimizing Azure workloads using Azure Well Architected. Covers design review checklists, recommendations, design principles, tradeoffs, service guides, workload patterns, and assessment questions. Use when designing AI, HPC, SaaS, AVD/AVS workloads, choosing regions/AZs, or configuring core Azure services, and other Azure Well Architected related development tasks.

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Architectural style selection decision matrices, trade-off analysis, structural enforcement rules, and combination patterns. Load when choosing or evaluating architecture styles.

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Analyze a codebase for high-leverage architectural improvements and explain evidence, tradeoffs, and next steps. Use explicitly for architecture audits or deep-module refactoring opportunities.

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Identify negotiable contract terms and draft options with commercial tradeoffs and legal review questions. Use for this Bot Generator Bot template; creates drafts or simulations within its declared scope.

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Use when working with this machine's local LLM setup (LM Studio + mlx-dspark) -- checking status, changing context window, diagnosing a reasoning hang or dead request, understanding RAM/speed tradeoffs, or wiring a new script to the local inference endpoint.

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。