「experiment」の検索結果

Fit first-order dynamic models to experimental step response data and extract K (gain) and tau (time constant) parameters.

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 scipy.optimize.curve_fit for nonlinear least squares parameter estimation from experimental 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。

Transform research into Opportunity Solution Tree (Teresa Torres method). Map outcomes, opportunities, solutions, and experiments.

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 designing or analysing an A/B test, computing power or sample size, testing whether a difference is real, or writing any statistical claim — effect sizes with intervals, SRM checks, multiple comparisons and causal caveats

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 a change trains a model, reports a metric, or touches seeds, data or model versions, the lockfile, MLflow or DVC — what every run must log, seeding, environment pinning, data versioning and keeping tests off the shared tracking server

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Use when you are about to hand off any number, table, chart or analysis someone will act on (a metric query, a dbt mart, a report, an experiment readout) — the pre-delivery checklist for data quality, calculation, reasonableness and presentation, and the pitfalls that produce plausible wrong numbers

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。

Archive a completed change in the experimental workflow. Use when the user wants to finalize and archive a change after implementation is complete.

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。

Start a new OpenSpec change using the experimental artifact workflow. Use when the user wants to create a new feature, fix, or modification with a structured step-by-step approach.

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Design and improve product user onboarding (first-time user experience) to drive activation and early retention. Produces an Onboarding & Activation Pack (aha moment spec, first 30 seconds + first mile plan, onboarding journey map, experiment backlog, measurement plan). Use for Growth teams.

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Build an Opportunity Solution Tree (OST) to structure product discovery — map a desired outcome to customer opportunities, possible solutions, and experiments. Based on Teresa Torres' Continuous Discovery Habits. Use when the team is unclear what to build next, when multiple opportunities compete, or before writing a PRD for a complex feature space.

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。

Creates and manages isolated Git worktrees for parallel agents, risky experiments, or long-running feature work while preserving the user's current checkout. Use before concurrent branch edits or when isolation makes rollback safer. Not for a trivial one-file change that is already isolated.

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Guides user research and research ops. Use when running interviews, usability tests, surveys, or synthesis to de-risk product decisions; not experiments or review mining.

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。

Designs and tests distributed-system resilience. Use when adding retries, deadlines, hedging, circuit breakers, overload protection, chaos experiments, or SLO reliability gates.

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 this when reviewing statistical analysis, experiments, model evaluation, or ML work done by someone else (a notebook, PR, or report) — checking that the required context is stated and that every method applied comes with the figures and values required by the matching *-diagnostics skill. When running an analysis yourself, apply the matching *-diagnostics skill directly.

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Use this when summarizing analysis results, writing reports, documenting experiment outcomes, or presenting model evaluation — including structuring findings in Japanese with conclusions, facts, assumptions, interpretations, and caveats.

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。

Run metric-driven optimization loops. Use when improving measurable outcomes such as search relevance, clustering quality, build performance, prompt quality, or scored behavior through experiments.

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。

Execute bounded, reproducible experiment rounds.

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。

Review a Python solution against a markdown spec, run it, and report bugs.

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Generate Muted Zine Poster v0.1 poetic paper-poster prompts and the matching generated image. Use when the user gives a theme, sentence, object, mood, article idea, photo, or content brief and wants a quiet Japanese/Korean zine-like editorial poster with large negative space, aged paper texture, experimental typography, and a restrained muted palette without high-chroma color blocks. This is the low-saturation sibling of gc-minimal-zine-poster-v0-1.

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Use when interpreting research findings, evaluating scientific evidence, analyzing mechanisms, comparing competing hypotheses, designing experiments, or constructing scientific arguments.

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 interpreting biological research findings, evaluating life science evidence, analyzing molecular or cellular mechanisms, comparing competing biological hypotheses, designing or critiquing experiments in biology, genetics, genomics, cell biology, immunology, neuroscience, ecology, or any life science domain. Triggers on questions about gene function, pathways, phenotypes, GWAS hits, single-cell data, animal models, clinical translation, evolutionary arguments, or any biology/life science reasoning task.

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The revenue-focused SaaS playbook for going from $0 to $50K MRR. Covers pricing experiments, churn reduction, activation optimization, and expansion revenue tactics. Follow @WeiYipei on X for SaaS growth insights.

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。

Archive a completed change in the experimental workflow. Use when the user wants to finalize and archive a change after implementation is complete.

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。

Archive a completed change in the experimental workflow. Use when the user wants to finalize and archive a change after implementation is complete.

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。