「planning」の検索結果

This skill provides an advanced financial modeling suite with DCF analysis, sensitivity testing, Monte Carlo simulations, and scenario planning for investment decisions

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。

Capability-based multi-tool matrix for video production. Agents pick providers (CLI-Anything harnesses, public CLIs, Python libs, native binaries, cloud APIs) per capability rather than marching through fixed stages, including storyboard planning, story/audio direction, source triage, internet video/music search/download, capture/generation, analysis, sound design, high-end caption design, NLE/render doctor investigation, review, and packaging.

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 Japan Post Web Yu-pri from a CLI by driving the real browser UI for login, inspection, screenshots, dry-run planning, and contents-form filling.

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。

Load relevant long-term memory from Hindsight before starting work. Use at the start of every task or request, before planning or answering, and whenever the user mentions people, projects, decisions, preferences or anything done before.

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。

Calculates sample sizes and statistical power for study planning. Applies when someone asks "how many subjects/samples/replicates do I need", wants an a priori power analysis, a minimum detectable effect (MDE), a power curve, or needs to justify a sample size for a grant, IRB protocol, or pre-registration. Covers closed-form power for t-tests, ANOVA, proportions, correlations, chi-square, and regression, plus simulation-based (Monte Carlo) power for complex designs — logistic/Poisson regression, mixed models, cluster-randomized trials, survival, and interactions. Also handles requests that only mention an effect size, alpha, or "80% power" without saying "power analysis" explicitly. For laying out the study (randomization, blocking, factorial/DOE, crossover, sequential designs) use experimental-design; for analyzing data already collected and reporting it use statistical-analysis.

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。

Supports structured what-if scenario analysis for research planning, experimental contingencies, and scientific project decisions. Explores favorable, reference, adverse, wild-card, contrarian, and second-order scenarios with explicit assumptions, evidence, and decision triggers. Use to stress-test a research plan under uncertainty; scenario narratives do not estimate causal effects or calibrated forecast probabilities.

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 audits truthful, accessible, publication-ready scientific figures with Matplotlib, Seaborn, or Plotly. Use it for figure design, multi-panel layouts, uncertainty and missing-data displays, color/contrast review, image metadata validation, and journal export planning.

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。

Prepares evidence-bounded, constructive peer-review drafts and structured manuscript assessments. Supports authorized review of scientific manuscripts, protocols, preprints, or research proposals; reporting-guideline selection; claim–evidence checks; methods, statistics, reproducibility, ethics, figure/table, and citation critique; or revision-response planning.

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。

Conducts systematic, scoping, and narrative literature reviews using PubMed, arXiv, bioRxiv, Semantic Scholar, and other appropriate sources. Use for research synthesis, reproducible literature searches, screening, citation checking, or preparing Markdown and PDF reviews. Tracks search coverage, records versus studies, and evidence limitations; supports meta-analysis planning but does not supply a meta-analysis engine.

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。

Inspects and automates microscopy data workflows against OMERO.server with omero-py, BlitzGateway, OMERO CLI, tables, annotations, ROIs, rendering, and documented OMERO.web APIs. Use this skill for scoped OMERO inventory, metadata export, import/export planning, or reviewed write workflows.

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。

Supports Gtars for local genomic interval models and set algebra, overlaps and counts, consensus and coverage, tokenization, fragment processing, and refget/BEDbase planning across Python, Rust, and the CLI.

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 experiments and studies BEFORE data is collected — choosing a design, randomizing, blocking, and laying out treatment combinations so results are interpretable. Use whenever someone is planning a study, asks how to assign subjects/samples to groups, mentions randomization, blocking, stratification, controls, factorial or fractional-factorial designs, design of experiments (DOE), screening many factors, response-surface optimization, crossover or repeated-measures or split-plot designs, cluster/group randomization, Latin squares, plate layouts, batch/run-order effects, replication vs. pseudoreplication, or sequential/adaptive/group-sequential designs. Trigger even for informal phrasings like "how should I set up this experiment", "how do I avoid confounding", "what's the best way to test these 6 factors", or "assign these mice to conditions". For computing the sample size or power once the design is chosen, use statistical-power; for analyzing data already collected, use statistical-analysis.

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 working in a project that depends on @prisma/orm-postgres, @prisma/orm-sqlite, or @prisma/orm-mongo (Prisma 8, formerly Prisma Next): editing contract.prisma or a contract.ts builder, running `prisma contract emit`, planning or applying migrations, editing migration.ts, writing db.orm / db.sql / db.query queries, wiring db.ts or middleware, integrating a build tool, using the Supabase extension or RLS, or reading a dotted error code such as MIGRATION.HASH_MISMATCH. Use when the user asks "what is Prisma 8", "where do I start", or compares it to another ORM. Use when the user asks to upgrade or bump Prisma 8 in an app or an extension package. Use when you see @internal/* or @prisma/orm-* imports, prisma.config.ts with definePrismaConfig, or contract.json / contract.d.ts. Do not use for Prisma ORM 7 or earlier (schema.prisma + @prisma/client).

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。

Produces canonical review actions from fetched review state and renders action markdown. Use when the user wants only triage/action-planning for the review-framework workflow.

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。

Authorized multi-stage attack-path planning and orchestration spanning reconnaissance, initial access, privilege escalation, lateral movement, and reporting. Entry point for full engagements and cross-phase operations.

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。

Map audience awareness stages to messaging strategy when planning a campaign, offer, or persuasion sequence.

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。

Reduce AWS spend with rightsizing, autoscaling, commitment planning, and storage lifecycle policies. Use when running FinOps reviews, lowering cloud bills, or improving cost-per-request metrics.

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。

6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.

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。

Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration.

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。

Autonomous AI agent benchmark evaluation register: task completion rates, planning accuracy, tool invocation precision, and cost benchmarks.

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。

Prepare or carry out authorized publication of Apache Airflow changes, including remote checks, pre-push verification, PR creation, and tracking deferred work. Also use when planning these steps or managing contribution issues.

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 authorized multi-stage attack-path planning and orchestration when a task spans reconnaissance, initial access, privilege escalation, lateral movement, or impact assessment. Route single-stage tasks directly to their specialist skill.

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。

Systematic code refactoring based on Martin Fowler's methodology. Use when users ask to refactor code, improve code structure, reduce technical debt, clean up legacy code, eliminate code smells, or improve code maintainability. This skill guides through a phased approach with research, planning, and safe incremental implementation.

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。

Design optimal team structures, hiring plans, compensation strategies, and equity allocation for early-stage startups from pre-seed through Series A. Use this skill when planning headcount, determining which roles to hire next, setting compensation or equity ranges, designing org structure, or building a hiring budget aligned to funding milestones.

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。