Microsoft

Video-to-GIF conversion with FFmpeg two-pass optimization

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。

Model and document existing or planned software architectures with the C4 model across System Context, Container, and Component levels plus deployment diagrams, then emit diagrams through a selected renderer. Use when an architect needs audience-appropriate software architecture documentation; use the 'architecture-diagrams' skill for infrastructure topology.

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。

Canonical documentation capability for audit, drift, validate, and author modes in hve-core.

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, improve, refactor, replace, review, or validate prompts, instructions, agents, subagents, and skills. Use for Copilot customization cleanup, extending HVE workflows with project-specific capabilities, and parent-owned correction of material review findings.

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。

Authoring skill for Architecture Decision Records (ADRs) supporting capture, from-planner-handoff, and adopt-template entry modes with selectable Y-Statement or MADR v4.0.0 output templates, supersession lineage, and ASR trigger evaluation.

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。

Experiment design reference for problem-class framing, Minimum Viable Experiment coaching, hypothesis formation, vetting and red flags, and experiment readiness. Use when translating a stated business outcome into candidate data-science problem classes, or when framing, vetting, scoping, or evaluating an experiment of any kind, including data feasibility, architecture, LLM, performance, use-case, UX, prototyping, and hardware 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。

Ultra-compressed response style that reduces output token count while preserving technical accuracy, with intensity levels and auto-clarity safety rules

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。

Set up GitHub Copilot OpenTelemetry capture: configure the VS Code export settings, generate a local Grafana stack and dashboard, or generate the Azure collector, infrastructure, and dashboard for an organization.

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。

Foundational Python best practices, idioms, and code quality fundamentals

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Design Thinking learning curriculum covering nine progressive modules across the full Problem, Solution, and Implementation Space methods plus a shared manufacturing reference scenario for teaching and practice

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。

Privacy planning reference for data-flow reasoning, standards mapping, and DPIA thresholds

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。

Repeatable HVE Core demo setup that simulates DT Coach sessions with a customer persona and scaffolds a hi-fi prototype - Brought to you by microsoft/hve-core

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。

DataOps and DS/MLOps testing reference for data tiering, Bronze-to-Silver validation placement, pipeline invariants, pytest categories, and validation-versus-drift. Use when designing, reviewing, or generating data pipelines, transformation code, data validation, or data-science test suites.

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。

Jira issue workflows for search, issue updates, transitions, comments, field discovery, and interactive credential setup via the Jira REST API. Use when you need to configure Jira access, search with JQL, inspect an issue, create or update work items, move an issue between statuses, post comments, or discover required fields for issue creation.

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。

Manage GitLab merge requests and pipelines with a Python CLI

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Create or assess an evidence-grounded, falsifiable outcome hypothesis: a testable prediction of what measurable business result will change, for whom, by when, and how leading and lagging indicators will prove or disprove it. Use when framing measurable outcomes, turning an MVP, POC, feature, or technical initiative into a beneficiary result, defining targets and indicators, or judging whether evidence is strong enough to invest. Also applies to business outcome hypotheses, value hypotheses, and outcome statements.

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。

Machine learning experimentation reference for model-experimentation conventions, experiment tracking and reproducibility, dataset and model abstractions, ML engagement fundamentals, and model-production readiness. Use when standing up ML experimentation infrastructure or assessing whether a trained model is ready for production.

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Architecture diagram authoring for cloud infrastructure and declared data catalogs. Use when rendering Azure IaC or DS_CATALOG_V1 relationships as caller-selected ASCII or Mermaid diagrams.

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。

Authors Vally conformance tests for prompts, instructions, agents, and skills, and refuses jailbreak, prompt-injection, harmful-elicitation, TOS, CoC, and PII-extraction stimuli

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Build traceable internal-review proposal, RFI, RFP, tender, bid, and questionnaire responses from supplied questions and approved sources. Use to analyze questions, contribute business or product evidence, or draft qualified responses.

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。

Decision-driven HVE-Core installer with multiple clone-based and extension install methods, environment detection, and selective component installation

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。

Shared backlog conventions for Azure DevOps, GitHub, and Jira. Use for platform resolution, autonomy tiers, sanitization guards, and story quality, and for Azure DevOps pull request creation and build status or log retrieval.

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, update or review presentation-first HTML slide decks about HVE Core under slides/. Use for slide content research, deck styling, VS Code-style walkthroughs, presenter controls, or single-file HTML sharing in this repository.

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。

State, resume, reconstruction, job-lifecycle, transition, and flow-state mechanics for the Data Science and Engineering Coach. Loaded by the coach; not a user entry point.

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。