'usd' 검색 결과

Knowledge base from the DoD Test & Evaluation Enterprise Guidebook (OUSD R&E, August 2022). Use for DoD test and evaluation (T&E) across the Adaptive Acquisition Framework — the four test communities (contractor testing, developmental DT&E, operational OT&E, live-fire LFT&E); the five operational events (Operational Demonstration, EOA, OA, IOT&E, FOT&E) and the OTRR gate; the T&E Strategy / TEMP and its IDSK, with the upstream documents that feed it and the test plans that decompose it; the T&E organizations and oversight roles (USD(R&E), DOT&E, TRMC, the Service Executives and OTAs, the T&E WIPT, CDT); and pathway-specific T&E for all six acquisition pathways (Urgent Capability, Middle Tier, Major Capability, Software, Defense Business Systems). Statutory anchors include 10 U.S.C. §4171 (IOT&E) and §4172 (LFT&E/FUSL). This is guidance, not policy: it points outward to DoDI 5000.89 and the DoDI 5000-series for binding detail, and is thin on detailed test statistics/design-of-experiments methodology, cost estimating, the Acquisition of Services pathway, and any CUI/CAC-gated companion guides.

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Autonomer Langlauf-Modus ohne laufende Rückfragen — Startbedingungen (Ziel, Scope, Budgets, Checkpoints), Entscheidungsstufen und Abbruchregeln für unbeaufsichtigte Läufe; erhöht die Ausdauer, nicht die Berechtigung. Nutzen, wenn ausdrücklich ein längerer autonomer Lauf gewünscht ist.

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ScalarDB Cluster および ScalarDB Analytics のアーキテクチャ、サイジング、構成を見積もるスキル。 性能要件、可用性要件、クラウド環境からScalarDB Cluster Pod数、Kubernetes構成、 バックエンドDB、API Gateway、監視システム等の全体構成を見積もる。 ScalarDB Analyticsを使用する場合はEMR/Databricksのサイジングも含む。 使用タイミング: - 「ScalarDBのサイジングを見積もりたい」「ScalarDB環境を構築したい」 - 「ScalarDB Clusterの構成を決めたい」「ScalarDBの費用を算出したい」 - 「開発/テスト/ステージング/本番環境のScalarDB構成」 - CI/CD、Blue/Green、Canary Deploymentを含む本番環境設計 - 「ScalarDB Analyticsを使いたい」「分析クエリ環境を構築したい」 - 「EMR/Databricksのサイジングを見積もりたい」 出力: Markdown形式の見積もり結果 + HTML形式のレポート 費用: USD/JPY両建て（為替レート明記）

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Distills enduring architectural decisions and design rationale from wave orchestration plans or historical git changes into structured, tracked Architecture Decision Records (ADRs). Use pre-wave to resolve active architectural constraints or post-wave to record enduring decisions in docs/decisions/.

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Incrementally studies git history across tagged or pattern-matched intervals (e.g., creation of planning artifacts like EXECUTION_PLAN_N.md, release tags, or milestone commits). Extracts commit diffs, file churn, and subsystem shifts to track feature evolution and architectural convergence, providing structured feeds for ADR distillation.

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Design and run reproducible benchmarks. Use when measuring performance scaling, comparing branches, or analyzing speedup metrics.

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Standardizes Makefile-based build and test workflows with reproducible targets. Use when creating or refining helpful build, test, and clean targets.

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Investigate a question against high-trust primary sources and capture the findings as a Markdown file in the repo. Use when the user wants a topic researched, docs or API facts gathered, or reading legwork delegated to a background agent.

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Performs architectural code reviews focusing on design patterns, duplication, circular dependencies, and refactoring impact. Is the design consistent with the codebase? Use when reviewing code changes for architectural concerns, code smells, or structural issues.

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Installs skills from aigb-skills into a coding agent's skill directory. Use when setting up aigb-skills for Claude Code, Cursor, Copilot, or Pi.

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Verifies bug fixes are correct and complete by tracing callers, checking imports/exports, validating path resolution, and confirming no regressions. Does the code work? Use when reviewing code changes for functional correctness.

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Reads Copilot conversation history and extracts executive-ready success stories. Use when the user asks to study prior Copilot sessions, summarize what was done before, mine evidence, or quantify time, cost, risk, and priority alignment from GitHub Copilot history in any workspace or repository.

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Create and maintain AGENTS.md files for project onboarding and grounding. Use when initialized in a new project, when project structure changes significantly, or when creating subfolder-level AGENTS.md files for complex monorepos.

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Captures web sources and compiles deterministic Markdown reference bundles with metadata and checksums. Use when asked to capture URLs, research web links, bundle search results, or archive web pages into an LLM-ready reference document.

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Interactive guided walkthrough of code, line by line, where the agent reflects back your explanations without analysis. Use when debugging, reviewing, or understanding code by forced articulation.

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Enforces lean, surgical output. Suppresses conversational fluff and verbose reasoning traces. Use when agent output feels too chatty or repetitive.

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Plain language directive. Cuts verbosity, corporate jargon, and overused AI vocabulary. Use when you want clear, direct, short writing. No fluff, no hedging, no unnecessary complexity.

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Processes JSON on the command line with jq. Use when parsing, filtering, or validating JSON from files or APIs.

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Validates code fixes by running the code, checking compilation, verifying runtime behavior, and confirming fixes resolve the reported issue. Use when verifying that bug fixes actually work at runtime.

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Grill the user relentlessly about a plan, decision, or idea. Use when the user wants to stress-test their thinking, or uses any 'grill' trigger phrases.

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Develops an implementation plan ready for wave-orchestration to execute. Takes the user's goal or rough plan as input, mines the codebase for facts, grills the user one question at a time for the decisions, drafts the planning/ artifacts (task DAG, wave table, worker prompt template, state file), then reviews its own plan on two axes before seeking approval. Use when the user wants to turn a feature idea, master plan, or backlog into a wave-orchestration execution plan.

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GitHub Actions CI/CD setup for Python, R, and multi-platform testing. Use when creating, debugging, or optimizing CI/CD pipelines.

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Normalizes documentation and examples for consistency in style, format, and accuracy. Ensures code matches existing patterns and documentation. Use when reviewing docs, examples, or writing new code that should follow established patterns.

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Guides the discovery, design, validation, and delivery of Agent Skills. Use when identifying skill gaps, creating or revising a SKILL.md, improving skill descriptions, or checking a skill against the Agent Skills specification.

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