「quality」の検索結果

Use when evaluating a completed or in-progress project across scope, correctness, quality, testing, and learning outcomes using structured Praise-Critique-Grow feedback.

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Use when the deliverable is authored or edited prose whose wording matters (docs, READMEs, release notes, public copy) or when the user asks to tighten, de-AI, or review writing. Not for chat answers, code review, or explanations of current work.

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Audit a dataset or access rule before it joins an identity-graph build (access rules behave like datasets in NQL here). Enumerates failure modes (hub identifiers, high-degree nodes, suspicious values, over-connected identifiers), tests hypotheses in parallel, quantifies damage by rows / edges / entities, and proposes minimal filters ranked by severity. When issues are found, returns a validated `CREATE MATERIALIZED VIEW` NQL the caller can run to produce a graph-ready clean source; if the data passes, says so and recommends it unchanged. Plans and authors the clean-view NQL; does not execute it. Use when: "audit this dataset before the graph build", "find bad edges in <source>", "check identity data quality", "recommend filters for the graph build", "quantify damage from <identifier_type>", "pre-graph DQ". (narrative-identity)

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ego-browser (ego-lite) is a Chromium-based browser that gives AI agents a CLI-accessible Node.js runtime for driving a real browser. Use this skill whenever the user needs to interact with a website opening pages, filling forms, clicking buttons, taking screenshots, extracting page data, testing web apps, logging into sites, automating browser operations, or any other browser automation task. Triggers include requests to "open a website", "visit a URL", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "extract content from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction. Also used for exploratory testing, dogfooding, QA, bug hunting, or reviewing app quality. Prefer ego-browser over any built-in browser automation, web fetch, or other web tools.

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Comprehensive code review for quality, security, performance. Triggers on 'review code', 'code review', 'check this PR', 'audit code'.

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Commit the current changes with quality checks, status review, staged diff inspection, appropriate tests, and a clear commit message. Use when the user says "commit", "c", asks to make a commit, or wants changes saved in git.

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 for pre-PR self-review or pull-request review that must verify the matched intent, allowed scope, required tests/docs, forbidden actions, architecture gates, and merge readiness; not for a general code-quality diff review alone.

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Audit code quality focusing on deep modules, naming, model hygiene, and architectural clarity. Use after a burst of development, when codebase structure needs assessment, or to make code more agent-navigable.

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Comprehensive results analysis for empirical research: generate publication-quality descriptive statistics and balance tables, interpret regression coefficients with economic magnitude and effect sizes, assess identification assumption diagnostics, and produce structured results memos. Use when asked to create summary statistics, Table 1, balance tests, interpret results, assess economic significance, or write results narratives.

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Create Beamer-style academic PPTX presentations using python-pptx. Produces publication-quality .pptx files with navy-blue Metropolis theme (16:9, frame title bars, progress bar) for conference talks, job market presentations, and seminar slides. Called by /present command.

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Knowledge base synthesized from the Guide to the Systems Engineering Body of Knowledge (SEBoK) v2.14 by the BKCASE Project. Use for the established systems-engineering canon: SE fundamentals and foundations (systems science, systems thinking, the systems approach, modelling/MBSE), the life cycle (ISO/IEC/IEEE 15288/24748/24774 process, development & agile approaches, model selection/tailoring), technical management, concept definition, architecture/realization (the Vee), V&V, maintenance/sustainment, SE standards, and the application contexts (product, service, enterprise, systems-of-systems, healthcare), plus enabling SE (business/team/individual), related disciplines, quality attributes/specialty engineering, implementation examples, and emerging knowledge. Good for grounding background, vocabulary, and prior-art positions across all eight SEBoK Parts. SCOPE LIMITS: this pack is SEBoK v2.14 only (released 2026-05-18); it synthesizes and signposts rather than reproducing the source — it does not reproduce ISO/IEC/IEEE standard text, is a curated guide (not a complete textbook), and is thin on AI-integration depth (Part 8 is a staging area, not settled practice). LICENCE: content is CC BY-NC-SA 3.0 — non-commercial use only, attribution to the BKCASE Project required, share-alike obligations carry forward.

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Knowledge base from the GAO Technology Readiness Assessment Guide (GAO-20-48G, 2020). Use for evaluating technology maturity in acquisition programs and projects: Technology Readiness Levels (TRL 1–9) and related scales (MRL, IRL), identifying Critical Technology Elements (CTEs), the five-step process for a high-quality TRA, the four characteristics of a reliable TRA (comprehensive, well-documented, credible, defensible), Technology Maturation Plans (TMPs), and using TRA results in knowledge-based, milestone decisions. Assessment/maturity oriented: it measures readiness and frames risk — it is not a full SE process model, cost-estimating, or schedule guide (see sibling packs for those).

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Análisis de requisitos de documentos: Analiza los documentos de concepto y requisitos en la carpeta docs/, verifica los requisitos con el código actual y crea un informe de diferencias consolidado.

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文档需求分析：分析 docs/ 文件夹中的概念与需求文档，针对当前代码检查需求，并生成整合的差异报告。

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Анализ требований к документации: Анализирует документы концепций и требований в папке docs/, проверяет требования на соответствие текущему коду и создает консолидированный отчет об отклонениях.

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Role-based multi-model strategy. The model catalog lives in a local configuration file instead of the skill text -- roles (thinker/decision-maker, long-running worker, draft generator, reviewer/acceptance, escalation), reasoning effort as an independent axis, mandatory review for draft models, provider switching for independent reviews, budgets, delegation flow, and cost/quality of small workers.

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。

ドキュメント要件分析: docs/ フォルダ内のコンセプトおよび要件ドキュメントを分析し、現在のコードと照らし合わせて要件を検証し、統合差異レポートを作成します。

系统化 Bug 扫荡工作流，包含基于代码库规模的目标值计算、翻倍升级机制、区域追踪及最终验证。适用于执行 /bugsweep 或用户要求进行系统化 Bug 排查时。

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Систематический поиск багов с целевым значением, масштабируемым по размеру кодовой базы, удвоением при эскалации, отслеживанием областей и финальной верификацией. Используйте при вызове /bugsweep или по запросу систематической проверки кода.

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コードベースの規模に応じた目標値計算、倍増エスカレーション、領域追跡、最終検証を備えた体系的なバグスウィープ。/bugsweep 実行時や体系的なバグ調査が要求された場合に使用します。

Barrido sistemático de errores con un valor objetivo escalado a la base de código, escalado por duplicación, seguimiento de áreas y verificación final. Se utiliza al ejecutar /bugsweep o cuando el usuario solicita una revisión sistemática de errores.

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Metakognitive Injektoren und Selbstgesprächs-Strategien (Self-Talk, Metacognitive Auditing, Evaluator Hooks, Pre-Flight Checklists, State Persistence & Personas). Integriert Aktive Informationssuche (Systemgedächtnis Gardener & USMC), Rehearsal (Aktives Abrufen & Wiedergeben), Baddeleys Arbeitsgedächtnis (State + Hooks), Miyake Exekutivfunktionen sowie CBT/ACT therapeutische Strategien.

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Systematic bug sweep with a codebase-scaled target value, doubling escalation, area tracking, and final verification. Use on /bugsweep or whenever the user requests a systematic bug pass.

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。

Standard procedure for onboarding new software projects: Feature analysis, code quality review, onboarding checklist, and task 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。