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Knowledge base from the NASA Cost Estimating Handbook (CEH) v4.0. Use for NASA's 12-step, three-part cost estimating process (Project Definition, Cost Methodology, Cost Estimate); the WBS, CADRe, and ONCE data backbone; estimating methodologies (analogy, parametric/CER, engineering build-up, EVM, Delphi); ground rules and assumptions, data normalization and inflation (BY/CY/RY); the point-estimate-to-S-curve cost-risk chain and Unallocated Future Expense (UFE) reserves; Joint Cost and Schedule Confidence Level (JCL) analysis via probabilistic cost-loaded schedules; decision-support analyses (sensitivity, trade studies, make/lease-vs-buy, affordability, CAIV); and economic analysis (discounting, NPV, OMB Circular A-94). The cost-estimating complement to NASA's SE and risk packs. Does NOT reproduce the handbook's tool-specific procedures, worked examples, or appendix-depth methods (it names the appendices), nor cover full DoD/GAO cost guides, schedule construction, or formal risk management beyond the cost interface.

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多模型编排与模型切换策略。基于评分的模型选择、跨 Agent 委派（Gemini、Codex、Ollama）、Advisor 配对、升级触发器、权限矩阵与成本效率优化。

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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.

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Многомодельная оркестрация и стратегия переключения моделей. Выбор моделей на основе оценки (score), делегирование между агентами (Gemini, Codex, Ollama), связывание с advisor, триггеры эскалации, матрица разрешений и оптимизация затрат.

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マルチモデルオーケストレーションとモデル切り替え戦略。スコアベースのモデル選択、クロスエージェント委任（Gemini、Codex、Ollama）、アドバイザーペアリング、エスカレーション条件、権限マトリクス、およびコスト効率の最適化。

Orquestación multimodelo y estrategia de cambio de modelos. Selección de modelos basada en puntuación, delegación entre agentes (Gemini, Codex, Ollama), emparejamiento con advisor, disparadores de escalado, matriz de permisos y optimización de eficiencia de costos.

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Structured decision making: Pro/Con matrix, weighted scoring, decision tree, scenario analysis, and Eisenhower matrix.

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Ordner und Dateiablagen semantisch aufräumen: lokale Regeln und vorhandene Taxonomie erkennen, Dateien inhaltlich zuordnen, zusammengehörige Sets erhalten, klare Vorgänger-/Nachfolgerreihen und Logs archivieren, gemischte gültige und überholte Inhalte per Cut-and-Clue trennen und Löschkandidaten reversibel in einen Prüf-Papierkorb legen. Nutze diesen Skill bei „Ordner aufräumen“, „Dateien sinnvoll einsortieren“, „alte Versionen archivieren“, „Logdateien bereinigen“, „veraltete und gültige Inhalte trennen“ oder wenn beim Aufräumen Geheimnisdateien beziehungsweise Geheimnisse in einer Cloud-Ablage auffallen. Er funktioniert anbieter- und nutzerneutral allein; erkannte kompatible Module oder Dateidienste werden nur als optionale Beschleuniger genutzt. Für bereits vollständig definierte wiederkehrende Sammelregeln stattdessen eine regelbasierte Datei-Automation, für den Umbau einer ganzen Projektarchitektur einen Pipeline-/Projekt-Optimizer verwenden.

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Ordnet freiwillig bereitgestellte Kontaktdaten datensparsam und erkennt mögliche Dubletten.

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Protocol for safely creating, publishing, releasing, auditing, and maintaining GitHub repositories: check local rules and locks, create .gitignore before the first add, run privacy checks, prepare README/i18n/banner/metadata, verify release tags and GitHub releases, and update organization profiles, llms.txt files, and registry links.

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Rollenbasierte Multi-Modell-Strategie. Modellkatalog liegt in einer lokalen Konfigurationsdatei statt im Skill-Text -- Rollen (Denker/Entscheider, Dauer-Worker, Entwurfsgenerator, Reviewer/Abnahme, Eskalation), Reasoning-Effort als eigene Achse, Abnahmepflicht für Entwurfsmodelle, Anbieterwechsel für unabhängige Reviews, Kontingente, Delegationsablauf und Kosten/Qualität kleiner Worker.

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Ordnet freiwillig bereitgestellte Gesundheitsinformationen und bereitet Fragen für Fachpersonen vor.

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Tiefe Pflegerunde für ein veröffentlichtes GitHub-Repository (Stufe 2): enthält den vollständigen surface-after-care-Durchlauf und ergänzt ihn um drei teure Schritte — rechtliche Ersteinschätzung über die Law-Checker mit Wiedervorlage nach einem Jahr (Gutachten bleibt gitignored im Repo), Querverweise zu verwandten Repos über ALLE Organisationen hinweg sowie das Nachziehen aller Sprachen auf App-Ebene, nicht nur in der Doku. Nutze diesen Skill bei "full after care", "deep after care", "tiefe Repo-Pflege", "große Runde", "Repo grundlegend durchgehen", wenn ein Repo länger nicht geprüft wurde, vor größeren Releases oder wenn rechtliche Relevanz, Querverweise oder Mehrsprachigkeit ausdrücklich Thema sind. Für die günstige, oft wiederholte Runde stattdessen surface-after-care; für die Erstveröffentlichung github-repo-care.

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Akademisches Studien- und Fristenmanagement mit Quellenprüfung, Datenschutz und realistischer Handlungsplanung.

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Ordnet Büroaufgaben, Korrespondenz und Fristen ohne Bindung an eine bestimmte Office-App.

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Small-business bookkeeping skills for converting financial evidence into accountant-reviewable outputs, including setup/context, receipt processing, expense categorization, bank reconciliation, tax preparation, monthly close, jurisdiction-aware tax references, plus specialized skills for home office, vehicle, meals, depreciation, 1099 contractors, and estimated taxes. Trigger on "help me with my bookkeeping", "set up my bookkeeping", "bookkeeping context", "chart of accounts", "process my receipts", "categorize expenses", "reconcile my bank statement", "get my books ready for taxes", "close the month", "organize my finances".

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Embedded firmware setup: STM32, ESP32, AVR, RP2040, Nordic nRF, Zephyr/RTOS, vendor SDKs, CMake/CMakePresets, VS Code/clangd, cross toolchains, flash/debug, Docker/CI. Use for MCU scaffolds, existing firmware modernization, BSPs, linker/startup files, SDK package selection, and embedded CI; prefer cpp-project-setup for host-side C++.

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Submission-grade remote-sensing Nature/high-impact journal figure workflow for Python or R. Use whenever the user asks to create, revise, audit, or polish manuscript figures, multi-panel scientific plots, map-led composites, remote-sensing image plates, sensor/product workflow figures, validation/error/uncertainty panels, or journal-ready SVG/PDF/TIFF outputs, especially for Nature-family, Remote Sensing of Environment, ISPRS, IEEE TGRS, or other high-impact venues. Before plotting, define the figure's conclusion, evidence logic, export needs, and review risks. If the user has not chosen Python or R, ask "Python or R?" and stop. Use only the selected backend for figure generation, previewing, exporting, and QA. Supports matplotlib/seaborn and ggplot2/patchwork/ComplexHeatmap. Not for dashboards or Illustrator/Figma-first infographics. Also trigger on general academic-writing figure needs even without the word "Nature", such as making figures/plots for a paper, scientific/academic plotting, data visualization for a manuscript, and Chinese phrasings like 遥感论文配图、学术写作配图、科研绘图、科研作图、地图出图、论文图表、可视化.

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Use when building custom agent backends, implementing the AG-UI protocol, debugging streaming issues, or understanding how agents communicate with frontends. Covers event types, SSE transport, AbstractAgent/HttpAgent patterns, state synchronization, tool calls, and human-in-the-loop flows.

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Audits WordPress plugin or theme PHP code for the most common security mistakes — missing nonce checks, capability checks, input sanitization, output escaping, unslashing, SQL preparation, AJAX nopriv exposure, file/path traversal, and unsafe redirects. Use when reviewing pull requests, before releasing a plugin, when the user asks "is this secure", or when handling code that touches $_GET / $_POST / $_REQUEST / $_COOKIE / $_FILES / $_SERVER, admin-ajax / admin-post, REST endpoints, options, user meta, custom DB queries, or file uploads.

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Designs and reviews the internal architecture of a WordPress plugin — folder layout, PSR-4 one-class-per-file discipline, Schema/Constants placement, composition-root vs singleton decisions, conditional asset enqueueing, script config via wp_add_inline_script, and prefixed custom-hook naming. Use when scaffolding includes/src, reviewing class organization, asset enqueue code, repeated strings, or "should I make this a singleton" decisions.

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Audits WordPress plugin or theme PHP code for internationalization (i18n) correctness — text-domain consistency, use of escaped translation helpers (esc_html__, esc_attr__, esc_html_e), correct placeholder helpers (sprintf with translator comments, _n for plurals, _x for context), no variable text-domains, no concatenation inside __() calls, presence of load_plugin_textdomain or load_theme_textdomain, and matching declared Text Domain in the plugin/theme header. Use before plugin/theme release, when reviewing contributor PRs, when adding new strings, when migrating to a new text domain, or when a translator reports issues with the .pot file.

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Scaffolds and reviews custom WordPress REST API endpoints registered via register_rest_route on rest_api_init — namespace and version slug, permission_callback authorization (NEVER __return_true on state-changing routes, which is the most common plugin-side vulnerability on wp.org), args schema with validate_callback / sanitize_callback / type / enum, object-level capability checks via current_user_can with object ID, responses with WP_REST_Response and WP_Error carrying an HTTP status, no raw DB rows / sensitive columns in responses, cookie auth via X-WP-Nonce, REST vs admin-ajax decision. Recommends the better-route library when the plugin grows past a few endpoints. Use when scaffolding or reviewing a REST endpoint, or migrating from admin-ajax. Triggers on register_rest_route, rest_api_init, permission_callback, WP_REST_Request, WP_REST_Response, WP_Error, X-WP-Nonce, rest_ensure_response, register_rest_field, or any file path containing /rest/ or /api/ in a WP plugin or theme.

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Optimize images for web performance — format conversion (WebP, AVIF), responsive sizing, compression, background removal, lazy loading, and WordPress media library integration. Uses MCP bridge tools (resize_image, remove_background) as primary; the Design Stack media worker (Sharp) as fallback for advanced/batch processing. Use when you need to resize images for social media, convert formats for faster page loads, remove backgrounds from product photos, generate alt text, or batch-process an entire media library.

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