「db」の検索結果

Knowledge pack from 'Towards Digital Engineering: The Advent of Digital Systems Engineering' (Huang et al., Old Dominion University, arXiv:2002.11672, 2020). Use when reasoning about digital engineering, the DoD Digital Engineering Strategy and its five goals, the Authoritative Source of Truth, digitalization vs. digitization, digital augmentation, digitalized models, provenance, unique identification, and the four-level (vision/strategy/action/foundation) DSE research framework. Covers the conceptual/vision layer of the field. SCOPE LIMITS: this is a single ~28-page vision paper, not a methods handbook — it is thin on step-by-step procedures, tooling, case studies, and quantitative evaluation, and it predates 2020 so it does not cover later digital-engineering standards or recent AI/LLM advances. Use the sebok pack for established SE canon and MBSE depth.

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Knowledge base from the DoD Risk, Issue, and Opportunity (RIO) Management Guide for Defense Acquisition Programs (OUSD R&E, Sep 2023 incl. Change 2.2). Use for the defense-acquisition mechanics of program risk management: the RIO definitions (risk/issue/opportunity), the five-step risk process (plan-identify-analyze-mitigate-monitor), 1-5 likelihood/consequence scoring and the 5x5 risk matrix, the four mitigation options (accept/avoid/transfer/control), burn-down plans, issue management (probability=1), opportunity management toward should-cost, cross-program/interface risk, tailoring RIO to the six AAF acquisition pathways (UCA/MTA/MCA/Software/DBS/Services), specialized methods (RMF, MBCRA/Cyber Table Top, Agile metrics, FMECA, ITRA/DTRAM), and institutionalizing RIO via the PRMP, boards (RMB/JRMB/ROMB/RWG), tools, tiered roles, and WBS/IMP/IMS/EVM/TPM integration. This is the DoD-acquisition complement to nasa-risk (general CRM/RIDM theory) and dau-se-guidebook (the SE process frame). Does NOT teach general risk theory from scratch, and defers specialized risk regimes (system safety/ESOH, HSI, detailed cyber/RMF, spectrum) to their own DoD instructions and the relevant packs.

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Knowledge base from NASA Risk Management Handbook (NASA/SP-2011-3422). Use for risk-informed decision making, continuous risk management, performance commitments, risk scenario diagrams, risk disposition types, risk burn-down schedules, objectives hierarchies, deliberation and alternative selection, risk driver identification, and RM planning for space and safety-critical programmes. Does not cover domain-specific PRA methods (fault trees, event trees) in procedural depth — see NASA PRA Procedures Guide for those.

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Verweist auf das öffentliche, anbieterneutrale Modul build-your-users-mind: ein datenschutzbewusstes Verfahren zum Aufbau eines empirischen Theory-of-Mind-Präferenzmodells eines autorisierten Nutzers aus dessen eigenen Interaktionsprotokollen. Verwenden, wenn eine zuständige Person ein Entscheidungsavatar-Profil erstellen, validieren, anbinden oder pflegen möchte, ohne das persönliche Profil oder seine Belege zu veröffentlichen.

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Baut und prüft neue Agentenrollen im ellmos-Ökosystem: native Claude-Code- Subagenten (dauerhafte Rollendefinition) und Companion-Worker (SendMessage- wiederverwendete Instanz für eine Ticket-/Aufgabenserie). Formalisiert das bislang unformalisierte eigene Wissen aus dem laufenden Bau von Agentenrollen (ati-agent, bueroassistent, versicherungs-agent u. a.) zu einem wiederverwendbaren Scaffold-plus-Verify-Ablauf. Nutze diesen Skill, wenn eine neue Rolle entstehen soll ("neue Agentenrolle bauen", "Subagent anlegen", "Companion-Worker starten") oder eine bestehende gegen die Haus-Konventionen geprüft werden soll ("ist dieser Agent richtig aufgesetzt?").

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Erstellt oder überarbeitet einen Skill FÜR DIESES ÖKOSYSTEM: delegiert die generische Interview-/Entwurfs-/Eval-Schleife an das offizielle skill-creator-Plugin (Anthropic, Apache-2.0), setzt aber zusätzlich die ellmos-Anforderungen durch, die das offizielle Plugin nicht kennt — Haus-Frontmatter (9 Felder), Kategorie/Sichtbarkeits-Regeln, anbieter-/nutzer-/ellmos-neutrale aber ellmos-sensitive Erkennung via grounding-seed, mitgelieferte config.json (inkl. Lernvertrag) + connections.json, Anbieter-/Vendor-Bewusstsein (State-of-the-Art-Alternativen inkl. eigener ellmos-Komponenten vorschlagen), einen Selbstaktualisierungs-Abschnitt UND einen wiederverwendbaren Lern-/Abdeckungs-Dateisatz (user-usecases.json, usecase-gaps.json, known-providers-and-abilities.json, gap-closing-analysis.json — an skill-creators evals.json und A2A/AgentSkill angelehnt, kein Parallelstandard). IMMER nutzen, wenn ein NEUER Skill für dieses System entstehen oder ein bestehender auf ellmos-Konformität nachgerüstet werden soll — auch bei "erstelle einen Skill", "bau mir einen Skill für X", "skill-creator nutzen", "neuen Skill anlegen", "diesen Skill ellmos-konform machen", "Lernvertrag/Verhaltenscodex für einen Skill anlegen", "Usecase-Abdeckung eines Skills prüfen". Für reines Skill-FINDEN/ROUTEN stattdessen skill-finder, für Landschafts-Audit skill-explorer.

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Baut und betreibt ein anbieterneutrales Self-Care-Kernset für geplante LLM-Aufgaben und Desktop-App-Automationen. Ergänzt deterministisches Policy-Linting, Lock- und Modell-Governance, semantische Prompt-Prüfung, konsistente Mehrflächen-Updates, Mini-Checks und Regeldatei-Hygiene. Nutzen, wenn ein Agent seinen nativen Scheduler entdecken oder eine bestehende Automations-Flotte mit Rollback, Readback und Löschschutz kontinuierlich verbessern soll. Löst aus bei automation self-care, Scheduler-Task-Pflege, Desktop-App-Automations-Wartung, Automations-Flotten-Audit, selbstheilende Zeitpläne, core-set-textautomations, basic-text-automations, textbased-automation-core, textbased-automation-drivers, textbased-governance-automations oder textbased-desktopapp-automations.

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Erstellt einen portablen KI-Arbeitsbereich auf einem USB-Stick oder beliebigen Laufwerk. RAG-Pipeline mit lokalen LLM-Modellen (Ollama), Vektordatenbank (ChromaDB) und vorkonfigurierten Prompts.

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Creates a personalised daily newspaper from RSS feeds and web sources. Ported from the BACH news system (news.py + newspaper_generator.py). Own SQLite store (no Origin-DB). feedparser optional — XML fallback via stdlib. PDF export via Edge Headless (msedge.exe).

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Builds a DRAFT change-communication plan from a change description: audience map, key messages per audience traced to the description, channels, sequence with dates or day offsets, owners, feedback loop and open questions, plus drafts of the first messages for the sponsor to approve. Use when the user asks to "plan the comms for this change", "who needs to hear about this and when", "draft the announcement sequence", "build a communication plan for the rollout", "write the first message to managers about this change" or "how do we tell people about this". Do not use for a clause-by-clause comparison of a changed HR policy with its previous version, use policy-change-briefing instead; for a new hire's first weeks, use onboarding-plan-builder. Drafts for human review; never approves, authorises or signs off.

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Synthesises training survey responses and manager notes into a DRAFT training needs report by role: coded needs with record counts per role (n of N), the evidence type behind each count, anonymised quotes cited to record codes, disagreements between what staff report and what managers observe, gaps where a role is under-represented or a required skill has no evidence, and follow-up questions to settle before any course is designed. Never rates an individual, ranks a team or turns a count into a competence verdict. Use when the user asks to "analyse this training survey", "what training do our teams need", "summarise the manager feedback on skills gaps", "build a training needs analysis by role" or "which roles have the biggest skill gaps". Do not use for turning an agreed need into a course, use course-outline-builder instead; for exit interviews, use exit-interview-synthesis. Drafts for human review; never approves, authorises or signs off.

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Answers an employee's or manager's HR policy question strictly from the policy documents the user supplies or the agent can reach as knowledge: a DRAFT answer that quotes the governing clause with its document, version and clause reference, lists the conditions and exceptions the text states, returns UNKNOWN where the documents are silent and names the role to ask. Never invents a rule, fills a gap from general practice or decides an individual's case. Use when the user asks to check a policy, for example "what does the policy say about", "am I entitled to", "how many days of leave do I get", "what is the process for requesting", "does the handbook allow" or "which clause covers". Do not use for comparing two policy versions or briefing a change, use policy-change-briefing instead. Drafts for human review; never approves, authorises or signs off.

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Maps every proposed P&ID object from an accepted enrichment draft to the generic family, catalogue class and required attributes the project's intelligent P&ID authoring tool would need, as a readiness table with UNKNOWN wherever the project catalogue extract is not provided, plus connectivity readiness per line and a numbered list of import blockers. Mapping only: it never writes to the tool, never produces an import file and never claims a class exists in the catalogue. Use when the user asks to "build the readiness map", "show how far the draft is from a sandbox import", "list what would block the import", "map the objects to the tool catalogue" or "check attribute readiness against the catalogue extract" after gate G3. Do not use for validating the discipline sections or assembling the review package, use validation-and-review-package instead. Drafts for human review; never approves, authorises or signs off.

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Runs twice in a PFD to P&ID job. Part 1, before gate G3, validates the discipline proposals for provenance, UNKNOWN handling, conflicts, prohibited content and cross-section consistency and returns results and blocking items. Part 2, before gate G5, assembles the review package, with job header, gate record, every register and proposal, consolidated UNKNOWN list, feedback log and closing statement. Use when the user asks to "validate the enrichment", "run validation before G3", "assemble the review package" or "build the G5 package". Do not use for the readiness map, use pid-tool-readiness-mapping instead; do not use to produce a missing discipline section, use the discipline skill that owns it. Drafts for human review; never approves, authorises or signs off.

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Diagnose or enable Session Cartographer semantic search in Codex, including least-privilege localhost access to local Qdrant and the embedding server. Use when setup, Qdrant health, localhost reachability, sandbox network denial, or semantic indexing is in question.

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同業種の複数企業（3〜5社程度）を横並びで比較し、業界内のポジショニングを整理する時に使う。「〇〇と△△を比較して」「この業界の主要企業を並べて」のような、複数社の比較指示で発火する。

決算発表直後に、速報値・会社予想・前年実績の3軸で当該企業の決算を読み解く時に使う。「〇〇の決算どうだった」「直近の決算をレビューして」のような、決算期に特化した指示で発火する。

特定の日本株1銘柄について、企業概要・セグメント経済性・財務トレンド・株主構成・ガバナンス・リスク要因・競争優位の仮説・バリュエーション素材までを網羅した総合リサーチレポートを作る時に使う。「トヨタを分析して」「〇〇の株を調べて」のように1社を深掘りする指示で発火する。

特定企業について、事業の質・価格と価値・リスク反証の3つの視点で討論形式の検討をしたい時に使う。「〇〇を多角的に検討して」「賛否両論で議論して」のような、単一の結論ではなく視点の対立を見たい指示で発火する。売買判断そのもの（買うべきか）への回答は目的ではなく、論点の整理までを行う。

財務指標の条件（ROE・PBR・配当利回り・自己資本比率等）で日本株を絞り込む時に使う。「ROE15%以上でPBR1倍以下の企業を探して」「配当利回りが高い企業ランキング」のような、条件検索・ランキング指示で発火する。

複数事業セグメントを持つ企業の、セグメント別の収益性・成長性・資産配分を深掘りする時に使う。「〇〇のセグメント別採算を教えて」「どの事業が稼いでいるか」のような、事業構成に着目した指示で発火する。

特定企業の株主構成・大量保有報告書・役員報酬・ガバナンス構造を深掘りする時に使う。「〇〇の株主構成を教えて」「大株主は誰」「アクティビストは入っているか」のような、企業統治・資本政策の観点の指示で発火する。

- СОРСИНГ ПОД РЕЙЗ в два трека: инвесторы из нашего VC-графа (leads.db) и покупатели-пилоты, найденные ПО СИМПТОМУ в интернете (GitHub issues, Hacker News, X, наши же репозитории, клуб СОСТАВ). Триггеры: “/raise-sourcing“, “/sourcing“, “собери список инвесторов“, “кому питчить“, “найди покупателей“, “добери лидов под рейз“, “кого звать в пилот“, “обнови стол сорсинга“, “raise sourcing“

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Наполнение пула ИНЖЕНЕРОВ-ТЕСТЕРОВ под миссию №2 из живых issue вендорских и агентных репо (не из крипто-базы leads.db). Триггеры: «/engineer-pool», «/ep», «где брать инженеров», «пул инженеров», «кому давать демо», «инженеров в выборке ноль», «наполни пул тестеров», «engineer pool», а также САМ, когда отчёт очереди touchbase печатает «инженеров из LLM-лабораторий в выборке: 0»

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。