データ・AIのスキル

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 M&S VV&A Recommended Practices Guide (RPG web edition). Use for verification, validation, and accreditation of models and simulations — role responsibilities (user, developer, M&S PM, V&V agent, accreditation agent), fidelity, validation fundamentals, data V&V, risk-informed accreditation, T&E/V&V integration, referent development, and conceptual-model validation. Complements nasa-ms-7009 and T&E packs; does not replace DoDI 5000.61 or Service VV&A policies.

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Knowledge base from DoD Architecture Framework (DoDAF) 2.02 — Volume II: Architectural Data and Models. Use for DoDAF viewpoints, DM2 meta-model, capability modeling, operational architecture, systems architecture, services architecture, data/information modeling, standards compliance, project/portfolio integration, and fit-for-purpose model selection. Does not cover DoDAF Volume I (policy/governance) or Volume III (enterprise-level guidance); also does not substitute for the live DISR or DARS registries.

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Knowledge base from IS-GPS-200 Rev N (NAVSTAR GPS Space Segment / Navigation User Segment Interfaces). Use as a worked ICD/IS exemplar: what an interface specification is, DIST-A and IRN/CCB change control, interface definition vs identification, composite-signal criteria patterns, NAV data as payload families, and time/definition hygiene. Covers Rev N synthesized notes only; does not transcribe Apps II–IV bit fields or PRN/Gold-code tables, and does not replace faa-std-025 preparation rules.

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Knowledge base from the NASA Systems Engineering Handbook (NASA/SP-2016-6105 Rev 2). Use for NASA's systems-engineering process model — the SE Engine and 17 common technical processes, the program/project life cycle (Pre-Phase A through F), Key Decision Points and technical reviews (MCR/SRR/PDR/CDR/…), stakeholder expectations, technical requirements, design solution, product verification and validation, and crosscutting technical management (planning, risk, configuration, interfaces, data, assessment, decision analysis). Strong for aerospace/spaceflight project SE and lifecycle gating; NASA-specific terminology (KDPs, NPR 7123.1).

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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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Knowledge base from the NASA Digital Engineering Acquisition Framework Handbook (NASA-HDBK-1004). Use for acquiring digital/model-based data under contract: the Data Requirements Description (DRD) instrument and the Appendix A DRD suite (TDP, MBx, CDMP, CSA, CR, FCA/PCA, SDT, EMDAL, REF, MRI); the Data Type 1-5 control scale; SOW/RFP/Cost-Volume contract language and data rights (FAR/DFARS, Table 3 license categories); the Requirements Exchange Format (REF/ReqIF, UUID schema) and Master Records Index; model-based data definition (MBSE, CAD/PMI, M&S credibility, BOM, release-state mapping, PLM); digital data collaboration, the four owned architectures, and interoperability standards (MIL-STD-31000, STEP/ISO 10303, ASME Y14); the four MBE interoperability use cases; and MBE Plan development. Scope is the acquisition and contracting side of digital engineering. Does not reproduce ISO/STEP/ASME/MIL-STD/SAE standard text, and the companion NASA-HDBK-1009A carries the deeper digital-engineering practitioner detail this handbook defers to.

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Knowledge base from the NASA Probabilistic Risk Assessment Procedures Guide (NASA/SP-2011-3421, 2nd ed.). Use for quantitative PRA of aerospace and safety-critical systems: the risk triplet and scenario logic stack (MLD, ESD, event trees, fault trees, minimal cut sets), Bayesian data collection and parameter estimation, aleatory/epistemic uncertainty modeling and common-cause failure (Alpha Factor, beta-factor, CCBE), human reliability analysis (THERP, CREAM, NARA, SPAR-H), context-based software risk (CSRM), physics-based and structural/phenomenological models (stress-strength, limit states, FORM/SORM, NASGRO, range safety), uncertainty propagation and importance measures (F-V, RAW, Birnbaum, DIM), and launch-abort modeling with worked PRA examples. This is the QUANTITATIVE engine beneath NASA's risk doctrine — for the qualitative RIDM/CRM decision framework use the nasa-risk pack. Thin on programme management, organisational risk governance, and non-aerospace regulatory contexts; aerospace-focused and anchored to the 2011 second edition.

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Apply before writing logic: choosing core types and data structures, sequencing scaffold-vs-feature work, asking what concurrent actors share. Get the data structures right so downstream code becomes obvious.

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Apply when designing types, reviewing a function signature, or writing code in any statically-typed language. Make illegal states unrepresentable, brand semantic primitives, parse external data at boundaries, refuse to lie to the compiler, exhaust variants, derive from authoritative schemas.

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Entfernt KI-Spuren, Chat-Reste, Platzhalter und LLM-Stilmuster aus fertigen Texten und prüft die AI-Disclosure auf Vollständigkeit und korrekten Sitz.

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Split documents into overlapping token chunks for RAG pipelines and LLM context windows. Zero dependencies.

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Use when the user wants to send the same prompt to several LLMs and compare the answers - sequentially (stopwatch, clean per-lane timing) or in parallel (true race), with optional repetitions per model, judged by the starting model across quality, correctness, completeness, instruction fidelity and latency (time is only one dimension). Triggers on /compare-race, 'model race', 'vergleiche modelle', 'gleicher prompt an mehrere modelle'.

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Calendar skill with user-adaptive backend selection (Flag 3). Default: local SQLite store. Optional: Google Calendar MCP, Routinika or UpToday as backend — controlled via assist/prefs.json. Without preference the LLM asks the user interactively.

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Hosting-Muster, um eine lokal gebaute Anwendung (eigene Datenbank, eigener API-Key, In-Process-State) sicher hostbar zu machen, ohne Nutzerverwaltung zu bauen. Kernzug — der Host speichert nichts, der Browser des Besuchers speichert alles — sodass Pro-Besucher-Accounts, Zugriffsprüfungen und Löschfristen gegenstandslos werden, statt gelöst werden zu müssen. Nutzen, wenn eine lokal gebaute App für mehrere Besucher gehostet werden soll, bei "mach das ohne Login hostbar", "keine Nutzerkonten bauen", "Datenschutzerklärung für ein gehostetes Tool verkleinern", "wer ist verantwortlich, wenn wir nichts speichern" oder beim Wählen/Implementieren eines Server-Modus (`local`, `huckepack-gift`, `huckepack-only-host`, `pay-membership`). Liefert eine Data-Flow-Plan-Vorlage, eine Datenschutz-Vorlage und eine rechtliche Ersteinschätzung (DSGVO/TDDDG/UWG, deutsch) als Referenzen.

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Produces a DRAFT product requirements document from the discovery notes, interview summaries, support themes, analytics notes and stakeholder messages the user provides: problem statement, target users, goals with measures as stated, numbered requirements each with a testable acceptance criterion, non-goals, constraints, dependencies and open questions, with every statement tagged evidenced, assumed or UNKNOWN so each assumption is visible for the product owner to confirm. Use when the user asks to "write a PRD", "draft the product requirements", "turn these discovery notes into requirements", "document what this feature needs to do" or "build the requirements doc for this epic". Do not use for supplier-facing requirements in a tender, use rfp-requirements-pack instead; for a technical design decision, use architecture-decision-record; for the change ticket that ships the work, use change-request-pack. Drafts for human review; never approves, authorises or signs off.

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Prepares the trend update for a recurring emailed report: identifies report emails not yet processed, extracts the configured metrics from their spreadsheet, CSV or PDF attachments, returns new rows for an append-only trends sheet and writes a DRAFT summary with period-on-period deltas and flagged anomalies. Use when the user asks to "update the trends for the weekly report", "analyse the latest monthly report", "what changed in the latest report", "do the latest numbers look unusual" or "set up tracking for a report that arrives by email". Do not use for a single file with no trend state, use dataset-insight-pack instead. Drafts for human review; never approves, authorises or signs off.

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Drafts the instructions field of a declarative agent that orchestrates a set of custom skills, under a character cap (default 8,000), from the skills' names and descriptions: role and scope, one routing rule per skill, workflow order, gates written as workflow holds, global rules, output format and what the agent never does. Returns the text in a fenced block with its character count labelled counted or estimated, plus a skill inventory, routing table, gate table, rules trace and open questions for the owner. Use when the user asks to "write the instructions for my agent", "draft a system prompt that ties these skills together", "tighten these agent instructions to fit the cap", "how should the agent route between these skills" or "revise my agent's orchestration text". Do not use to review a single SKILL.md, use skill-file-reviewer instead. Drafts for human review; never approves, authorises or signs off.

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Drafts a blameless incident postmortem (summary, impact, timeline, detection and response, contributing factors as candidates for review, action items) from an incident channel export, ticket notes and any alert or change records, with UNKNOWN for every fact the inputs do not contain. Never assigns blame, names a root cause or re-rates severity. Use when the user asks to "write the postmortem for this incident", "build the incident timeline from the chat export", "draft the post-incident review" or "turn this ticket and channel log into an incident report". Do not use for a knowledge article from the resolved ticket, use knowledge-article-drafter instead; for project retrospectives, use lessons-learned-synthesis; for data-breach impact and notification questions, use data-incident-impact-brief. Drafts for human review; never approves, authorises or signs off.

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Turns a list of KPIs or metrics into a DRAFT definition sheet: one row per KPI with the name as stated, formula quoted from a source, source system, owner, refresh cadence, unit, filters and known caveats, with UNKNOWN in every cell no source fills and a gap list grouped by data owner. Never composes a formula, sets a target or names an owner the sources do not state. Use when the user asks to "define these KPIs", "build a metrics definition sheet", "document how each KPI is calculated", "which of our KPIs have no owner or formula" or "reconcile the two KPI lists". Do not use for the weekly numbers report, use kpi-weekly-report-writer instead; for field-level definitions of a table, use data-dictionary-builder; for logging a wrong figure as a defect, use data-quality-issue-log. Drafts for human review; never approves, authorises or signs off.

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Reads one spreadsheet, CSV export or pasted table together with the user's question and drafts an insight pack: a data profile, observations cited to named columns and values and framed as leads, caveats, and suggested charts and follow-up analyses. Every figure carries the label "as read, verify in source"; correlation is never stated as causation. Use when the user asks to "profile this spreadsheet", "what does this data say", "find anomalies in this extract", "give me a first read on this CSV" or "summarise this table". Do not use for a purchase order log or spend extract, use purchase-order-anomaly-review instead; for actuals versus budget, use budget-variance-explainer instead; for a report that arrives by email on a schedule, use report-attachment-analyzer instead. Drafts for human review; never approves, authorises or signs off.

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Proposes redactions of personal identifiers in pasted text or an attached document (names, contact details, identification and account numbers, dates that identify, quasi-identifiers) and returns a DRAFT redacted copy with consistent placeholder tokens plus a redaction table stating what was replaced, where and why, with a confidence level and a blank reviewer decision per row. Use when the user asks to "redact the names in this document", "de-identify this transcript before we share it", "strip personal data from these notes", "anonymise this report for publication" or "what would need redacting here". Do not use for assessing a planned processing activity, use dpia-draft-pack instead; do not use for working out what data an incident exposed, use data-incident-impact-brief instead. Never declares a document anonymised, safe to release or compliant. Drafts for human review; never approves, authorises or signs off.

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Synthesises a batch of customer feedback (survey answers, reviews, ticket and chat comments) into a DRAFT themed report: coded themes with record counts and shares, anonymised verbatim quotes cited to record codes, contradictions and unverified claims, and the follow-up questions to answer next. Produces no sentiment score or percentage positive as fact; ratings appear only as distributions with n. Use when the user asks to "theme this feedback", "what are customers saying", "summarise these survey answers", "analyse these reviews", "find the top complaints in these comments" or "code this voice of the customer data". Do not use for exit interviews, use exit-interview-synthesis instead; for sorting a live queue and drafting replies, use ticket-triage-pack; for project retrospectives, use lessons-learned-synthesis. Drafts for human review; never approves, authorises or signs off.

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Turns reported data problems (messages, tickets, meeting notes, complaints) into one DRAFT issue log: one row per issue with the symptom as reported, dataset and fields named, reproduction steps, affected reports, first seen date, reporters, a suggested owner, and blank severity, root cause and status cells for the owner. Never asserts a root cause, severity or fix. Use when the user asks to "log these data issues", "turn these complaints about the numbers into an issue list", "build the data quality backlog", "which reports are hit by this data problem" or "write up the reproduction steps for this data bug". Do not use for exploring or profiling a dataset, use dataset-insight-pack instead; for an IT incident write-up, use incident-postmortem-drafter; for tracking fix actions once assigned, use corrective-action-tracker. Drafts for human review; never approves, authorises or signs off.

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