「provenance」の検索結果

Load before external product/design research, competitor comparison, clean-room implementation, or adding/updating code, packages, images, fonts, datasets, generated artifacts, or other dependencies whose source/provenance/license may affect outbound licensing; not for purely internal source use with no dependency change.

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Knowledge base from the NIST Framework for Cyber-Physical Systems (SP 1500-201/202/203, v1.0, 2017). Use for engineering and analyzing cyber-physical systems (CPS) and IoT: the CPS Framework's aspect × facet grid (nine aspects of concern examined through conceptualization, realization, and assurance facets), cross-property trustworthiness risk management (safety/security/privacy/reliability/resilience together), data interoperability (syntactical/semantic/contextual, canonical models, identifiers, provenance), the timing aspect (time-interval classes, time domains, time-aware networking) and secure/resilient time (GNSS jamming/spoofing, PTP/NTP attacks, holdover, the clock-error model and Allan/MTIE metrics), plus the use-case requirements method. Bridges IT and OT under a clock. Scope limits: a 2017 living/draft document, thin on post-2017 standards and on detailed control catalogs; it names but does not reproduce ISO/IEC/IEEE 42010 and 15288 process text or permission-licensed third-party figures.

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Knowledge base from the DoD Digital Engineering Strategy (2018, OUSD/ODASD(SE)). Use for the Department of Defense's five digital engineering goals — (1) formalize the development, integration, and use of models; (2) provide an enduring authoritative source of truth; (3) incorporate technological innovation; (4) establish supporting infrastructure and environments; (5) transform the culture and workforce — plus their focus areas, the document-to-model and design-build-test→model-analyze-build shifts, model formalisms/provenance, governance and access control of the authoritative source of truth, and the coordinate→plan→pilot→sustain rollout. A vision-and-policy strategy, deliberately non-prescriptive. Does NOT provide a how-to method, tool tutorials, MBSE/SysML mechanics, or implementation-plan content; thin on metrics, acquisition-policy detail, and step-by-step procedure. Excludes the CAC-gated Digital Engineering Body of Knowledge (DEBoK).

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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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Bei vorhandenem und ausdrücklich autorisiertem lokalem Entscheidungsprofil: wiederkehrende Entscheidungen aus belegtem Feedback vorhersagen, Konfidenz kalibrieren und Vorhersage, Entscheidung und Ausführung strikt trennen.

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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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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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Reads a process flow diagram (image, PDF or native export) with its job header and returns a provenance-tagged extraction register: equipment, streams, tags, connectivity, instruments already shown, notes and legends, connectivity findings and an explicit UNKNOWN list, then holds at gate G1. Extracts and records only; never enriches, tags or proposes. Use when the user asks to "start a PFD to P&ID job", "read in the attached PFD", "extract the equipment and streams from this PFD" or "read this PFD into a register". Do not use for checking the model, use process-model-check instead; do not use to propose lines, instruments or tags, use piping-enrichment or instrumentation-and-control-enrichment after gate G2. Drafts for human review; never approves, authorises or signs off.

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- End-to-end workflow to establish where and when a photo or video was captured and whether it is authentic — evidentiary handling, metadata extraction, reverse image search for provenance, visual geolocation, chronolocation from shadows, and manipulation… Use when asked to verify where an image was taken, confirm or refute a claimed location or date, or authenticate media before relying on it

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- Extract and interpret embedded file metadata with exiftool — EXIF GPS coordinates, camera make, model and serial, DateTimeOriginal and CreateDate timestamps, XMP and IPTC fields, and Office and PDF… Use when reading EXIF from a photo, checking who really wrote a document, dating a file, fingerprinting a camera or phone, or investigating provenance in JPEG, HEIC, RAW, MP4, DOCX, XLSX or PDF

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- Verify whether an image or video is authentic, original and correctly captioned — provenance checks, error level analysis, noise and JPEG compression analysis, clone and copy-move detection, lighting and shadow consistency, C2PA… Use when fact-checking a photo or video, checking for a deepfake or AI-generated image, spotting manipulation, or testing whether footage is recycled or miscaptioned

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Import any source into an Obsidian vault as well-linked atomic notes: social posts, Telegram HTML exports, ChatGPT JSON, voice-note transcripts, WhatsApp exports, Apple Notes, Notion dumps, pasted text, articles. Handles structure, frontmatter, provenance, dedup against existing notes and a closing RAG reindex. Triggers: "/obsidian-ingest", "import this into the vault", "file this".

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Record provenance for an Agentic MIKE+ step via the mike-plus MCP server — a manifest.json tying the model copy, inputs, edits, run QA status, result files and figures together, each file with a sha256. Use at the end of every run/compare/plot task (successful or failed) so the work can be audited, re-compared or reproduced later. No license required.

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Operational guide for the NV oOS Model Foundry (Proposal 067) — the cluster fine-tuning flywheel. Phase 1 shipped: the Corpus Foundry (Pro addon, addons/pro/includes/model-foundry/) — consent-gated, deduplicated, PII-scrubbed corpus exporters (trajectory_v1, preference_v1, docs_v1) with deterministic holdout splits and R12 provenance manifests. Covers the governance contract, row schemas, output layout, filter seams, the registration pattern for foundry tool slices, how to add a new exporter, and the test/validation gates. Use when extending the Corpus Foundry, debugging corpus exports, wiring a new data source, or continuing Phases 2–6 (train-worker, registry/serving, eval gate, fleet sharing).

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Build qualified prospect lists using an explicit ICP, source provenance, qualification criteria, scoring, enrichment hygiene and outreach-compliance checks.

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Find useful moments in long-form video, create ranked short clips, reframe them, and generate captions without losing source provenance.

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Upscale and prepare a selected video parent for delivery while preserving provenance and checking that enhancement did not create new defects.

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Apply a short artistic effect or VFX transformation to an approved media parent with explicit intent, provenance, and safety labeling.

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Turn an authorized long-form video into a ranked set of YouTube Shorts candidates with vertical framing, captions, timestamps, and source provenance.

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Capture and retrieve durable YYLO Ledger artifact Records with intentional profiles, payload modes, provenance, retention, and secret-safe immutable evidence.

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The AI native file format. EXIF for AI — stamps every file with trust scores, source provenance, and compliance metadata. Embeds into 20+ formats (DOCX, PDF, images, code). EU AI Act, SOX, HIPAA aud

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Preserve and inventory scan archives while retaining capture variants, hashes, calibration, and frame provenance.

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Implement software supply chain integrity verification for container builds using the in-toto framework to create cryptographically signed attestations across CI/CD pipeline steps.

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Plan and execute reproducible computational research with explicit inputs, environments, provenance, deterministic steps, validation, and workspace outputs. Use for analyses that must be rerun, audited, or handed to another researcher.

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