'classification' 검색 결과

Reconstructed reference notes on EU and UK medical-device software, built from six sources captured 2026-10-13: MDCG 2019-11 rev.1 (June 2025), MDCG 2020-6 (April 2020), MDCG 2020-13 (July 2020), MDCG 2020-7 (April 2020), MDCG 2020-8 (April 2020), and the MHRA software-applications guidance (2023-07-01) with its stand-alone software PDF. Use for SaMD qualification and classification, legacy-device clinical evidence, the clinical evaluation assessment report, the PMCF plan, and the PMCF evaluation report, under EU MDCG guidance or UK MHRA guidance. SCOPE LIMITS: EU MDR and UK MHRA device software only; no IEC 62304, ISO 13485, or ISO 14971 text; no MDR or IVDR regulation body text; MDCG guidance is not legally binding; MHRA guidance is UK-specific; synthesized notes, not legal advice. LICENCE: Per-document: European Commission reuse policy (Decision 2011/833/EU, CC BY 4.0, attribution and indication of changes) for the MDCG documents; Open Government Licence v3.0 for the MHRA guidance; reconstructed notes with attribution; official PDFs not redistributed.

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Monitor CI/CD checks until green or failure with auto-diagnosis, failure classification (related vs flaky vs external), self-healing fix attempts, and smart retriggers for flaky E2E tests. Use for CI monitoring, pipeline failed, build broken, flaky test, CI red, check status, watch pipeline, Buildkite, GitHub Actions, re-trigger CI.

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Knowledge base from MIL-HDBK-61B, the DoD Configuration Management (CM) guidance handbook (7 April 2020, cataloged with Change 1, 2025). Use for defense CM across the acquisition and sustainment life cycle: the five CM functions (planning, identification, control/change management, status accounting, verification & audit), the three configuration baselines (FBL/ABL/PBL) and FCD→ACD→PCD documentation, configuration items (CIs/HWCI/CSCI), change instruments (ECP/NOR/RFV), the CCB and Class I/II classification, CDCA/AA authority, FCA/PCA audits, data management and data rights (CDRL/DID, DFARS rights ladder, master/authoritative source), and tailoring CM by phase (EIA-649-1 R/T/NR matrix, Appendix C templates), plus digital-era CM (digital twin, viewpoints, MOSA). NOTE: 61B is advisory guidance that adopts and points to the SAE EIA-649 / EIA-649-1 / GEIA-HB-649 suite for the authoritative CM requirements — this pack names and describes that suite but does not reproduce its copyrighted requirement text. Thin on: the EIA-649 requirement clauses themselves, form-level detail (DD-form field instructions, full DID content), and non-DoD/commercial CM frameworks.

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Knowledge base from NASA NPR 7150.2D — NASA Software Engineering Requirements. Use for NASA's Agency-wide software-engineering requirements: the software classification scheme (Classes A–F), the Requirements Mapping Matrix and class-driven applicability, tailoring and Technical Authority governance, the SWE-### 'shall' requirements across software management (Ch 3) and the engineering life cycle (Ch 4), the supporting disciplines (configuration management, risk, peer reviews/inspections, measurement, non-conformance — Ch 5), safety-critical and cybersecurity requirements, IV&V applicability, traceability, and the Appendix A definitions. Does not cover detailed software-assurance/safety procedures (see NASA-STD-8739.8), implementation how-to and document content (see NASA-HDBK-2203), the systems-engineering process framework (see NPR 7123.1), or program/project management (see NPR 7120.5).

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Knowledge base from EU Artificial Intelligence Act — Regulation (EU) 2024/1689. Use for AI system classification (high-risk vs prohibited vs general), conformity assessment procedures, GPAI model obligations, transparency and disclosure requirements, governance structure (AI Office / AI Board), penalty tiers, value-chain operator obligations, post-market monitoring, and Annex III use-case analysis. Does not cover EU liability rules for AI (separate proposed directive) or implementation guidance documents issued after July 2024.

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Scans .NET code for ~50 performance anti-patterns across async, memory, strings, collections, LINQ, regex, serialization, and I/O with tiered severity classification. Use when analyzing .NET code for optimization opportunities, reviewing hot paths, or auditing allocation-heavy patterns.

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Machine learning in Python with scikit-learn. Use for classification, regression, clustering, model evaluation, and ML pipelines.

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Azure AI Content Safety SDK for Python. Use for detecting harmful content in text and images with multi-severity classification.

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Classify ideation input, run structural classification, seed fractal ideation folders, and persist engagement/expansion mode

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Manage API versioning and evolution with URL/header/query strategies, deprecation workflows, breaking change classification, sunset headers, and consumer-driven contract testing. Use when designing versioning strategy, deprecating endpoints, or evolving API contracts.

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Use when working with Codeball — codeBall AI-powered pull request risk assessment including automatic PR quality scoring, risk classification, and auto-approval for low-risk changes. Use when configuring CodeBall for automated PR triage, risk-based review routing, and review workload optimization.

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Zero-shot image classification and image-text search.

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Use before starting any significant build or before shipping: problem-framing pre-flight, one-way/two-way door classification, ADR-lite decision logging, and post-ship outcome loop. Complements godmode-shipping (technical gate) and bdbresilience (error recovery).

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Use when: investigating a legacy, inherited, third-party-built, or otherwise newly-unfamiliar codebase before scoping a migration or major refactor. Formalizes a repeatable five-step investigation pass — entry-point census, business-rule classification, side-effect/idempotency cataloging, implicit-contract discovery, semantic-duplication detection — plus a canonical per-project Open Questions document. Requires checkable signals: ticket keywords or prior documentation gaps. Not a mandatory gate; reserved for entry-point-heavy work in code areas with no prior investigation record.

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Workflow-state classification vocabulary for inter-team relay. Maps inbound signals from the remote agent to a routing decision. Intentionally narrow — states and destinations only.

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Use when: 'intake this repo/skill', 'evaluate external skill', 'should we adopt X', a candidate repo URL plus an adoption question, or any external skill/workflow/tool surfaces as a possible fit for Agistra. Structured front-door evaluation of external skills, workflows, and similar capability artifacts before any install, promotion, or adaptation decision. Ends in exactly one classification: Install candidate, Adapt concept, or Reject. Never install first — intake first.

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Azure AI Content Safety SDK for Python. Use for detecting harmful content in text and images with multi-severity classification.

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Implements data loss prevention policies using Microsoft Purview to protect sensitive information across Exchange Online, SharePoint, OneDrive, Teams, endpoint devices, and Power BI. The analyst configures sensitivity labels with encryption and content marking, creates DLP policies using built-in and custom sensitive information types with regex patterns, Dağıt:s endpoint DLP rules to control file operations on Windows and macOS devices, and monitors policy effectiveness through Activity Expl...

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Implementing Cloud Data Loss Prevention (DLP) using Amazon Macie, Azure Information Protection, and Google Cloud DLP API to discover, classify, and protect sensitive data across cloud storage, databases, and data pipelines.

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tespit etme
unauthorized SaaS and cloud service usage (shadow IT) by analyzing proxy logs, DNS query logs, and netflow data using Python pandas for traffic pattern analysis and domain classification.

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Implement data handling for Adobe APIs including PII redaction in logs, Firefly content policy compliance, PDF document data classification, and GDPR/CCPA data subject access requests via Adobe Privacy Service. Trigger with phrases like "adobe data", "adobe PII", "adobe GDPR", "adobe data retention", "adobe privacy", "adobe content policy".

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「学内業務で AI を使う前に『禁止／限定／推奨／開放』のどれに該当するか判定したい」 「既存の機密情報 3 段分類と業務場面を接続したい」 「部署ごとの AI 利用方針を体系化したい」 場面で使う横断スキル。

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Classify financial PDFs by extracting company identity, document type, and date metadata. Validates ticker via Yahoo Finance with multi-step fallback. Uses a Python orchestration script for validation, file modification, and writing markdown.

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Detect unauthorized SaaS and cloud service usage (shadow IT) by analyzing proxy logs, DNS query logs, and netflow data using Python pandas for traffic pattern analysis and domain classification.

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