Results for “systems”

Knowledge base from NASA-STD-8729.1A — NASA's R&M (Reliability and Maintainability) standard covering spaceflight and support systems (Revision A, 2017-06-13). Use for NASA's objectives-driven R&M framework — the R&M objectives hierarchy (one Top Objective decomposed into four sub-objectives, paired with tailorable strategies), how R&M requirements are established inside the SMA Plan required by NPR 7120.5, the SMA Technical Authority concurrence/independent-evaluation governance, milestone vs. readiness review gates, the reliability/maintainability/availability vocabulary (Ai vs. Ao, the failure causal chain, risk as a triplet), and the R&M Evidentiary Methods catalogue (FMEA/FMECA, FTA, RBDA, RCM, LORA, plus the space-environment and parts-pedigree analyses). Scoped to NASA spaceflight and support systems and to assurance planning: it is an objectives-and-strategies standard, so it deliberately does NOT prescribe step-by-step analysis procedures (those live in the referenced NASA Preferred Reliability Practices) and is thin on facility R&M, detailed math/derivations, and non-NASA or commercial life cycles.

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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 FAA Requirements Engineering Management Handbook (DOT/FAA/AR-08/32). Use for writing requirements for real-time, embedded control systems — the avionics/medical slant — via the Handbook's eleven recommended practices: system overview and boundary (monitored/controlled variables), operational concepts (use cases), environmental assumptions, change-tolerant functional architecture (dependency diagrams), safety-driven architecture revision (FHA/PSSA/fault tree), system modes, detailed behavior and performance requirements (ideal value + tolerance + latency), the four-variable model bridge to software requirements (MON/CON/NAT/REQ, IN'/REQ'/OUT', DO-178B mapping), subsystem allocation, and rationale. Carries the Isolette Thermostat and Flight Control System worked examples. Slanted toward control systems with sensors and actuators; thin on information-system / enterprise / agile requirements, on requirements-management tooling, and on the formal-methods internals of SCR/RSML/SpecTRM (it names them, does not re-teach them). Does not reproduce the IEEE/DO-178B/ARP standards it cites.

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Knowledge base from DOE Systems Engineering Methodology (SEM) Version 3 — the DOE SDLC for IT investments. Use for staged systems lifecycle planning through maintenance, Stage Exits, Structured Walkthroughs, In-Stage Assessments, requirements traceability, and CMM-aligned process discipline. Covers SEM3 (2002) guidance only; does not replace modern DevSecOps tooling standards, DOE O 413.3B capital-asset directives, or current CIO policy supersessions.

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Knowledge base from the OUSD(R&E) guidebook Implementing a MOSA in DoD Programs (Feb 2025). Use for the Modular Open Systems Approach as a DoD acquisition + design discipline: the statutory/regulatory basis (10 U.S.C. 4401–4403, FY2017/FY2021 NDAA, DFARS), the five OUSD(R&E) pillars, the plan→modularize→identify→define→standardize interface lifecycle, technical levers (WBS/MIL-STD-881F taxonomy, standards profiling, API-first software, COTS/cybersecurity), MOSA assessment metrics and tools (PART/OAAT/KOSS/SEAM/MAUT), technology-change and DMSMS management, roadmaps, MOSA across the six AAF pathways, and contracting/IP/data-rights mechanics. SCOPE LIMITS: this is U.S. DoD weapon-system acquisition policy, not a general open-architecture or interface-standards reference — it names standards (FACE, OMS, DISR) without reproducing them, is light on detailed engineering of any specific standard, and does not cover the underlying SE processes (use dau-se-guidebook / nasa-npr-7123) or general systems-engineering theory (use sebok).

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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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Knowledge base from the FAA Human Factors Design Standard (HF-STD-001B, 2016) — the FAA's consolidated human-factors / human-systems-integration design criteria for systems it manages, operates, or maintains. Use for requirement-level design rules on: general human-factors design principles; automation and function allocation; designing equipment for maintenance; displays, controls, and visual indicators; alarms, audio, and voice communications; the computer-human interface (information presentation, coding, interaction styles, windows, dialogue); keyboards, input devices, and workstation/workplace ergonomics; system security, personnel safety, environment, anthropometry, and user documentation; plus intended-use and tailoring guidance. It is a quantified, source-attributed synthesis (heavily citing MIL-STD-1472G, MIL-HDBK-759C, DOD-HFDG-ATCCS, NUREG-0700, NASA-STD-3000, ANSI, OSHA, ISO 9241). Complements nasa-hsi as a parallel HSI tradition from the civil-aviation domain. Scope limits: it is design criteria, NOT an HSI lifecycle/process model (use nasa-hsi, nasa-npr-7123, or dau-se-guidebook for process); it does NOT reproduce the third-party standards it cites; it is FAA-acquisition-oriented (ATC, towers, TRACONs, airway facilities), not a general consumer-UX guide; and it is thin on modern touchscreen/mobile, web, and AI-driven interface patterns and on quantitative human-reliability analysis.

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Use when working with Aws Systems Manager — aWS Systems Manager parameter store management, session management, patch compliance tracking, and Run Command analysis. Covers parameter inventory, managed instance status, automation execution, maintenance windows, and inventory collection.

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This skill should be used when the user asks to "escalate privileges on Linux", "find privesc vectors on Linux systems", "exploit sudo misconfigurations", "abuse SUID binaries", "exploit cron jobs for root access", "enumerate Linux systems for privilege escalation", or "gain root access from low-privilege shell". It provides comprehensive techniques for identifying and exploiting privilege escalation paths on Linux systems.

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This skill covers detecting cyber attacks targeting Supervisory Control and Data Acquisition (SCADA) systems including man-in-the-middle attacks on industrial protocols, unauthorized command injection into PLCs, HMI compromise, historian data manipulation, and denial-of-service against control system communications. It leverages OT-specific intrusion detection systems, industrial protocol anomaly detection, and process data analytics to identify attacks that traditional IT security tools miss.

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Analyze nonlinear dynamical systems — phase portraits, fixed points, stability analysis, bifurcation diagrams, Poincare sections, Lyapunov exponents, and chaos detection. Use for any autonomous or non-autonomous ODE system where qualitative behavior matters.

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Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)

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Expert firmware analyst specializing in embedded systems, IoT security, and hardware reverse engineering. Masters firmware extraction, analysis, and vulnerability research for routers, IoT devices, automotive systems, and industrial controllers. Use PROACTIVELY for firmware security audits, IoT penetration testing, or embedded systems research.

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This skill covers detecting cyber attacks targeting Supervisory Control and Data Acquisition (SCADA) systems including man-in-the-middle attacks on industrial protocols, unauthorized command injection into PLCs, HMI compromise, historian data manipulation, and denial-of-service against control system communications. It leverages OT-specific intrusion detection systems, industrial protocol anomaly detection, and process data analytics to identify attacks that traditional IT security tools miss.

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Run ongoing quality checks across OpenClaw subsystems, reproduce bugs, and verify fixes to their underlying causes. Track real environment tests, stress tests, independent reviews, and confirmed merges in an evidence report that supports resuming work.

• Investigate bugs across multiple OpenClaw subsystems
• Verify fixes through regression tests and independent review
• Check real provider, UI, and package behavior

Measure and improve delays in realtime dashboards, data feeds, queues, and caches. Track response times, data age, and waiting work separately, then verify that changes improve the actual behavior of a deployed system.

• Investigating delays in realtime dashboards
• Checking whether streamed data is still fresh
• Reducing queue backlogs

Save, organize, synchronize, and retrieve documents, conversations, and project information across local files, memory tools, knowledge repositories, and databases. Check for duplicates before storing information and choose a suitable home for each type of knowledge.

• Saving documents and conversations to a knowledge base
• Consolidating duplicate notes
• Synchronizing knowledge across storage systems

Build HTML/CSS pages using visual specifications from 54 established websites, including Stripe, Linear, and Vercel. Templates provide colors, typography, spacing, component styles, and layouts that adapt to different screen sizes.

• Creating a landing page inspired by Stripe
• Designing a dashboard inspired by Linear
• Styling a documentation website

Plan the retirement of old APIs, features, and systems, then move their users to replacements in stages. The skill also guides database schema changes through additive steps that keep old and new application code compatible during rollout.

• Moving users from an old API to its replacement
• Planning the retirement of an unused feature
• Renaming production database columns in stages

Produces detailed HTML or Markdown reports on pull requests, branches, or large code contributions. It explains how the system works, ranks concrete findings, compares designs when needed, and recommends whether and how to merge the changes.

• Reviewing a large pull request in depth
• Explaining a contribution’s design and behavior
• Comparing a design with similar systems

Develop visual effects with GLSL, a language for graphics programs called shaders. Apply ray marching, fluid simulation, particle systems, and rule-based generation to create hero visuals and still frames of motion.

• Creating hero visuals for a page
• Developing fluid simulation visuals
• Building particle effects

Create a brand overview image combining logo concepts, colors, typography, and examples across digital and physical materials. The skill bases the visual direction on the brand’s purpose and audience, arranging connected panels in a clear presentation grid.

• Presenting a new product’s visual identity
• Exploring logo concepts grounded in brand meaning
• Reviewing colors, typography, and application examples together

Build document search with AgentDB that finds related content by meaning. Store documents as numerical representations, combine similarity search with metadata filters, and retrieve relevant passages for systems that generate answers using source material.

• Build answers grounded in retrieved documents
• Add meaning-based search to a knowledge base
• Find documents with similar content

Build advanced AgentDB workflows with database synchronization, multiple databases, and search that combines meaning with attribute filters. Covers similarity measures, diverse search results, and operational patterns for distributed AI applications.

• Sharing stored patterns across multiple AgentDB instances
• Filtering semantic search by date or category
• Separating knowledge and conversation databases