「engineer」の検索結果

Knowledge base from the Expanded Guidance for NASA Systems Engineering (NASA/SP-2016-6105-SUPPL, Volume 1, March 2016) — the practitioner-depth supplement to the NASA SE Handbook. Use for the load-bearing mechanics the handbook compresses: the SE Engine's per-phase cadence and the iterative-vs-recursive distinction, the AS9100 crosswalk, life-cycle tailoring vs. customization and the Compliance Matrix, the recursive system-design consistency loop (ConOps vs. Operations Concept, requirement flow/type/ownership, successive refinement), product realization (verify/qualify/accept/certify cardinality, protoflight, 'test the way we fly' coverage, two-form transition), and the crosscutting technical-management procedures (six-step scheduling, cost-estimating method selection, JCL, interface document family, RIDM/CRM, CM baselines, EVM arithmetic, MOE/MOP/KPP/TPM, the three required leading indicators, and the decision-analysis method catalog). This is the DEPTH layer — it deliberately does NOT restate the base definitions; pair it with the nasa-se-handbook pack. Scope: NASA space-flight SE practice (Vol 1 Practices); thin on the Vol 2 process-appendix detail, on non-NASA/commercial life cycles, and on software-engineering methodology.

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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 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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Prepares a change request pack (description and justification, scope, schedule, implementation plan, risk as stated, rollback, test evidence as provided, communications, approvals route) from an engineer's notes, ticket or pull request description, with UNKNOWN for anything not provided and a question list for the engineer. Never rates risk or marks a change tested or approved. Use when the user asks to "write the change request for this", "fill in the change ticket from my notes", "prepare the CAB submission" or "turn this pull request into an RFC". Do not use for the step-by-step procedure with commands and rollback, use runbook-drafter instead; for release notes describing what shipped, use release-notes-writer. Drafts for human review; never approves, authorises or signs off.

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PRを出す前に、自分のgit diffをGoogle Engineering Practicesの チェックリストでセルフレビューする。実装が一段落して、commit・push・PRの 直前に使う。

6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, an

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Sales enablement, technical presales and deal acceleration for B2B SaaS revenue teams. Use this skill when planning outbound campaigns, running discovery calls, building deal strategy, creating battle cards, writing proposals, responding to RFPs, preparing a product demo, scoping a proof of concept or pilot, answering a security questionnaire, executing sales sequences, or coaching qualification. Also triggers on: outbound, cold outreach, deal strategy, pipeline, battle card, proposal, RFP, discovery call, MEDDPICC, sales sequence, qualification, sales engineer, solutions engineer, presales, demo plan, demo script, POC, proof of concept, pilot success criteria, technical evaluation, technical win, security questionnaire, vendor security review, SOC 2 questionnaire, technical objection, mutual action plan, MAP, close plan, mutual close plan, buyer enablement, the deal is stalled, deal stalled after the demo, how do I get this deal to close, close date keeps slipping, JOLT, buyer indecision, no-decision loss.

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Developer marketing and developer relations for B2B SaaS with technical audiences. Use this skill when the buyer or user is a developer: documentation as a marketing surface, quickstarts and time-to-first-call, SDKs and sample apps, developer community and DevRel programs, open-source strategy, and technical content that survives engineer scrutiny. Also triggers on: developer marketing, DevRel, developer relations, developer experience, DX, docs, documentation, quickstart, SDK, API marketing, open source strategy, developer community, technical content, dev audience, hacker news, engineering blog.

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Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.

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Engineering operating model for teams where AI agents generate a large share of implementation output.

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Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing.

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Expert en data science (pandas, numpy, scikit-learn, feature engineering, model selection)

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Data Scientist IA — Expert en data science (pandas, numpy, scikit-learn, EDA, feature engineering, statistical analysis)

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Expert en ingénierie du contexte IA (compression de prompts, chunking, gestion tokens, optimisation fenêtre)

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Expert en direction artistique IA (Midjourney, DALL-E, Stable Diffusion, prompt engineering, cohérence de style)

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Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring.

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Transforms user prompts into optimized prompts using frameworks (RTF, RISEN, Chain of Thought, RODES, Chain of Density, RACE, RISE, STAR, SOAP, CLEAR, GROW)

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Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability.

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BDB Engineering Godmode, enforcing strict Domain-Driven Design, TypeScript strictness, and Clean Architecture. Use when implementing backend/fullstack features or running the systematic 5-step debugging triage.

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Reference for how BDB structures autonomous software engineering work — the seven-node dispatcher graph (Architect, TechLead, UI/UX, Engineering, Media/EventTech, Reviewer, Shipping) that /startcycle-graph actually runs. Use when you need the high-level lifecycle framing without inventing your own process.

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Frontend design blueprints with craftsmanship scoring, self-critique loops, and engineering handoff. Aesthetic intelligence against AI homogeneity. Use for: UI/UX design specs, design system creation, visual direction exploration, component design review, design-to-engineering handoff.

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ソフトウェア・エンジニアリングのための倫理集。開発時に必ず意識するものです。

Use when: implement a scoped ticket, fix a bug, add tests, prepare a PR-ready code change, or reuse a standard builder workflow across software engineer agents.

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An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.

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。