「software engineering」の検索結果

Applies the reasoning, principles, and mental models of Sebastian Thrun (robotics and self-driving cars pioneer, founder of Google X, Waymo, Udacity, Stanford University). Reach for this skill whenever Claude is asked to advise on hardware/software systems engineering, autonomous vehicles, moonshot ideation, probabilistic robotics (SLAM), or leading high-stakes engineering teams. Trigger this skill for discussions on democratizing education, regulating AI, transitioning from academic research to product development, or managing technical teams with empathy. Use it to shift focus from incremental component debates to end-to-end execution and audacious goals.

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AI code reviewer grounded in classic software engineering books for catching design smells, coupling issues, and architectural risks.

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Specialized agentic and context engineering prompt methodology for Claude. Use when designing AI agents — system prompts, tool definitions, context window architectures, failure mode planning, multi-agent coordination, agent evaluation. Trigger on: "design an agent," "build a system prompt for an agent," "architect an agent's context," "design tool definitions," "agent system prompt," "context window architecture," "tool interface design," "evaluate agent behavior," "multi-agent coordination," "context engineering for agents," "ACI design." Provides 12 tested approaches for agent architecture (designing agents, not using them). Do NOT use for evaluating or scoring individual non-agent prompts (use rootnode-prompt-validation if available). Do NOT use for general software architecture that hosts agents (use standard technical reasoning for that).

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Analyze recent code changes via git history and extract software engineering lessons. Use when the user asks 'what is the lesson here?', 'what can I learn from this?', 'engineering takeaway', 'what did I just learn?', 'reflect on this code', or wants to extract principles from recent work.

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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 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 NASA NPR 7123.1D — Systems Engineering Processes and Requirements. Use for the 17 common SE processes (SE Engine), life-cycle reviews and entrance/success criteria, SEMP structure and content, ETA roles and tailoring/customizing rules, Technology Readiness Levels (TRL 1–9), SE work product maturity terminology, contracted project oversight, and MOE/MOP/TPM measurement frameworks. Does not cover NASA programme financial management, human spaceflight medical standards, or software engineering requirements in depth (see NPR 7150.2 for the latter).

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Data Engineer IA — Expert en pipelines de données (ETL, data lakes, dbt, Airflow, streaming). Construit des pipelines fiables, scalables et auditable.

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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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Generate flat, minimal light/dark-aware SVG diagrams as standalone HTML files, using a unified educational visual language with 9 semantic color ramps, sentence-case typography, and automatic dark mode. Best suited for educational and non-software visuals — physics setups, chemistry mechanisms, math curves, physical objects (aircraft, turbines, smartphones, mechanical watches), anatomy, floor plans, cross-sections, narrative journeys (lifecycle of X, process of Y), hub-spoke system integrations (smart city, IoT), and exploded layer views. If a more specialized skill exists for the subject (dedicated software/cloud architecture, hand-drawn sketches, animated explainers, etc.), prefer that — otherwise this skill can also serve as a general-purpose SVG diagram fallback with a clean educational look. Ships with 15 example diagrams.

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Comprehensive offensive methodology for software supply chain attacks covering the full kill chain from reconnaissance through exploitation. Addresses dependency confusion across npm, PyPI, and NuGet ecosystems where internal registry override allows an attacker to inject malicious packages that shadow private dependencies. Covers typosquatting techniques for popular packages, compromised package injection via maintainer account takeover or social engineering, and build system attacks through Makefile injection, setup.py install hooks, and npm postinstall scripts. Extends into CI/CD artifact tampering where build outputs are replaced or modified in transit, code signing abuse through stolen or self-signed certificates, upstream repository compromise via commit injection or force-push to trusted repos, and container image supply chain attacks including base image trojaning and registry confusion. Maps to MITRE ATT&CK T1195.001 (Supply Chain Compromise: Compromise Software Dependencies and Development Tools...

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Make sure to use this skill whenever the user mentions anything related to job searching on Akademikernes Jobbank, jobbank.dk, or looking for academic or highly educated positions in Denmark — even if they don't mention jobbank.dk explicitly. Also invoke this skill for questions about Danish job listings, graduate trainee positions, Ph.d. jobs, or finding work in specific industries or regions in Denmark. Trigger phrases include: jobbank, akademikernes jobbank, jobs denmark, academic jobs denmark, find job denmark, highly educated jobs, graduate job denmark, trainee position denmark, ph.d. position denmark, postdoc denmark, studiejob, fuldtidsjob, deltidsjob, vikariat, freelance job, praktikplads, job søgning, jobsøgning, søg job, ledige stillinger, nye jobs, it jobs denmark, engineering jobs denmark, marketing jobs denmark, finance jobs denmark, healthcare jobs denmark, remote job denmark, fjernarbejde, job københavn, job aarhus, job odense, nyuddannede job, job til nyuddannede, international job denmark, jobbank søgning, find stilling, data scientist job, software developer job, projektleder stilling, konsulent job, data analyse job.

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Understand anti-reversing, obfuscation, and protection techniques encountered during software analysis. Use this skill when analyzing malware evasion techniques, when implementing anti-debugging protections for CTF challenges, when reverse engineering packed binaries, or when building security research tools that need to detect virtualized environments.

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Optimize resumes for software engineering, PM, and technical roles

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Use when the user asks to run Codex CLI (codex exec, codex resume) or references OpenAI Codex for code analysis, refactoring, or automated editing. Uses GPT-5.2 by default for state-of-the-art software engineering.

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Custom VM and bytecode reverse engineering playbook. Use when CTF challenges or protected software implement custom virtual machines with proprietary bytecode, dispatcher loops, or maze-style challenges.

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You are a code refactoring expert specializing in clean code principles, SOLID design patterns, and modern software engineering best practices. Analyze and refactor the provided code to improve its quality, maintainability, and performance.

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You are a code refactoring expert specializing in clean code principles, SOLID design patterns, and modern software engineering best practices. Analyze and refactor the provided code to improve its quality, maintainability, and performance.

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Test quality review drawing on twelve classic engineering books — with primary focus on xUnit Test Patterns, The Art of Unit Testing, How Google Tests Software, and Working Effectively with Legacy Code — that diagnoses structural problems in an existing test suite: brittleness, mock abuse, coverage illusions, slow execution, poor readability. Triggers when: user asks about test quality, shares test files for review, or expresses frustration: "tests keep breaking whenever I change anything", "our tests take forever", "tests pass but bugs still reach production", or "we have too many mocks". Do NOT trigger for: writing new tests from scratch (use the regular test-writing workflow) or testing framework/syntax questions — this skill reviews an existing suite for structural quality problems, not individual test authoring.

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This skill should be used when writing, reviewing, or refactoring Python code. Covers code integration, idiomatic patterns, docstring formatting, anti-abstraction rules, and software engineering basics.

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Use when the user needs to plan, de-risk, or ground a software engineering / computer science undergraduate thesis from a real codebase before final writing. Trigger for requests such as "根据项目写毕业论文", "先做论文大纲", "用真实项目材料规划论文", "检查论文是否脱离代码", "根据检测报告降查重", "根据AIGC报告改写定稿", "继续在手改初稿上改", or when an existing draft thesis must be reworked against a school format sample. Produces an evidence-backed outline, chapter rewrite plan, figure plan, and handoff package for academic-paper-composer.

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Use when the user needs to turn a real software engineering / computer science project and an existing thesis draft into a submission-ready undergraduate thesis manuscript. Trigger for requests such as "根据项目把论文改成定稿", "按学校模板排版成最终版", "复制初稿后生成定稿 Word", "为定稿降查重", "根据PaperPass报告降AIGC", "继续在手改初稿上改", "恢复原来的图表和数据库说明", or when academic-paper-strategist has already produced an evidence-backed rewrite plan. Outputs a cleaned manuscript, final DOCX workflow, and a separate rework report.

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PavedPath Code is the code-focused edition of PavedPath. It helps agents solve software engineering problems by finding proven implementation paths from GitHub repositories, issues, pull requests, discussions, code examples, release notes, and open-source evidence.

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Deliberate-choice procedure for a medium-to-high-stakes engineering fork — when the first plausible solution (the instinct, the default next-token pull) would be costly to get wrong. Fires AUTONOMOUSLY: invoke proactively whenever a fork fits, never wait to be asked — mechanical triggers include a fix failing for the 2nd-3rd time, adding a dependency, schema/migration design, deleting or deprecating things others depend on, changing a public API, choosing an architecture or stack. Refuse the premature collapse: hold real options open (urge · contrarian · synthesis · out-of-box · intuitive dots · precedent · first-principles), ground each branch in at least one fact from outside the model (codebase, docs, benchmark, spike), future-model consequences (blast radius, reversibility, maintenance, pre-mortem), collapse by deliberate choice, then try to refute the winner before acting. Log the decision and rejected branches in docs/decision_logs/. Not for routine calls — a decision worth more than one forward pass.

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