「engineer」の検索結果

Systems engineering covering requirements management, the V-model lifecycle, integration planning, verification and validation (V&V), interface control, trade studies, and the NASA Systems Engineering Handbook. Spans the full lifecycle from concept through operations and disposal. Includes requirements decomposition, traceability matrices, configuration management, and technical performance measures. Use when managing complex system development, writing requirements, planning integration, or conducting V&V activities.

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Transition from a hands-on "bricklayer" to a high-level "architect" by managing a fleet of autonomous AI agents. Use this when you need to scale engineering output with a small team, handle repetitive migrations/bug fixes, or onboard engineers to complex legacy codebases.

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。

Use whenever the user wants to find, shortlist, vet, or enrich US software development firms — custom software, web development, mobile app development, backend/API development, DevOps/cloud, system integration, and hosting. Triggers on "find a software dev shop in Austin", "shortlist three custom-software firms with healthcare experience", "we need a mobile app developer for our iOS launch", or "pull contact info for these 10 dev shop domains", even when described indirectly (build a tool, ship a feature, technical partner). Drives the ServiceGraph API (api.servicegraph.co) — a 100k+ US firm catalog filterable by industry, services, location, size, ratings. Defer to find-web-developer for strictly website/landing-page projects. Defer AI/ML, ML pipelines, model building, and data-engineering asks — those are a sibling industry, not software development. Skip in-house engineer hires, code-writing/debugging tasks, cloud-product comparisons, hardware/civil engineering, non-US firms, individual freelancers.

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。

[Deprecated] engineering bridge merged. Use `cm-mcp-engineering` instead.

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。

Nature-style engineering manuscript reviewer skill for testing devices, robots, platforms, systems, algorithms embodied in physical systems and engineering methods against requirements, validation, benchmarking, uncertainty, robustness and failure modes.

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Executes authorized phishing simulation campaigns to assess an organization's susceptibility to email-based social engineering attacks. The tester designs realistic phishing scenarios, builds credential harvesting infrastructure, sends targeted phishing emails, and tracks open rates, click-through rates, and credential submission rates to measure human security awareness. Activates for requests involving phishing simulation, social engineering assessment, email security testing, or security awareness measurement.

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Decompile, analyze, and reverse engineer Android applications (APKs). Utilize tools like JADX, Apktool, and dex2jar to extract source code (Java/Kotlin), analyze manifest configurations (Intents, Activities), and identify hardcoded secrets or insecure API endpoints.

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。

Reverse engineers malware written in less-common compiled languages (Nim, Crystal, V, Zig, D) by detecting language-specific runtime signatures, name-mangling, and runtime strings to orient analysis. Activates for requests to reverse Nim malware, identify the source language of an exotic binary, or analyze Crystal/V/Zig/D compiled samples.

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Reverse engineers ARM/AArch64 malware by identifying the architecture and instruction set state (ARM/Thumb), parsing ELF/Mach-O ARM headers, and orienting analysis around the ARM calling convention. Activates for requests to reverse ARM binaries, analyze AArch64 malware, or handle ARM/Thumb instruction-set decoding.

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Reverse engineers binaries with Binary Ninja using its analysis stack and Python API to enumerate functions, navigate IL levels (LLIL/MLIL/HLIL), and automate annotation and extraction. Activates for requests to reverse a binary with Binary Ninja, script the Binary Ninja API, or work with its intermediate languages.

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Reverse engineers binaries using radare2/rizin interactively, covering analysis initialization, navigation, disassembly and graph views, patching, and the visual mode workflow. Activates for requests to reverse a binary with radare2 or rizin, learn r2 commands, or navigate and disassemble a sample in r2.

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Uses Ghidra to disassemble and decompile a binary, navigate to key routines via imports and strings, annotate decompiled code, and run headless scripts to automate extraction of C2, crypto, and config. Activates for requests to reverse engineer with Ghidra, decompile a binary, or script Ghidra headless analysis.

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。

Use when a task needs the judgment of a systems engineer/architect working at the whole-program level — decomposing requirements across hardware/software/network subsystems, writing or reviewing an interface control document (ICD), making a build-vs-buy tradeoff for a subsystem, or tracing a verification failure back through the requirements baseline. Distinct from a software engineer (owns one service's code) or DevOps/SRE (owns operational reliability) — this role owns the technical baseline for a whole system-of-systems and is accountable when subsystems built by different teams or vendors don't fit together.

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Use when a task needs the judgment of a Civil Engineer — sizing stormwater detention/drainage systems, checking foundation bearing capacity against a geotechnical report, verifying IBC/ASCE 7 code compliance for site civil work, reviewing construction RFIs against stamped drawings, or evaluating value-engineering changes for safety-margin impact.

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。

Use when a task needs the judgment of a Civil Engineering Technician — running and interpreting a concrete field acceptance test (slump, air content, cylinder breaks against ACI 318 criteria), verifying soil compaction against a Proctor density curve, reconciling a concrete or rebar quantity takeoff against a pay application, or documenting a field condition that deviates from the approved plans for the engineer of record.

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。

Generates structured engineering specs (backend or frontend) from feature descriptions, designs, or ticket references. Triggers on "spec", "story", "ticket", "engineering spec", "write a story for", "write a spec for", "create a ticket for".

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Design and implement features from raw data for machine learning models. Use when the user says "engineer features", "feature engineering for X", "create features from this dataset", "transform these variables", "encode categoricals", "handle skew", "create interaction features", "lag features", "extract features from text or dates", "improve model performance with better features", or wants to move from raw columns to model-ready inputs.

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Universal prompt engineering techniques for any LLM. Use when crafting, optimizing, or reviewing prompts for AI models. Triggers on requests like "improve this prompt", "write a system prompt", "optimize my instructions", "help me prompt engineer", "audit this prompt", "review my prompt", or when building agentic systems that need structured prompts.

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。

Prevents overengineering when designing, implementing, refactoring, or reviewing software. Use when the user asks to implement a feature, add an abstraction, refactor code, review a design, simplify existing code, or make an architectural decision. Also triggers when the user asks if code is too complex, whether to abstract something, or how to structure something. Do not use for deployment, infrastructure configuration, or debugging runtime errors unrelated to code structure.

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MUST USE for any real runtime debugging across ANY language or binary — crashes, silent failures, wrong responses, stuck processes, memory leaks, async misbehavior, unexplained timing, reverse engineering. Runs a hypothesis-driven loop: form ≥3 hypotheses, investigate in parallel, after 2 failed rounds spawn Oracles from orthogonal angles, confirm root cause, lock with a failing test, fix minimally, QA by actually USING the system, scrub artifacts. The actual HOW lives in `references/` — READ THEM. Triggers: 'debug this', 'why is X not working', 'hanging', 'attach a debugger', 'reverse engineer', 'pwndbg', 'gdb', 'lldb', 'node inspect', 'tsx debug', 'pdb', 'dlv', 'delve', 'rust-gdb', 'set a breakpoint', 'context window exploded', 'why is the response empty', 'attach the debugger', 'debug it', 'why is this happening', 'trace this bug', 'reproduce and fix', 'silent failure', 'HTTP 200 but empty', 'why did it stop', 'inspect the binary', 'reverse engineering', 'playwright'.

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Runtime router for context decisions during a task — decide read-more / stop / ask / assume / delegate, pick a minimal read-set, and time compaction. Front-page mental model is Write / Select / Compress / Isolate; a lazy pattern index routes to 25 context-engineering patterns, one reference per decision. Use when context is near budget, a long session is degrading, you must choose what to read for a task, or you are deciding /compact vs /clear vs a sub-agent. NOT for structural .claude/ overhead audits (see mk:context-audit); NOT for owning persistence (see mk:memory); NOT for USD cost (see mk:budget).

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Prepare high-quality datasets for LLM fine-tuning with filtering, deduplication, augmentation, and RLHF data formatting. Activate on: fine-tuning data, training data curation, RLHF dataset, data quality filtering, SFT dataset. NOT for: model training infrastructure (ai-engineer), prompt engineering without fine-tuning (prompt-engineer).

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。

dbt Core/Cloud data transformations, testing, documentation, and CI/CD. Activate on: dbt, data transformation, analytics engineering, ref, source, staging model, mart, dbt test. NOT for: orchestration/scheduling (use airflow-dag-orchestrator), data warehouse tuning (use data-warehouse-optimizer).

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Design decisions for when and what sounds to use in web apps, mobile apps, and desktop native apps — plus how to create them with AI. Covers UI sound vocabulary (confirms, alerts, transitions, errors), platform conventions (iOS, macOS, Material), creating sounds with AI tools (ElevenLabs, AudioCraft, Stable Audio), Web Audio API for procedural sounds, haptic-audio pairing, and the psychology of satisfying vs. annoying UI sounds. Activate on 'app sounds', 'UI sounds', 'notification sound', 'sound design app', 'earcons', 'sonic branding', 'interaction sounds', 'feedback sounds', 'app audio', 'satisfying sounds'. NOT for professional audio engineering (use sound-engineer), Windows 3.1 retro sounds specifically (use win31-audio-design), music composition (use sound-engineer).

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。