104건 · 관련도순
Analyzes malicious and vulnerable Windows kernel drivers (.sys) by parsing the PE for the native subsystem, identifying DriverEntry/IRP dispatch and IOCTL handlers, and flagging BYOVD and kernel-callback abuse. Activates for requests to analyze a Windows driver, examine a .sys sample, or assess a BYOVD/kernel driver threat.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Decide whether a Pulp test earns its place — before writing it, when reviewing one, or when sweeping a subsystem for low-value, duplicated, or mis-routed tests. Applies a four-question authoring gate, a junk-pattern list, a retention bar, and an evidence template for any deletion, and routes kept tests to the right lane (required gate, pr-fast, slow, nightly). Handles "should I add a test for this", "is this test useful", "audit these tests", "reduce test time", "which tests can go", and "move toward functional tests".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Discovers requirements and designs a borderless open data lakehouse using Lakehouse for Apache Iceberg and BigQuery data agents. Use when architecting multi-cloud storage infrastructure (Cloud Storage, AWS S3, Azure Blob), establishing ingestion and AI serving subsystems, configuring Cross-Cloud Interconnect, or deploying Gemini Enterprise Agent Platform and BigQuery data agents. Don't use for single-cloud data warehouses, or when the focus is on Knowledge Catalog metadata governance and Spark-driven IDE analytics workflows (use google-cloud-solution-agentic-analytics-spark-knowledge-catalog instead).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Understand what Skmtc is, how its engine works, and the architectural invariants — for agents building or extending infrastructure *around* Skmtc rather than authoring generators or running the CLI. Covers the three-phase pipeline, the host/Worker boundary, cross-generator coordination, the manifest, the attribution / gen-maps (provenance) subsystem, the package graph, the Deno and JSR platform, and the design decisions that make Skmtc behave unlike a typical codegen tool. Use this skill when the user asks "what is Skmtc", "how does the Skmtc engine work", "explain the Skmtc architecture", or is building platform infrastructure around Skmtc — a hosted generate API, a schema or generator registry, tracing or provenance tooling, a web app or SaaS that wraps the engine, or platform-level CI integration. This skill is the system mental model. It does NOT cover authoring generators (→ skmtc-generator), running CLI commands (→ skmtc-cli), or diagnosing broken runs (→ skmtc-debug).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Design, configure, debug, and optimize OpenClaw AI agent deployments. Master guide for gateway configuration, openclaw.json settings, model routing and fallback chains, skills development and publishing, cron job scheduling, memory systems (Qdrant, Neo4j, SQLite), Docker infrastructure, and Tailscale VPN networking. Includes config analyzer that audits your openclaw.json and suggests improvements, plus health checker that validates all OpenClaw subsystems. Built for AI agents — Python stdlib only, no dependencies. Use for OpenClaw setup, gateway debugging, skill building, cron management, model optimization, cost reduction, and infrastructure troubleshooting.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Review GEODE dependency direction, composition boundaries, circular imports, and optional-dependency loading when changing package or subsystem dependencies.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Capture, gate, and query long-term project knowledge via the gated wiki subsystem: propose candidates, recall, or search; agents propose, humans approve. NOT for short-lived memory (mk:memory).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Capture, gate, query, and render long-term project knowledge through the gated `mewkit wiki` subsystem. Use to create a wiki, propose/approve candidates (scanner-gated), hand off a skill's terminal artifact as a scanned candidate, recall context, search the FTS index, or list pages. Agents may only PROPOSE candidates; canonical pages are written only via human approve. NOT for short-lived JSON memory (see mk:memory); NOT for fetching external sources (see mk:wiki-research); NOT for HTML snapshots (see mk:wiki-render).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Structured logging conventions for DraftForge backend (Django/Celery/Daphne) and frontend (React). This skill should be used when adding log statements, creating new modules, debugging with logs, or reviewing logging patterns. Covers structlog usage, system/subsystem taxonomy, required fields, log levels, and Grafana query patterns.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Document-driven workflow for implementing, reviewing, and verifying Chisel processor and memory-subsystem RTL. Use when Codex must work from maintained Architecture and Design, trace a complete integration surface, reason in synthesized-hardware terms, keep source-adjacent _codex.md summaries current, avoid redundant or overprotective logic, or run focused functional verification.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Expert-level spacecraft design covering mission analysis, subsystem design, power and thermal management, attitude control, communications, and systems engineering.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Investigate two separate areas of the codebase simultaneously and combine findings into a comparison report. Use when you need research on two independent topics at once, want to compare two subsystems, or need to understand how two components relate to each other.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Turn an idea into a Conventional Docs Decision, then a Plan: classify the request, explore options and risks through dialogue, then commit a numbered Decision that review, prototypes, and acceptance move through. Use PROACTIVELY before any creative work — new features, new subsystems, interface or dependency changes, or any open-ended design question ("what are the options", "how should I approach", "should I use X or Y", "help me decide", "write a decision", "ADR").
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
[Process] Use when a developer needs to understand and judge scoped work: a change set, subsystem, decision, plan or concept — flow, rationale, trade-offs, testing. Bugs: investigate --mode=debug; one feature's code flow: investigate.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Analyze a local codebase and infer the architecture it is actually using, including repo kind, best-fit dominant architecture, directly evidenced coexisting patterns, major subsystem exceptions, confidence, docs-vs-code drift, and repo-fit hints for downstream agents. Use when prompts ask what architecture a repo uses, whether it is really hexagonal or just layered, what subtle patterns shape a repo or slice, how a target slice fits a hybrid monorepo, whether documented architecture matches implementation, or when `/tw-tk` needs a repo-dialect preflight before making a minimal change.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Read and reason about an unfamiliar SS14 ECS subsystem before changing it.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Explain how a subsystem works, trace a runtime flow, or determine where code and ownership belong before making a change.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Explain a body of work plainly so a person actually understands it. Runs the `how` and `why` skills and weaves what they find into one clear explanation. Use for 'teach me this', 'help me really understand X', 'explain this change or subsystem to me'.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Audit a codebase or subsystem for architecture simplification - shallow or pass-through modules, scattered concepts, naming drift, sibling workflows that disagree, weak test seams. Use when the user asks what to simplify or restructure. Not for a narrow fix or for implementing an already chosen design.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Plan, scaffold, implement, and integrate a complete new emulated computer or console in the EMULATORS repository. Use when Codex creates a new machine, platform, hardware model, or emulator executable; decides which existing CPUs, chips, memory, I/O, video, sound, ROM, and framework classes to reuse or extend; or coordinates several emulator subsystems into a bootable tested system.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build an evidence-backed working model of an existing codebase. Use when you must orient in a large or unfamiliar repository; explain architecture or a subsystem; trace a request, runtime, control, or data flow for a concrete question or bug; analyze a branch, commit, diff, or pull request for behavioral impact and blast radius; or assess AI-generated or changed code against requirements, contracts, invariants, tests, and runtime evidence. Default to read-only investigation. Do not trigger only because a coding task touches an existing repository; use when understanding or verification is the requested outcome or a substantial prerequisite.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when developing, reviewing, debugging, porting, or engineering Linux kernel device drivers, kernel modules, device tree bindings, bus clients, subsystem drivers, DMA/IRQ/memory paths, userspace interfaces, or hardware bring-up plans.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Knowledge base from FAA-STD-025f (Preparation of Interface Documentation). Use for IRD/ICD family definitions, common format and content, IRD shall-requirements, ICD design characteristics, verification/VRTM, and CM/revision/approval of FAA Enterprise Subsystem interfaces. Covers rev F preparation rules only; does not replace FAA-STD-005 drawing/spec prep, Order 1800.66 full CM procedures, or non-FAA interface standards.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.