“kernel”的搜索结果

Set up the BSP source workspace: Linux_for_Tegra overlay tracker, bsp_sources, Crosstool-NG toolchain. Use after jetson-init-image; not for fetching inputs.

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Use when you need to print Jetson device info (module model, L4T version, kernel, OS version, current power mode) from a running Jetson target. This is an example skill.

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Use this skill when the user is doing hands-on DOCA Device Emulation on a BlueField DPU — exposing a custom emulated PCIe device the host sees as a real peripheral while DPU-side code runs the backend, picking the sub-library (PCI Generic, virtio-net, virtio-fs), wiring the per-sub-library Core context plus doorbell / DMA primitives, querying `doca_devemu_*_cap_*`, or debugging DOCA_ERROR_* from a `doca_devemu_*` call. Trigger even when the user does not say "devemu" — typical implicit phrasings include "expose a custom PCIe device from BlueField to the host", "host should see a virtio NIC backed by my DPU code", "lspci does not show my emulated device", "device enumerated but no driver binds", "DPU sees nothing when host kicks the queue", or "virtio feature negotiation failed at bind". Refuse and route elsewhere for the packaged DOCA SNAP / Virtio-net Services, host-side virtio kernel drivers, backend body design, or standard BlueField NIC behavior — those belong to other skills.

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DOCA Flow packet steering on NVIDIA NICs/DPUs, linked into a C application: match/action pipes, ports, representors, forwarding, hairpin, counters. Trigger on packet steering, classifier, rule-matching, representor, hairpin or 5-tuple-to-queue questions even when "DOCA Flow" is not named. Remote or non-C++ control over gRPC belongs to doca-flow-grpc-server. Also covers pipe validation before hardware programming, Flow/DOCA version match, and Flow API errors. First app: follow the local bundle (doca-setup, doca-programming-guide, doca-flow, doca-public-knowledge-map). Stage 1 pull nvcr.io/nvidia/doca/doca (NGC catalog tag, never invent); Stage 2 needs a real NIC. Then ls samples inside the container; never emit main.c, a Dockerfile, or hand-typed -l flags. Route software-only host pipelines, plain DPDK `rte_flow`, kernel TC, OVS, BFB bring-up, and DPU OS installation elsewhere. DPU OS installation is destructive and always requires explicit confirmation.

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Prepare changes to live BlueField or ConnectX hardware with baseline records, a separate management connection, a maintenance window, and rehearsal on matching test hardware. Define verification and rollback steps before applying firmware or configuration changes.

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• Planning a BlueField mode change
• Preparing an SR-IOV change and restart
• Reviewing firmware update and rollback procedures

Scaffold a complete knowledge system. Detects platform, conducts conversation, derives configuration, generates everything. Validates against 15 kernel primitives. Triggers on "/setup", "/setup --advanced", "set up my knowledge system", "create my vault".

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Provides binary exploitation techniques for CTF challenges. Use when you already have a vulnerable native target or service and need to turn memory corruption or low-level primitives into code execution or privilege escalation, such as buffer overflows, format strings, heap bugs, ROP, ret2libc, shellcode, kernel exploitation, seccomp bypass, sandbox escape, or Windows/Linux exploit chains. Do not use it when the main blocker is understanding what the binary does; use reverse engineering first. Do not use it for pure web bugs, disk or packet forensics, or standalone crypto/math challenges.

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Linux privilege escalation playbook. Use when you have low-privilege shell access and need to escalate to root via SUID/SGID binaries, capabilities, cron abuse, kernel exploits, misconfigurations, or credential harvesting on Linux systems.

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Defining and extracting kernel constants for syzkaller syzlang descriptions

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Write efficient C code with proper memory management, pointer arithmetic, and system calls. Handles embedded systems, kernel modules, and performance-critical code. Use PROACTIVELY for C optimization, memory issues, or system programming.

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Attribute GPU time in one sglang-omni stage to specific lines of `sglang_omni/` python source. Drives the `llm-torch-profiler-analysis` backend with a mapping/formal trace pair plus steady-state gates, and carries the rules for reading its three tables on an omni stage. Use when a stage is slower than expected and the question is which python code is the bottleneck, not which kernel is hot.

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Save provenance, decisions, phases, context policy, and next steps as kernel.handoff/v1. Triggers: handoff, save, pause, context, continue later.

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Save bounded progress as kernel.checkpoint/v1 for exact resume after reset. Triggers: checkpoint, save progress, context reset, compact soon, long task.

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Show KERNEL help. The unified skill system, state operations, agents, current plugin status. Triggers: help, how, what, skills, commands.

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Release-gate sequence: validate → review → push → tag. Wraps kernel:git mechanics with the pre-ship safety chain. Triggers: ship, release, push to main, ready to merge, deploy.

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Initialize KERNEL links and AgentDB in a confirmed Vaults directory.

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Use when optimizing the performance of an existing native scalar expression in the datafusion-comet-spark-expr crate (native/spark-expr/) — casts, string/JSON/array/math kernels that run per-row or per-batch. Covers benchmarking, keeping output bit-identical, and the no-regression gate. Not for adding new expressions (use implement-comet-expression) or wiring upstream functions (use wire-datafusion-function).

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Detect single-nucleotide crosslink (CL) sites in CLIP-seq data using truncation patterns (iCLIP/eCLIP CITS), crosslink-induced mutations (HITS-CLIP CIMS deletions, PAR-CLIP T-to-C), or HMM/kernel-density methods (PureCLIP, PARalyzer, CTK). Use when single-nucleotide resolution is required for motif registration (mCross), allele-specific binding (BEAPR), variant-effect prediction, or comparing crosslink chemistry across CLIP variants.

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Optimize MATLAB design files for GPU Coder to generate faster CUDA code. Iteratively profiles, rewrites, and benchmarks until performance targets are met or diagnostics are resolved. Use when asked to: optimize for GPU Coder, improve GPU codegen performance, profile generated GPU/CUDA code, profile GPU MEX, fix gpuPerformanceAnalyzer diagnostics, speed up GPU MEX, reduce GPU memory transfers, improve kernel parallelism, rewrite MATLAB for CUDA, or run gpuPerformanceAnalyzer.

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Optimize Linux system performance. Configure kernel parameters, analyze bottlenecks, and tune resources. Use when improving system performance.

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Use eBPF for deep kernel-level observability — trace syscalls, network flows, and application behavior without code changes using Cilium, Tetragon, and bpftrace.

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LuisaCompute API guide covering the DSL (kernels, callables, structs, buffers, atomics, control flow, dispatch), the runtime API (Context, Device, Stream, buffers, images, ray tracing, rasterization), AST usage markers and AST-to-XIR lowering, the core library (traits, types, logging, math, fiber, allocators, STL containers), and vstd containers (HashMap, queues, pools, variant, smart pointers, utilities).

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Compile hand-written native compute shaders and dispatch them from a Stream through NativeShaderExt — HLSL on dx/vk (DXC), GLSL on vk (glslang), CUDA C++ on cuda (NVRTC) — covering compile/reflect/load, NativeShaderLauncher binding by index/register+space/positional, uniforms (root constants / push constants / scalar kernel params), the per-argument Usage contract, include dirs, and lifetime.

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Manual AST construction with FunctionBuilder for kernels and callables without DSL sugar.

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