53 件 ・ 関連度順
概要と使いどころ
Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.
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。
対話型のコマンド操作が動いているtmuxの画面を確認し、文字やキーを送るスキル。セッションの一覧、出力履歴の取得、入力待ちの監視に使えます。
Use when the user wants to customize keyboard shortcuts, rebind keys, add chord bindings, or modify ~/.claude/keybindings.json. Examples: "rebind ctrl+s", "add a chord shortcut", "change the submit key", "customize keybindings".
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。
Set up a working ML training/inference environment on NVIDIA DGX Spark (GB10, aarch64, CUDA 13). Use when installing PyTorch/Unsloth/TRL/vLLM on DGX Spark, hitting libcudart or wheel-ABI errors on aarch64, or choosing between NGC containers and bare pip installs.
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Use this skill when wiring the DOCA SHA Offload Engine (an OpenSSL ENGINE) into an existing OpenSSL pipeline to offload one-shot SHA-1, SHA-256, or SHA-512 (EVP_Digest) onto DOCA SHA hardware without rewriting against doca-sha. Covers engine load mechanics (`openssl engine dynamic`, `set_pci_addr` ctrl, `-engine_impl`), the SHA-224 negative test that proves offload engaged, the message-size window where offload beats CPU SHA, and engine-vs-library selection. Trigger even when the user does not say "DOCA SHA Offload Engine" or "OpenSSL ENGINE" — typical implicit phrasings: "speed up openssl SHA on BlueField", "offload SHA without code changes", "is openssl using the accelerator or falling back to software", "prove DOCA SHA actually ran", "openssl dgst hashed but I'm not sure it was offloaded". Refuse and route elsewhere for new SHA pipelines (use doca-sha), MD5 / SHA-3 / SHA-224 / HMAC-SHA offload, incremental hashing via chained `EVP_DigestUpdate`, or OpenSSL PROVIDER authoring.
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。
Scroll mouse wheel in specified direction. When you need to scroll page/document content, navigate long lists, or zoom in/out with Ctrl. Directions - up/down/left/right.
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。
Set up infrastructure for fine-tuning LLMs with QLoRA, LoRA, and full fine-tuning using Hugging Face TRL, Axolotl, and distributed training with DeepSpeed or FSDP. Covers dataset prep, training runs, and model export.
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。
RF Toolbox and RF Blockset in MATLAB -- S-parameter I/O, network conversions (S/Z/Y/ABCD/T/H/G, mixed-mode), cascade/de-embedding, rfbudget analysis, circuit composition, matching networks, amplifier stability, mixer spurs, rational fitting, SI channels, baseband processing, Circuit Envelope simulation. Trigger: sparameters, Touchstone, .s2p, .s4p, rfplot, smithplot, rfparam, rfwrite, zparameters, yparameters, abcdparameters, s2sdd, cascadesparams, deembedsparams, rfbudget, noise figure, OIP3, IIP3, amplifier, modulator, nport, rffilter, attenuator, seriesRLC, shuntRLC, lcladder, txline, circuit, setports, clone, matchingnetwork, stabilityk, stabilitymu, powergain, gammams, gammaml, mixerIMT, OpenIF, rational, rationalfit, stepresp, txlineWRLGC, rf.Amplifier, rf.Mixer, rf.Filter, rf.Sparameter, rfsystem, RF Blockset.
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使用pynput库监听键盘事件，提取按键的整数键值（如162）和按键名称（如ctrl_l），并返回标准字典格式。
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。
Train or fine-tune language models with TRL or Unsloth on Hugging Face Jobs, including SFT, DPO, GRPO, reward models, and GGUF conversion. Use for cloud LLM training; use huggingface-vision-trainer for vision tasks.
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。
Trigger for: OT project description, OTA scope, Research OT under 10 U.S.C. 4021, Prototype OT under 10 U.S.C. 4022, follow-on production scope under 10 U.S.C. 4022(f), milestone-based project scope, prototype objective, phase or go/no-go structure, BAA white-paper conversion, SOO conversion, transition planning, or scope reduction before OT cost analysis. Produce a milestone-based .docx plus a separate chat-only handoff to OT Cost Analysis. Use TRL only when the project supports it. Never place cost estimates, funding profiles, contribution calculations, prices, or skill-routing content in the document. Do NOT use for a FAR SOW/PWS, OT cost analysis, IGCE, agreement terms and conditions, or an Agreements Officer's authority, participant-status, successful-completion, or follow-on determination.
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。
Think and work like an expert Electromagnetics Engineer. Use when a task calls for Electromagnetics Engineer judgment. Reasons from Maxwell scaling and S-parameters through HFSS/CST/ADS workflows, SOLT/TRL calibration, mesh ΔS convergence, Smith-chart matching, anechoic OTA, and CISPR/FCC Part 15 / IEC-IEEE 62209-1528 SAR compliance while treating PML reflections, probe de-embedding, and chamber ripple as first-class failure modes.
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 implementing/debugging Ctrl+O Expanded Transcript (second view) and thinking persistence in the REPL.
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。
Trigger this skill when the user wants to train, fine-tune, or adapt Gemma models (e.g. SFT, DPO, RLHF, Reward Modeling) on local hardware. Covers TRL, Unsloth, dataset preparation, validation, and GGUF/LiteRT conversion.
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。
Atuin replaces your shell history file with a searchable SQLite database that records directory, exit code, duration and host for every command, and can sync it end-to-end encrypted between machines. Use when a user asks to "search shell history", "replace Ctrl+R", "sync history across machines", "find the command I ran in this directory", "import bash/zsh history", "see my most used commands" or "self-host Atuin".
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。
Hardware interfacing and peripheral management for Zephyr RTOS. Covers the sensor subsystem (channels, triggers, fetch/get), pin control (Pinctrl) and multiplexing, GPIO management using Devicetree specs, and SoC-level hardware configurations. Trigger when adding new hardware components, configuring pinmux, or developing sensor-based applications.
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。
Atuin replaces your existing shell history with a SQLite database that records additional context like exit codes, session IDs, working directories, and command durations. It provides encrypted cross-machine sync and a full-screen fuzzy search UI bound to Ctrl-R.
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。
SBTI 赛博人格测试 — 互联网最火的人格测试，27 种沙雕人格等你解锁。可以测自己，也可以测你的 AI agent。 **当以下情况时使用此 Skill**： (1) 用户说"做 SBTI 测试"、"SBTI"、"赛博人格测试"、"测人格"、"人格测试" (2) 用户说"给龙虾做测试"、"测一下我的 agent" (3) 用户提到 CTRL、BOSS、SEXY、IMSB、DEAD 等 SBTI 人格代码 (4) 用户说"来个性格测试"、"整点沙雕测试"
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Cross-platform desktop development for macOS and Windows -- platform abstractions, UI convention differences, and multi-platform CI/CD. Activate on: cross-platform app, macOS and Windows, platform differences, DMG installer, MSI installer, NSIS installer, Cmd vs Ctrl, platform abstractions, menu bar differences, native conventions. NOT for: mobile apps, web-only apps, Tauri-specific internals (use rust-tauri-development).
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。
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).
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。
Knowledge base from the GAO Technology Readiness Assessment Guide (GAO-20-48G, 2020). Use for evaluating technology maturity in acquisition programs and projects: Technology Readiness Levels (TRL 1–9) and related scales (MRL, IRL), identifying Critical Technology Elements (CTEs), the five-step process for a high-quality TRA, the four characteristics of a reliable TRA (comprehensive, well-documented, credible, defensible), Technology Maturation Plans (TMPs), and using TRA results in knowledge-based, milestone decisions. Assessment/maturity oriented: it measures readiness and frames risk — it is not a full SE process model, cost-estimating, or schedule guide (see sibling packs for those).
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。
Knowledge base from the DoD Manufacturing Readiness Level (MRL) Deskbook (Version 2022, OSD ManTech / Joint Service–Industry MRL Working Group). Use for assessing and managing manufacturing maturity and producibility risk in defense acquisition: the 10-level MRL scale and its non-linear, target-not-grade nature; the five demonstration environments (laboratory → production-relevant → production-representative → pilot line → production line); the nine manufacturing-risk threads and sub-threads; how MRL targets map onto Adaptive Acquisition Framework milestones (MRL 4/6/7/8/9); conducting an MRL Assessment; building a Manufacturing Maturation Plan; putting MRL maturity into contract language (Section L/M, SOW, AS6500A, DIDs); tailoring criteria for S&T, single-system, ship, sustainment, and industry contexts; and screening operational-technology cybersecurity. Manufacturing-readiness focused — it measures producibility risk and frames mitigation; it is NOT a technology-readiness (TRL) guide, a full systems-engineering process model, a cost-estimating or schedule guide, or a deep cybersecurity audit method (see sibling packs for those).
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。
Ctrl k GitBook Assistant Ask License GitHub More Home AutoGPT Platform Integrations Contribute AutoGPT Classic GitBook Assistant GitBook Assistant arr
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。
Add search to a GNOME app — header-bar search button toggling a GtkSearchBar with SearchEntry, live filtering, Ctrl+F, and empty "no results" state. Use when adding search or filtering to lists or content views.
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。