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Set up ruvector@0.3.3 with supporting packages, register its MCP server for agent access, and verify installation with diagnostic commands and smoke tests. Helps prepare the dependencies needed for initial use of /vector subcommands.
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Set up Python projects with uv, manage required packages, and create isolated environments for each project. Covers Python versions, dependency updates, migration from existing tools, and lockfiles that record package versions for repeatable installations.
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uv (Astral 製 高速 Python パッケージ / プロジェクトマネージャー) のリファレンス。 pyproject.toml, uv.lock, uv init / add / sync / run / lock, uv pip, uv venv, uvx / uv tool, uv python install / pin, PEP 723, tool.uv.sources, uv build / publish, uv_build, uv.toml, UV_* 環境変数, uv auth, Docker / GitHub Actions / PyTorch 連携。
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Verify RuView changes with Rust and Python tests, output hashes that detect processing changes, and firmware hash records. Packages test logs and audit evidence into a witness bundle that recipients can check with a single command.
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Integrate RuView through shell commands, an HTTP API, or WebAssembly builds, which run compiled code in browsers and supported edge devices. Covers survivor scan commands, result exports, API hosting, and browser or ESP32 deployment paths.
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Work on advanced RuView sensing with signals from multiple WiFi nodes. Explore position estimation, three-dimensional occupancy reconstruction, changes in movement patterns over time, and defenses against suspicious signals, with guidance on implementation modules and validation.
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Start using RuView by choosing a Docker demo, a source build, or a live ESP32 setup. The skill guides the next concrete step, distinguishing sample data playback from sensing an actual room with WiFi signals.
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Train and evaluate RuView models using Wi-Fi measurements for pose estimation, retrieval, and adaptation to different environments. Follow laptop or GCloud GPU workflows, then package and publish trained models to Hugging Face.
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Get RuView running with a Docker demo using simulated WiFi signal data, source checks, or live sensing with supported hardware. Choose a starting path based on available equipment and open a working sensing dashboard.
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Configure a RuView sensing deployment, including WiFi settings, detection thresholds, processing on devices, and coordinated nodes. It covers device provisioning, server options, firmware variants, and Cognitum Seed integration to adjust how the system operates.
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Verify RuView pipeline output against a reference hash, package evidence for others to check, and review accuracy statements. The skill distinguishes measured, claimed, and synthetic results and requires real device logs for hardware validation.
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Set up RuView sensing nodes by building and flashing ESP32 firmware, configuring WiFi and the receiving address, and checking serial logs. Diagnose missing CSI streams, the WiFi signal measurements sent by sensing nodes.
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Run RuView applications for occupancy, breathing and heart rate, activity, pose, sleep, and room changes. Choose suitable examples and startup commands for a simulated demonstration or a working ESP32 setup that captures WiFi signal measurements.
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Connect RuView sensing data to AI agents through tools for presence, breathing, and heart rate. Explore agent coordination and prototype storing agent state and decisions alongside sensing data in a shared RVF container.
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Group related code files by analyzing a graph of their connections with ruvector. Review group assignments, community labels, and links between files to understand code organization and investigate files that stand apart.
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Generate RuVLLM settings for local language model inference and create MicroLoRA or SONA instances. Adjust their small internal weights using quality scores as feedback, with instances held in server memory for subsequent adaptation calls.
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在 Windows/macOS/Linux 上使用 `uv` 执行 Python 运行、依赖同步、锁文件管理、Python 版本管理与工具命令（`uv run`、`uv sync`、`uv lock`、`uv python`、`uv tool`）。当任务涉及“用 uv 运行脚本/命令”“临时依赖（`--with`）”“锁文件一致性（`--locked`/`--frozen`）”“跨平台 shell（bash/zsh/PowerShell）下排查 uv 执行问题”时使用此 skill。
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Set up an ESP32-S3/C6 sensing node for RuView by choosing firmware, checking and flashing binaries, and configuring WiFi and capture settings. Use boot logs to confirm CSI, measurements of the WiFi channel, is being captured.
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Inspect RuView sensing hardware, including ESP32 nodes, mmWave radars, and LiDAR distance sensors. Find connected devices, read logs and live measurements, and use SSH to access supported read-only tools on another machine.
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半导体芯片制造工艺 (Semiconductor Process Engineering / 半导体芯片制造工艺 — 以「物理化学极限 + 良率经济双重约束」为特征的集成电路前道/后道制造工艺认知操作系统：从「器件架构与微缩路线（planar→FinFET→GAA nanosheet→CFET、等效微缩 DTCO/STCO、背面供电 BSPDN）→ 工艺整合（FEOL/MOL/BEOL 模块协同、PDK、流片 tape-out）→ 单元工艺（光刻 litho（DUV 浸没式 193i / EUV 13.5nm / 多重曝光 SADP-SAQP / OPC / mask）、刻蚀 etch（RIE/ICP/原子层刻蚀 ALE）、薄膜沉积 depo（CVD/PECVD/LPCVD/ALD/PVD/外延 epi/电镀 Cu）、掺杂 doping（离子注入 ion implant / 退火 RTA-laser anneal）、CMP 化学机械抛光、清洗 cleaning）→ 量测检测 metrology（CD-SEM / OCD / overlay / 椭偏 ellipsometry / 缺陷检测 defect inspection / e-beam）→ 良率工程 yield（缺陷密度 / SPC 统计过程控制 / FDC / APC / DOE 实验设计 / 良率爬坡 yield ramp）→ 先进封装 advanced packaging（2.5D/3D、CoWoS、HBM、混合键合 hybrid bonding、chiplet、TSV）」的完整器件-工艺-良率-量产决策链。覆盖 (a) 第一性张力 — **物理/化学/材料的硬约束（量子隧穿、短沟道效应、RC 延迟、热预算、缺陷物理、ppb 级污染控制）⇄ 良率/成本/产能的经济约束（每片晶圆成本、良率是主经济指标、D0 缺陷密度、wafer-per-hour、折旧）**；**持续微缩/摩尔定律（dimensional + equivalent scaling、节点路线图 N5/N3/N2/A16/A14、high-NA EUV）⇄ 物理极限/微缩放缓（原子尺度、漏电、功耗墙、成本墙、'摩尔定律已死'之争）**；**协同优化/整合（DTCO 设计-工艺协同、STCO 系统-工艺协同、模块间 trade-off）⇄ 单模块极致优化（单一 unit process 工艺窗口）**；**工艺配方的 tacit 手艺（process recipe 调试、工艺窗口、缺陷根因分析、量产 ramp 的经验）⇄ 可建模/可仿真/数据驱动（TCAD 仿真、ML 良率分析、APC/FDC、数字孪生）**；**性能-功耗-面积-成本 PPAC 四角权衡 ⇄ 良率/可制造性 DFM 优先**；**自主可控/国产替代（卡脖子、EUV 出口管制、设备/材料/EDA 国产化、SMIC/长江存储/长鑫/中微/北方华创/上海微电子）⇄ 全球高度分工（ASML 光刻垄断、AMAT/Lam/TEL/KLA 设备、TSMC/Samsung/Intel 代工、全球供应链）**。(b) 核心工作流 / pipeline（最标准、最易蒸高质量 + CLI 化）— 新节点/新工艺导入：器件目标与 PPAC 定义 → 工艺整合方案与 PDK → 单元工艺开发与配方调试（DOE 找工艺窗口）→ 量测建立与 SPC → 良率爬坡（缺陷分类 + 根因分析 + 工艺改善）→ 量产移交与持续改善。具体单元工艺 SOP：光刻（曝光条件 / OPC / overlay 控制 / 多重曝光分解）、刻蚀（选择比 / profile / loading effect / ALE）、沉积（台阶覆盖 / 应力 / 均匀性）、CMP（去除速率 / 碟形 dishing / 侵蚀 erosion）、量测与缺陷分析、良率分析与 FA 失效分析。(c) 工具栈 — 设备（光刻 ASML/上海微电子 SMEE；刻蚀+沉积 AMAT/Lam/TEL/中微 AMEC/北方华创 Naura；离子注入 AMAT/Axcelis；CMP AMAT/荏原 Ebara；量测检测 KLA/AMAT/中科飞测/上海精测）、EDA/TCAD/仿真（Synopsys Sentaurus/Silvaco/Coventor SEMulator3D、OPC Calibre/Synopsys）、良率与制造软件（SPC、MES、FDC/APC、缺陷分析 yield management、PWS）、材料（光刻胶 photoresist、CMP 浆料 slurry、特气、靶材、掩膜 mask blank）。(d) 知识正典 — 半导体工艺 canon 横跨器件物理与制造工程：核心教材（Plummer/Deal/Griffin《Silicon VLSI Technology》、Sze《Physics of Semiconductor Devices》《VLSI Technology》、Wolf & Tauber《Silicon Processing for the VLSI Era》、Campbell《Fabrication Engineering at the Micro and Nanoscale》、May & Spanos《Fundamentals of Semiconductor Manufacturing and Process Control》、Quirk & Serda《Semiconductor Manufacturing Technology》、肖国勇/施敏中文教材）、路线图（ITRS/IRDS、IEEE 国际器件与系统路线图）、顶会论文（IEDM、Symposium on VLSI Technology、IRPS 可靠性、SPIE Advanced Lithography、ISSCC、DAC）、行业分析（SemiAnalysis、TechInsights、IC Knowledge）。(e) figures/流派 — 学术与器件奠基（Gordon Moore 摩尔、Robert Dennard 登纳德微缩、胡正明 Chenming Hu FinFET/UTB-SOI 发明人 UC Berkeley、刘明 Tsu-Jae King Liu Berkeley、Krishna Saraswat Stanford、Mark Bohr Intel 工艺架构）、产业领袖与工艺大师（张忠谋 Morris Chang TSMC、林本坚 Burn-Jeng Lin 浸没式光刻、Martin van den Brink ASML、米玉杰/梁孟松 SMIC/三星工艺、Mark Liu/魏哲家 TSMC）、行业分析师（Dylan Patel SemiAnalysis、Scotten Jones IC Knowledge、TechInsights 拆解、Handel Jones）、科普与拆解（Asianometry、The Chip Letter、半导体行业观察）。流派分歧：等效微缩派 vs 摩尔定律已死派、前道微缩 vs 先进封装 chiplet 异构集成、dimensional scaling vs DTCO 协同优化、IDM vs 纯代工 foundry 模式、自主可控/国产替代路线 vs 全球分工。(f) 行业话术/黑话 — 制程节点 node / 流片 tape-out / 工艺窗口 process window / 良率 yield / 缺陷密度 D0 / 关键尺寸 CD / 套刻 overlay / 掩膜 reticle-mask / 光刻胶 PR / 浸没式 immersion / EUV/DUV / 多重曝光 multi-patterning SADP-SAQP / OPC / 刻蚀选择比 selectivity / profile / loading effect / 原子层刻蚀 ALE / 台阶覆盖 step coverage / 外延 epi / 离子注入 implant / 退火 anneal / RTA / CMP / dishing-erosion / FEOL-MOL-BEOL / FinFET / GAA / 纳米片 nanosheet / CFET / 背面供电 BSPDN / high-NA / PPAC / DTCO-STCO / DFM / SPC / FDC-APC / DOE / WAT-PCM / 良率爬坡 yield ramp / FA 失效分析 / particle 颗粒 / 洁净室 cleanroom-级别 / 12寸-8寸 wafer / chiplet / CoWoS / HBM / 混合键合 hybrid bonding / TSV / PDK / 等效微缩. (g) 争议/批判 — 「摩尔定律是否已死 vs 等效微缩继续」、「节点命名营销化（N3/N2 ≠ 物理 3nm/2nm，gate pitch/metal pitch 才是真尺度）」、「前道微缩 vs 先进封装异构集成谁是未来」、「自主可控/国产替代的现实（EUV 卡脖子、设备/材料/EDA 国产化进度、出口管制 entity list、良率与成熟度差距）」、「良率/成本/性能三角权衡」、「人才稀缺与工艺 know-how 的 tacit 性」、「中国大基金/产能扩张是否过剩」、「制裁与技术封锁」。(h) 大量隐性工艺手艺（process recipe 调试与工艺窗口寻找、缺陷根因分析与 FA、量产良率爬坡的经验判
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Set up Python projects and standalone scripts with uv for dependencies, ruff for code checks and formatting, and ty for type checking. Configure tests and project files, or migrate existing workflows when requested.
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Discover and integrate RuV Stack projects when the user asks where to begin with ruvnet, which RuV tools fit a goal, or how the stack fits together.
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Python のスクリプト作成・依存管理・環境構築を行うときに使う。uv 優先の実行方法、PEP 723 インライン依存、uv add / uv run --with の使い分けを定める。
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Train and evaluate models that estimate human poses from WiFi signals. Compare results against a model that always predicts the average pose, check for data leakage, and report measured improvements with clear evaluation conditions.
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