'wan' 검색 결과

Assess LoRaWAN and sub-GHz wireless devices by capturing signals, examining packet formats, and checking key reuse and replay weaknesses, where recorded messages are resent. It covers IoT networks, fixed-code remotes, and proprietary radio protocols.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing LoRaWAN keys and frame counters
• Testing fixed-code remotes for replay weaknesses
• Analyzing proprietary wireless packet formats

Monitor model training through WandB metrics to detect invalid numbers, worsening loss, and declining evaluation results. Ambiguous trends receive a Codex review, while stopped runs retain key metrics and the reason for stopping.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check loss and evaluation trends during model training
• Detect NaN values and diverging loss
• Review flat or noisy training metrics

Guides LLM compression through pruning, which removes selected model weights. Covers Wanda and SparseGPT methods without retraining, accuracy comparisons, and structured weight patterns that can accelerate inference on compatible hardware.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Trying model compression without retraining
• Comparing accuracy before and after pruning
• Choosing between Wanda and SparseGPT

Reduce LLM weight usage through pruning, which removes selected model parameters. Explore Wanda and SparseGPT for compression without retraining, choose hardware-friendly patterns for faster inference, and compare the compressed model's accuracy with the original.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compressing an LLM without retraining
• Comparing pruning methods
• Exploring faster inference on supported hardware

Track machine learning experiments with SwanLab by recording training settings, metrics, and media such as images and audio. Learn to compare runs, inspect training charts, and connect existing training code to local, cloud, or self-hosted dashboards.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing experiments with different training settings
• Viewing training metrics in a local dashboard
• Logging sample images and audio alongside metrics

Monitor machine learning training through WandB or training logs. Check for invalid numbers, worsening metrics, and stalled improvement, then report whether to continue, wait, or stop, preserving evidence and notes when stopping a run.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Monitoring loss and evaluation metrics during training
• Investigating invalid numbers or diverging training
• Assessing whether stalled training should continue

Add file-based observability, tracing, and run analysis to LLM/agent experiments — per-turn transcripts, tool calls and results, reasoning traces, token/cost accounting, per-episode artifacts, plus terminal viewers and a run-comparison report. Platform-agnostic: one small adapter covers OpenAI, Gemini/Vertex, Anthropic, LiteLLM, Tinker, vLLM, or any custom inference stack, and the same tooling then works unchanged. Use this skill whenever the user wants observability, tracing, logging, or run tracking for model experiments; asks why an agent failed a task or wants to replay/debug/diff an episode; wants to compare models, arms, or ablations; wants to instrument a new folder, repo, or platform ("add observability here", "do the same for Tinker/Gemini/OpenAI"); wants to import or tie in existing experiment results so old runs show up alongside new ones; or wants eval runner scripts where many models share one protocol. Reach for this even when they only say "log what the model is doing", "I can't tell what happened in this run", or "set up traces for this experiment".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Clean up after (or abandon) a Rails upgrade. Drop NextRails.next? and NextRails.current? branches and retire dual-boot scaffolding (Gemfile.next, Gemfile.next.lock, conditional Gemfile groups), keeping either the next or the current version. Trigger when the user says they are done with the upgrade, want to clean up dual-boot, want to drop NextRails branches, want to finish the upgrade, want to abandon or revert the upgrade attempt, want to roll back to the current Rails version, or want to pause this upgrade hop. Based on FastRuby.io's "Finishing an Upgrade" methodology, extended with an abandon/pause path.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Spin up a working LiveAvatar demo from a curated catalog — clones the reference repo, installs dependencies, provisions the LiveAvatar account, and fills the env file, then hands off the parts only a human can do. Use when: (1) User wants to try, test, see, or demo LiveAvatar, (2) User asks what LiveAvatar demos or examples exist, (3) User wants a sales agent / lead-qualification avatar, (4) User wants a LiveKit agent driving a LiveAvatar avatar, (5) User wants an avatar joining their own LiveKit room, (6) User wants a speech-to-speech avatar, a GPT-Live demo, a language-learning or tutor avatar, a customer-support avatar, a poker coach / game-style avatar, or an avatar with on-screen visuals/overlays, (7) User says "run the LiveAvatar demo", "show me LiveAvatar", "get me started with LiveAvatar", or "clone the LiveAvatar example", (8) User is evaluating LiveAvatar and wants something running before writing their own integration.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use W&B to record machine learning experiments, visualize training, and compare results. The skill also covers automated searches for training settings and version tracking for datasets and models, helping teams share experiments and trace their inputs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Logging training metrics and viewing progress in real time
• Comparing experiments with different training settings
• Searching automatically for better training settings

Set up W&B to record machine learning metrics and settings, visualize training progress, and compare experiments. Includes automated searches for training settings, version tracking for datasets and models, and shared reports for team collaboration.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Recording and visualizing training metrics
• Comparing experiments with different training settings
• Automatically searching for better training settings

Use when the user wants a YouTube video's transcript fetched, wants to summarize/analyze/quote a YouTube video by its spoken content, wants to search YouTube (globally or a channel handle's videos), wants a channel handle resolved to its channel ID, or wants the videos in a YouTube playlist. Calls the getyoutubetranscript.com public API - requires an API key (free tier available, no card required).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Extend / continue a video temporally with Pusa 2.2 in ComfyUI. Temporal flowmatching (the flowmatch_pusa scheduler + WanVideoAddPusaNoise) on the WanVideoWrapper stack with WAN 2.2 T2V A14B (HIGH/LOW) models and the Pusa V1 LoRAs, conditioning on the loaded clip via WanVideoEncode so the existing motion carries into the continuation. Covers the kijai wanvideo_2_2_14B_Pusa_extension graph, model/LoRA slots + downloads, noise/length/scheduler settings, chaining multiple extensions, VRAM tiers, gotchas, and the extend→upscale handoff.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Expert knowledge for Azure Virtual WAN development including troubleshooting, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring VWAN hubs, P2S/Always On VPN, BGP/IPsec routing, ExpressRoute/SD-WAN, or Private Link, and other Azure Virtual WAN related development tasks. Not for Azure Virtual Network (use azure-virtual-network), Azure VPN Gateway (use azure-vpn-gateway), Azure ExpressRoute (use azure-expressroute), Azure Traffic Manager (use azure-traffic-manager).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Socratic questioning partner named Gadfly for deep learning through dialogue. Use this skill when the user wants to learn, understand, or explore a topic through questions rather than explanations. Trigger when the user says "teach me about...", "help me understand...", "I want to learn...", "ask me questions about...", "quiz me", "Socratic method", "Gadfly", or wants to deeply understand something through dialogue rather than passive reading. Also trigger when the user provides a text or document and wants to be questioned about it, or wants to check their own understanding of a concept.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Authoritative reference for working with the released Wandelbots NOVA API v2 in the wandelbots-nova Python SDK. Use whenever a task touches the NOVA API, the generated `wandelbots_api_client` models (`nova.api.models.*`), motion planning requests (`plan_trajectory`, `MotionGroupSetup`, `LimitSet`, `LimitsOverride`), robot limits, or torque/velocity/acceleration/jerk limits. Reach for this skill before guessing field names or assuming a field exists — the installed client reflects the RELEASED API, while nightly specs may contain unreleased fields. Especially relevant for controller-reported limits (e.g. KUKA torque) and any "does field X exist / how is it forwarded to the planner?" question.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Find and identify the CCSPlayerPawn_SnapViewAngles function in CS2 server binary using IDA Pro MCP. Use this skill when reverse engineering CS2 server.dll or libserver.so to locate the non-virtual SnapViewAngles method, anchored on the "setang" console-command usage string and its non-teleport call branch. Emits a unique IDA-style byte signature and a func YAML. Trigger: CCSPlayerPawn_SnapViewAngles, SnapViewAngles

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Track machine learning experiments in W&B, record training settings and metrics, and compare results in dashboards. Automate searches for training settings and keep version histories for models and datasets.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing accuracy across training runs
• Monitoring training progress in dashboards
• Searching for better training settings

Run molecular property predictions, explore molecular shapes, and estimate how compounds bind to proteins through a cloud Python API. Process compound batches and connect modeling steps without maintaining local high-performance computing infrastructure.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Predicting properties across a compound library
• Exploring molecular shapes and tautomeric forms
• Estimating how compounds bind to proteins

This skill should be used when the user asks for a publication-quality scientific figure or table, wants help choosing the right chart for results, needs a paper-ready pubfig or pubtab workflow, wants a figure + companion table for a results section, wants an Excel sheet turned into publication-ready LaTeX, or wants an existing scientific figure/table reviewed and upgraded.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Run the R code review protocol on R scripts. Checks code quality, reproducibility, domain correctness, and professional standards. Produces a report without editing files. Make sure to use this skill whenever the user wants their existing R code evaluated or audited — not when they want new analysis written. Triggers include: "review my R script", "check my R code", "is my code replication-ready", "audit this R file", "does this code follow conventions", "will this reproduce", "check my analysis script", "code review", "review-r", or when the user has an existing .R file and wants quality feedback rather than new code.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Start a new research project by conducting a structured interview to formalize a research idea, then generates research questions with identification strategies and a project spec. Make sure to use this skill whenever the user wants to develop or document a new research idea — not to search for literature or data. Triggers include: "new project", "start research", "I have an idea", "help me develop this", "I want to study X", "help me formalize this idea", "what's my research question", "what identification strategy should I use", "write up my project idea", or when the user describes a topic they want to turn into a paper.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml + matplotlib/seaborn. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step pipeline an applied economist or quantitative social scientist runs on every paper — (1) data cleaning, (2) variable construction & transformation, (3) descriptive statistics & Table 1, (4) statistical diagnostic tests, (5) baseline empirical modeling, (6) robustness battery, (7) further analysis (mechanism, heterogeneity, mediation, moderation), (8) publication-ready tables & figures. **Also covers two parallel domain modes that share the same 8-step scaffolding** — **Mode A — Epidemiology / public health** (target-trial emulation via `zepid` / hand-rolled `pandas`, IPTW + g-formula + TMLE doubly-robust triplet via `zepid` / `econml` / `lifelines`, Mendelian randomization via `pymr` / `mrtool` (or `rpy2` → `MendelianRandomization`/`TwoSampleMR`), KM / AFT / Cox survival via `lifelines`, E-value sensitivity, principal stratification — STROBE / TRIPOD reporting), and **Mode B — ML causal inference** (DML via `econml.dml` / `doubleml`, S/T/X/R/DR meta-learners via `econml.metalearners` / `causalml`, causal forest via `econml.grf` / `causalml`, Dragonnet / TARNet / CEVAE neural causal via `causalml`, BCF via `pymc-bart` / `bcf-py`, matrix completion, CATE distribution + policy tree via `econml.policy` / `policytree-py`, off-policy evaluation, conformal causal via `mapie`, fairness audit via `fairlearn`, DAG learning via `causal-learn` / `cdt` / LLM-assisted). Prescribes which library to reach for at each step, shows the canonical code, and links to deeper `references/` files for variant-specific patterns. Use when the user asks for a **complete empirical analysis** in Python, wants to replicate an applied-economics paper from scratch, needs a reproducible workflow that is NOT opinionated on any single vertical package (contrast with StatsPAI), wants explicit control over every estimator and diagnostic, or asks "how do I write a full empirical pipeline in Python?". Also triggers when the user names a specific classical step in isolation — "winsorize at 1/99%", "run Breusch-Pagan", "build a Table 1 balance table", "do a placebo test", "event study plot", "mediation analysis" — and wants it wired into the broader pipeline. Mode A triggers on "target trial emulation", "IPTW", "TMLE", "Mendelian randomization", "STROBE", "公共健康", "流行病学". Mode B triggers on "DML", "double machine learning", "causal forest", "meta-learner", "Dragonnet", "BCF", "policy tree", "conformal causal", "fairness audit", "因果机器学习".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Text-to-Speech (TTS), voiceover, narration placement/sync, and custom sound effects (SFX) generator. Use when the user wants generated speech from text, wants to add/replace/align narration or voiceover for an existing video/timeline, wants to keep existing voiceover synced after visual retiming edits, needs voice audition/selection, or explicitly wants a newly generated/custom sound effect that is not available in the Sound Effects library.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.