「machine」の検索結果

Re-ingest-correctness SOP for production RAG. Activate when a calling agent builds, reviews, or debugs an ingestion pipeline that runs more than once over a changing corpus — scheduled re-index, incremental updates, CI re-ingest, or a "retrieval has duplicates / shows deleted docs" bug. Encodes the rule — **ingestion must be idempotent: a document's content hash decides insert/update/skip, so re-running over unchanged docs is a no-op** — plus the docstore + doc-hash upsert machinery (LlamaIndex `IngestionPipeline` + `DocstoreStrategy`), the delete-propagation problem, and cross-framework equivalents (LangChain `index()` + `RecordManager`, manual hash table). ENHANCE overlay over [[llamaindex]]: the IngestionPipeline exists in the base skill but the re-ingest-correctness contract is not surfaced.

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 a user asks to edit Russian prose that sounds formulaic or machine-generated while preserving its meaning and authorial voice.

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 a user asks to edit Russian prose that sounds formulaic or machine-generated while preserving its meaning and authorial voice.

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。

Web archiving and retrieval via Wayback Machine and Archive.today. Use to preserve content, reach dead pages, or save evidence.

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。

High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning 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。

Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus

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。

Molecular machine learning toolkit. Property prediction (ADMET, toxicity), GNNs (GCN, MPNN), MoleculeNet benchmarks, pretrained models, featurization, for drug discovery ML.

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。

Produce a scored heuristic critique of the UI using Nielsen's 10 + 6 design laws + optional persona walkthroughs. Outputs a machine-parseable scorecard plus a 0-100 UsabilityScore (the judged companion to the deterministic UICraftScore). Invoke when the user asks for heuristic on their UI, or mentions 'heuristic' alongside design / UI / frontend work.

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。

State-first design pass — inventories and implements all non-happy states (loading, empty, error, partial, conflict, offline) before the happy path, and refactors impossible boolean state to proper state machines. Use when starting a new screen, reviewing an existing one for edge-case gaps, or when the user says "handle the error state" / "add loading states" / "what happens when data is missing". Invoke when the user asks for unhappy on their UI, or mentions 'unhappy' alongside design / UI / frontend work.

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。

Move issues and external PRs through a state machine of triage roles, categorise, verify, grill if needed, and write agent-ready briefs.

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。

Parallel/distributed computing for larger-than-RAM data. Components: DataFrames (parallel pandas), Arrays (parallel NumPy), Bags, Futures, Schedulers. Scales laptop to HPC cluster. For single-machine speed use polars; for out-of-core without cluster use vaex.

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。

Classical ML in Python: classification, regression, clustering, dim reduction, evaluation, tuning, preprocessing pipelines. Linear models, tree ensembles, SVMs, K-Means, PCA, t-SNE. Use PyTorch/TF for deep learning; XGBoost/LightGBM for scale.

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。

OneClickVirt operations skill for managing containers, virtual machines, provider nodes, health checks, and metrics through MCP.

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。

Bringing up a complete ROS2 system on a robot's onboard computer: systemd services, launch file composition, ordered startup, and production monitoring. Use this skill when configuring a robot to start ROS2 nodes on boot, writing systemd unit files for ROS2 launch, composing layered launch files for full robot stacks, setting up watchdog monitoring, configuring udev rules for deterministic device naming, or debugging boot-time race conditions. Trigger whenever the user mentions robot bringup, robot startup, systemd for ROS2, ROS2 on boot, launch file composition, robot boot sequence, udev rules for cameras or serial ports, automatic restart for ROS2 nodes, network configuration for multi-machine ROS2, log rotation for robots, graceful shutdown of robot stacks, or SSH-based remote debugging. Also trigger for sourcing workspaces in systemd, ordered startup with health checks, or running ROS2 systems as long-running production services. Covers systemd on Ubuntu 22.04/24.04 with ROS2 Humble, Iron, and Jazzy.

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。

Predict construction project costs using Machine Learning. Use Linear Regression, K-Nearest Neighbors, and Random Forest models on historical project data. Train, evaluate, and deploy cost prediction models.

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。

Forecast project outcomes using historical data: cost overruns, schedule delays, risk probabilities. Machine learning models for construction prediction.

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。

Apply machine learning for construction project risk assessment. Predict schedule delays, cost overruns, and safety incidents using historical data and project characteristics.

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。

Document UI component states (current vs expected) with transitions

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。

Installs egregore watchdog daemon via launchd or systemd for autonomous relaunching. Use when setting up egregore on a new machine. Do not use on CI/CD runners.

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。

Delegates tasks to a locally served Muse Glimmer via ollama. Use when delegation-core selects glimmer or the prompt must not leave the machine.

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 DeepChem for molecular machine learning, data loading, featurization, model training, MoleculeNet, docking, and optional backend troubleshooting.

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 DeepXDE for scientific machine learning, PINNs, DeepONet/operator learning, backend selection, training, and troubleshooting.

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。

Routes Argos Translate workflows for offline machine translation, package installation, CLI usage, runtime settings, and troubleshooting.

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。

Boot-to-root methodology for a full machine (THM/HTB/PG/CTF box, "get user.txt+root.txt", "root the box", "foothold to root"). Enforces basic-tool recon (nmap, nc, ffuf, nuclei, dig) before anything custom, wiki-first lookups, and ALWAYS pspy + linpeas/winpeas for privesc. Use when handed a box/IP to own end-to-end.

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。