「machine」の検索結果

Test-Driven Development workflow for Software Metrics Machine. Covers test frameworks (Vitest 4.x for CLI/REST/core/utils, Jest 30.x for webapp), test patterns (describe/it, builder pattern, mocks via vi.fn), test commands, coverage requirements, and the Red-Green-Refactor cycle. USE FOR: write tests, run tests, test this, add tests, testing, coverage, TDD, test-driven development, vitest, jest, test pattern, test convention. DO NOT USE FOR: linting (use lint skill), building (use build commands directly), debugging production issues.

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。

VitePress documentation workflow for Software Metrics Machine docs under docs/vitepress. Use when creating, updating, reviewing, or restyling SMM documentation pages, especially feature docs that must keep CLI terminal examples and dashboard views/screenshots in sync. Covers Markdown style, VitePress tabs, command examples, dashboard screenshots, sidebar links, and docs validation. USE FOR: docs/vitepress, VitePress docs, documentation style, CLI docs, dashboard docs, screenshots, feature pages. DO NOT USE FOR: application code changes unless needed to verify documented behavior.

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Expert-level materials informatics covering materials databases, machine learning for property prediction, high-throughput computation, and data-driven materials discovery.

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Expert-level mechanical vibrations covering free and forced vibration, damping, resonance, modal analysis, vibration isolation, and rotating machinery.

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。

Measure, root-cause, fix, verify and record performance contention on a developer machine: cloud-sync daemons (iCloud Drive, OneDrive, Dropbox, Google Drive), file indexers, AI-tool MCP server fleets, and automation browsers left behind by tests and browser skills. Trigger when the machine is slow, apps freeze, browser or MCP tool calls time out, load exceeds the core count, or before and after any UI/UX/performance test run. Includes the L0–L4 optimisation scale and the test-hygiene contract.

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Plan, scaffold, implement, and integrate a complete new emulated computer or console in the EMULATORS repository. Use when Codex creates a new machine, platform, hardware model, or emulator executable; decides which existing CPUs, chips, memory, I/O, video, sound, ROM, and framework classes to reuse or extend; or coordinates several emulator subsystems into a bootable tested system.

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Implement machine input/output, I/O port decoding, external buses, host input adapters, storage and media devices, and peripherals in EMULATORS. Use when adding keyboard matrices, joysticks, controllers, cassette, cartridge, disk, printer, serial or parallel interfaces, protocol state machines, PortManager behavior, or deterministic host-to-emulated-device integration.

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Implement or extend a reusable hardware chip and its machine-specific wiring in EMULATORS, including registers, pins or logical signals, clocks, state machines, memory-mapped behavior, side effects, interrupts, DMA or bus requests, serialization, introspection, and chip tests. Use when adding a controller, timer, PIA, PPI, UART, video or sound device, custom ASIC, register bank, or a variant of an existing chip.

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This skill covers deploying anomaly detection systems for industrial control environments using machine learning models trained on OT network baselines, physics-based process models, and behavioral analysis of industrial protocol communications. It addresses building normal behavior profiles for SCADA polling patterns, detecting deviations in Modbus/DNP3/OPC UA traffic, identifying rogue devices, and correlating network anomalies with physical process data from historians.

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。

Metaplex development on Solana — NFTs, tokens, compressed NFTs, candy machines, token launches. Use when working with Token Metadata, Core, Bubblegum, Candy Machine, Genesis, or the mplx CLI.

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Report this machine's file-synchronization state with the whole fleet: peers connected, per-share state and files still needed, folder errors, and how many sync-conflict files piled up, which is the silent sign two machines are fighting over a file. Read-only and zero tokens. Triggers: "/sync-check", "is sync alive", "sync status".

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Recover a fleet whose machines cannot see each other over the file-sync network: establish ground truth at the hub from the live sync API rather than memory or one peer's claim, publish the hub's verified device id over the messaging rail, and walk each disconnected peer through re-pairing. Triggers: "/raise-sync", "sync is down", "machines cannot see each other".

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Cut background heat and noise on this machine: audit scheduled tasks by runs per hour, autostart entries, GPU holders, power plan and top CPU, then propose disable or stretch candidates, decide line by line with the operator and apply only approved lines. Triggers: /deflate, fan is loud, laptop runs hot, what can I turn off, machine slow from background load.

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Run an autonomous consensus round between agent peers on several machines: they negotiate a decision over a shared channel and execute it without a human relaying messages. Use when multiple machines must agree on a change. Triggers: "/03", "work it out among yourselves", "find consensus". Wakes the human only for irreversible actions or a deadlock.

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 for multi-machine HTTP delta sync with peer management.

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Machine learning development with JAX, functional programming patterns, and high-performance computing.

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Use when adding, changing, deploying, onboarding, or auditing homedir-manager content repos — the marker-based symlink deployment system that manages dotfiles and config across your machines. Covers the update workflow, the manifest, OS-splitting, secrets, new-machine onboarding, and the audit process.

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作業パターンを skill 化すべきか判定する共通スキル。org-retro と org-curate から呼ばれ、 「skill 化推奨 / 候補止まり / curated ノートのまま」の 3 値と根拠を返す。 自動 skill 化はせず、推奨は machine-local な knowledge/skill-candidates.local.md に追記し、 窓口が候補キューが溜まった時点でバッチで人間に問い合わせる二段構え。

This skill covers deploying anomaly detection systems for industrial control environments using machine learning models trained on OT network baselines, physics-based process models, and behavioral analysis of industrial protocol communications. It addresses building normal behavior profiles for SCADA polling patterns, detecting deviations in Modbus/DNP3/OPC UA traffic, identifying rogue devices, and correlating network anomalies with physical process data from historians.

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。

Validates predictive models on omics and biomedical data with nested cross-validation, group/batch/temporal-aware splits, the full data-leakage taxonomy, probability calibration, decision-curve net benefit, optimism correction, sample-size planning, and TRIPOD+AI reporting. Use when estimating model performance honestly, choosing a CV scheme, detecting leakage, or judging whether reported discrimination means the model is actually useful. For feature selection itself see machine-learning/biomarker-discovery; for confirmatory-trial inference see clinical-biostatistics/trial-reporting.

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。

Explains ML predictions on omics data with SHAP, LIME, and permutation importance, handling the correlated-feature trap, the conditional-vs-interventional Shapley choice, and the attribution-is-not-causation boundary. Use when interpreting an omics classifier, debugging shortcut/batch learning, or deciding whether an attribution ranking can be trusted as biology. For validated feature selection see machine-learning/biomarker-discovery; explanations are not a selection method.

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Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.

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Decision-grade statistical analysis for metabolomics intensity tables. Covers transformation and scaling (Pareto vs unit-variance as a hidden hypothesis), unsupervised structure (PCA/HCA for QC), permutation-validated PLS-DA/OPLS-DA (R2 vs Q2, double CV, VIP as heuristic), univariate testing (Welch/Mann-Whitney/ANOVA/LMM with covariate adjustment), and dependence-aware multiple testing. Use when testing which metabolites differ, building or validating a discriminant model, choosing a scaling, or correcting many correlated tests. For sample-wise normalization/drift correction see metabolomics/normalization-qc; for ML classifiers and selection-inside-CV leakage see machine-learning/biomarker-discovery and machine-learning/model-validation; for pathway interpretation see metabolomics/pathway-mapping; for design/power/multiplicity regime see experimental-design/multiple-testing.

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。

Sync the current machine's configuration into the dotfiles repo and update the React showcase page. Use this skill whenever the user says "sync dotfiles", "update dotfiles", "sync config", "actualizar dotfiles", "sincronizar config", "update the repo with my current setup", or anything about pulling their current machine state into the dotfiles repo. Also trigger when the user mentions updating the toolkit page, adding new tools to the page, syncing Claude Code or Codex config (AGENTS.md, codex agents/skills/hooks), or keeping the dotfiles repo in sync with what's actually installed.

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。