「weight」の検索結果

Use this skill when the user asks about trucking terminology — bobtail vs deadhead vs loaded vs empty trailer — and the insurance, weight, and fuel-tax implications of each. Covers bobtail insurance (also called non-trucking liability), how to track bobtail miles for state taxes, and when "deadhead" affects driver pay.

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Turn a large dataset (an existing oversized Coval test set, an export of past conversations, or a CSV/JSON of cases) into a small, high-signal Coval test set by removing duplicates, identifying unique scenarios, and selecting a representative, failure-weighted subset — then bulk-loading it with no row cap. Use when the user says "I have thousands of cases", "dedupe my test set", "my test set is too big", "turn this dataset into a test set", "pick representative scenarios", or "my CSV import only kept 10 / uploaded everything".

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。

Ahrefs API analyst (extension). Reads referring domains, backlinks, organic keywords, and content explorer data via the tested @ahrefs/mcp@0.0.11 server. Pairs with seo-backlinks for multi-source confidence weighting.

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。

Improves typography by fixing font choices, hierarchy, sizing, weight, and readability so text feels intentional. Use when the user mentions fonts, type, readability, text hierarchy, sizing looks off, or wants more polished, intentional typography.

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Complete workflow for topology optimization using Tosca. Use to minimize weight while maintaining stiffness. Requires full Abaqus license (not Learning Edition).

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。

Core UI/UX engineering skill for building highly interactive, spatial, weightless, and glassmorphism-based web interfaces using GSAP and 3D CSS.

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Запускает lightweight baseline verification для docs, OpenSpec, skills и live context этого репозитория.

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。

Remove refusal behaviors from open-weight LLMs using OBLITERATUS — mechanistic interpretability techniques (diff-in-means, SVD, whitened SVD, LEACE, SAE decomposition, etc.) to excise guardrails while preserving reasoning. 9 CLI methods, 28 analysis modules, 116 model presets across 5 compute tiers, tournament evaluation, and telemetry-driven recommendations. Use when a user wants to uncensor, abliterate, or remove refusal from an LLM.

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Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collaborative MLOps platform

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。

Architecture Decision Records (ADR) — a lightweight method for capturing important architectural decisions with context, trade-offs, and rationale. Based on Michael Nygard's ADR pattern and Google/Meta/ByteDance's design documentation culture. USE WHEN: deciding between technology choices, designing new architecture, changing existing architecture, documenting why a decision was made, onboarding new team members, or preserving design context for future maintainers. Triggered by terms like "tech choice", "architecture decision", "why did we choose", "design rationale", "ADR", or "architectural decision record".

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。

Improve UI typography by fixing font choices, type scale, hierarchy, weight, and readability so text feels intentional. Use when the user mentions fonts, typography, type scale, readability, text hierarchy, or sizing that feels off.

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。

Clarify priority and emphasis by fixing unclear primary actions, competing visual weight, loud section titles, and weak label/value treatment. Use when the user mentions hierarchy, prioritization, too much competition, grayscale confusion, loud headings, or unclear primary actions.

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。

Rapid codebase assessment — lightweight alternative to $gsd-map-codebase

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。

Campaign attribution analysis involves systematically evaluating evidence to determine which threat actor or group is responsible for a cyber operation. This skill covers collecting and weighting attr

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。

AEM Cloud Service expert skill for Sling ResourceChangeListener. Covers the lightweight listener + JobConsumer pattern, migration from javax.jcr.observation.EventListener and resource-topic OSGi EventHandler, ResourceChangeListener vs ExternalResourceChangeListener decision, OSGi filter configuration, review checklist, troubleshooting (silent failures, hot-path blocking, missing service-user mapping), and common pitfalls.

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AEM Cloud Service expert skill for OSGi Event Admin handlers (non-resource events). Covers migration of javax.jcr.observation.EventListener (residual non-resource cases) and OSGi EventHandler with inline business logic to the lightweight EventHandler + JobConsumer split. Includes routing rules (resource events go to resource-change-listener, external notification goes to AEM Eventing), TopologyEventListener for leader-only execution, replication and workflow event patterns, service-user setup, review checklist, troubleshooting, and common pitfalls.

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。

Implements lightweight state management using Zustand with TypeScript, persistence, devtools, and modular store patterns. Use when users request "zustand store", "state management", "global state", "zustand setup", or "jotai alternative".

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 search and content extraction via Brave Search API. Use for searching documentation, facts, or any web content. Lightweight, no browser required.

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。

Score raw leads as HOT/WARM/COOL based on config-driven weights from agency.config.json

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。

Revenue forecast from pipeline data with stage-weighted probability and sensitivity analysis

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。

Principled action selection for autonomous agents using Expected Free Energy (EFE) under the Active Inference framework. Each candidate action is scored as G(n) = w_prag * (-E[p_n] * damping) + w_epist * (-Var[p_n] * novelty(n)), where agent beliefs are maintained as per-node Beta distributions and updated via Bayesian observation. Selection is a softmax over -gamma * G, making exploration an automatic consequence of uncertainty rather than a hardcoded epsilon. Precision weights w_prag and w_epist self-adapt via surprise history, shifting the agent between exploitation and exploration regimes without any manually tuned schedule.

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An innate exploratory drive that guarantees no graph node remains permanently invisible to an agent population. After a warm-up period (step > 3), each agent fires a coverage scan with probability |unseen_nodes| / |all_nodes|. When triggered, the agent teleports directly to the globally unseen node with the highest belief uncertainty, works it, and returns control to the normal action-selection loop. Probability is self-annealing: it starts near 1.0 when almost everything is unseen and decays toward 0.0 as coverage saturates, requiring no manual schedule. The mechanism is "innate" (hard-coded into the agent step loop) as distinct from the "adaptive" EFE path, which requires local neighbors and learned precision weights. Together they form a two-layer exploration architecture: adaptive for locally-guided foraging, innate for topological completeness.

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Audit or design server-side indexes, directories, reputations, or aggregates that are DERIVED from traffic a platform witnesses (chain heads, run verdicts, heartbeats, publish activity) so that consent gates the derivation, not merely the read: no shadow index over non-consenting principals, post-consent-only evidence windows, retention bounds and delete-propagation into derived rows, activity-oracle threat modeling, and audit-logged ranking discretion. Use when building a capability directory / whois from witnessed activity, a reputation ledger from settled outcomes, vitals aggregation across tenants, or any "the yellow pages can't lie because they index signatures" feature. Keywords: shadow index, derived index, consent gates derivation, activity oracle, phone book not a log, delist propagation, ranking weight audit, k-anonymous aggregate, subpoena surface. NOT for the consent-screen UX and scope-ladder mechanics themselves (use local-first-tenancy-boundary), reputation scoring math and Sybil economics (use agent-identity-continuity-reputation), or discovery-layer architecture L1/L2/L3 (use agent-discovery-directories-guilds).

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。

Practical incident-response runbook for a suspected or confirmed ComfyUI / GPU-daemon compromise. Triage, scope, contain, forensic collection, weight-integrity verification, rebuild from clean snapshot, postmortem. NOT for hygiene scans on a healthy box (use supply-chain-defense-for-ml-extensions), and NOT for runtime hardening of a fresh deployment (use gpu-hosted-asset-hardening).

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。