「core」の検索結果

Score deal velocity based on email response times, meeting frequency, and stakeholder engagement. Predict which deals will close vs stall.

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。

Identifies upsell and cross-sell opportunities within existing customer accounts. Analyzes product usage, feature gaps, team growth, industry benchmarks, and competitive pressure to surface revenue expansion plays scored by potential, effort, and likelihood. Generates an expansion-playbook.md with account-by-account opportunities, recommended pitch, timing, and approach.

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。

Assesses how ready a business is for AI adoption across six dimensions. Evaluates data maturity, tech stack, team skills, process documentation, budget, and culture. Generates a comprehensive ai-readiness-report.md with scores, gap analysis, and recommended starting points. Aligned with OneWave AI's audit methodology.

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 to test whether an AI agent, prompt, or skill actually works before or after it ships — build a small eval set, score runs, and catch regressions when the prompt or model changes. Trigger when the user asks whether a prompt is good, wants to compare models or versions, sees inconsistent agent output, or is about to put an agent in front of customers.

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。

Generates a comprehensive client health overview across all accounts. Reads CRM data, support tickets, usage metrics, billing, and engagement logs. Calculates health scores, trend direction, and RAG status per client. Outputs a sorted risk report with recommended 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。

Scores agent actions by expected gain, cost, uncertainty, and redundancy. Use when deciding whether to dispatch an agent or invoke a tool.

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。

Scores feature worthiness and enforces branch-size limits against overengineering. Use when evaluating whether a feature belongs in the current scope or branch.

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。

Provides review-workflow scaffolding for context, evidence, and output. Use at the start of any detailed review to ensure consistent, comparable findings.

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。

Scores backlog items with RICE/WSJF/Kano and files GitHub issues for top candidates. Use when triaging a roadmap or prioritizing features for a sprint.

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。

Establishes CPU/GPU baselines before resource-intensive operations. Use before builds, training runs, or any task that pins cores or GPUs for over a minute.

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 Z.ai GLM-5.x via the stock claude binary and an endpoint swap. Use when delegation-core selects GLM or a 1M-token context is needed.

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。

Delegates execution to eight CLIs (Gemini, Qwen, MiniMax, GLM, Muse, Codex, OpenCode, Glimmer). Use for execution tasks. Do not use for secrets.

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。

Convenes a multi-LLM expert panel to pressure-test hard-to-reverse decisions. Use when reversibility score is low and adversarial review is warranted.

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。

Turn hunches into accepted results: worthiness score, evidence bar, research-to-rules. Use when vetting ideas. Not for QA; use night-market-validation-and-qa.

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 data needs statistical analysis. Runs reproducible Python/R code for Table 1, diagnostic accuracy, agreement, regression, survival, propensity score, survey-weighted and repeated-measures models, with publication tables. Sample size is /calc-sample-size; pooling studies is /meta-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。

Use DPM-Solver and DPM-Solver++ single-file samplers for diffusion ODE sampling, PyTorch/JAX integration, ScoreSDE/DDPM examples, and Stable Diffusion acceleration.

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 EasyR1 for multimodal LLM RL post-training, dataset/reward preparation, core API debugging, and checkpoint export workflows.

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。

Route ClearerVoice-Studio tasks for ClearVoice inference, SpeechScore metrics, and speech-model training or data-preparation workflows.

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 Astropy core for astronomy units, coordinates, times, tables, FITS/WCS files, modeling, statistics, visualization, cosmology, configuration, and public CLIs.

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。

Guides core Autodistill workflows for foundation-model auto-labeling, target-model distillation, CLI/plugin registry use, custom model interfaces, and utilities.

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 this skill for maintaining, migrating, debugging, and safely operating Microsoft AutoGen Python 0.7.x applications, including AgentChat, Core runtimes, extensions, Studio, Magentic-One, AG Bench, and pyautogen compatibility.

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。

Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.

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。

Same pixels, fewer tokens — for user-facing deliverables where visual polish is the spec. Use when building or editing a landing page, marketing site, hero, pricing/feature section, dashboard, or any HTML/CSS UI component. Keeps the full rendered design (layout depth, hierarchy, motion, responsive richness, a11y) and cuts tokens by expressing that design densely — CSS custom properties, shared classes, shorthand, fluid units — instead of by cutting the design. The honey core trims code and prose; this trims how the design is *written*, never how it looks. Reach for it whenever output is user-facing markup, even if the user never says "minimal".

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。