「learning」の検索結果

Analyze learning management and training pathway systems for prerequisite mapping, competency-based progression, adaptive sequencing, and credential stacking.

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Analyze defense program budgets and acquisition costs — earned value management (EVM/CPI/SPI), should-cost modeling, PPBE process alignment, cost estimation per GAO guidelines, Nunn-McCurdy breach risk, FYDP profiles, and learning curve analysis. Audit DoD 5000 acquisition software, program cost databases, and budget justification systems.

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Audit curriculum management software for learning outcome alignment, pacing guide optimization, standards coverage mapping (Common Core, NGSS, state standards), differentiation support, assessment design quality, and content gap detection across grade levels and subjects..

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。

Fetch the latest AI/ML news, trending open-source projects, social media discussions, and KOL (Key Opinion Leader) perspectives from 370+ curated sources. Use when the user asks about recent AI news, AI trends, what's happening in machine learning, open-source AI projects, AI Twitter/Reddit discussions, or wants a daily AI briefing.

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Plan a monthly content calendar across channels using the Content Marketing Matrix (Dave Chaffey, Smart Insights) - Entertain/Inspire/Educate/Convince. Every post gets a quadrant label. The monthly calendar must hit 40% Educate, 40% Inspire+Convince, 20% Entertain. Produces a week-by-week posting schedule with topics, formats, channels, and asset links. Use when the user says "content calendar", "social calendar", "plan next month's content", "what should we post", "content plan", "editorial calendar", "schedule posts for the month", or wants a structured posting plan for LinkedIn, Twitter, email, or blog. Reads brand voice, ICP, and past learnings from knowledge/. For writing the individual posts, see linkedin-post, newsletter-writer or content-writer. For the campaign this sits inside, see campaign-brief. For the end-of-month review, see retro.

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Write a blameless postmortem after an incident. Use when the user says "postmortem", "post-incident review", "PIR", "what happened in that incident", "incident review", "blameless postmortem", "5 whys", "how do we prevent this again", or needs to document learnings from a production incident - even if they don't explicitly say "postmortem".

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Review marketing KPIs and produce an executive summary with insights, anomalies, and recommended actions using the Storytelling with Data framework (Cole Nussbaumer Knaflic). Every review starts with a Big Idea - one sentence capturing the insight, its implication, and why it matters. Use when the user asks for a KPI review, monthly metrics review, marketing dashboard review, "review last month's numbers", quarterly review, performance review, or wants to analyze marketing data. Reads kpis.md and any uploaded data files. For planning a campaign against these numbers, see campaign-brief. For the post-campaign learning loop, see retro. For setting the baseline itself, see brand-context.

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Create and maintain the shared knowledge/ context files that every other marketing skill reads - brand voice, ICP personas, positioning, KPIs, and the learnings log. Run this once per company or client before using any other marketing skill. Use when the user says "set up brand context", "onboard my brand", "set up the marketing knowledge base", "the skills don't know my brand", "update our positioning", "our voice has changed", "add a new persona", or when any marketing skill reports that a knowledge/ file is missing. Also use when output sounds generic or off-brand. For writing a positioning statement as a standalone document, see positioning-doc. For deep persona research from raw sources, see customer-research.

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Document a machine learning model in a structured, transparent format for stakeholders, reviewers, and future maintainers. Use when the user says "document this model", "write a model card", "model documentation", "how should I document my ML model", "bias and fairness report", "model transparency", "what does this model do", "model handoff", "production model documentation", or needs to communicate what a model does, how it was built, where it works, and where it fails.

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Design and implement features from raw data for machine learning models. Use when the user says "engineer features", "feature engineering for X", "create features from this dataset", "transform these variables", "encode categoricals", "handle skew", "create interaction features", "lag features", "extract features from text or dates", "improve model performance with better features", or wants to move from raw columns to model-ready inputs.

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Short alias for external-site-profile-learning. Use this when investigating, adding, validating, or debugging external website profiles for the 99idea Playwright browser demo.

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**WORKFLOW SKILL** — Register and generalize agent errors into reusable lessons. USE FOR: when the user corrects the agent, capturing what went wrong, analyzing root cause, generalizing the error into a lesson that prevents recurrence. Use to register errors, analyze mistakes, create lessons, generalize corrections. DO NOT USE FOR: general debugging, writing tests, code review.

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Use this skill when investigating, adding, validating, or debugging external website profiles for the 99idea Playwright browser demo. It teaches how to probe selectors, classify failure modes, add config-driven profiles, and validate both heuristic and Gemini flows.

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Shortest English alias for site-profile / external-site-profile-learning. Use this when turning an already-proven site flow into a stable reusable external profile.

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Analyzes your recent Claude Code chat history to identify coding patterns, development gaps, and areas for improvement, curates relevant learning resources from HackerNews, and automatically sends a personalized growth report to your Slack DMs.

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Silently restructures the user's natural-language prompt into the format the model CURRENTLY running this skill handles best, then answers. On activation it identifies which model family is executing it (Claude, GPT, Gemini, Llama, DeepSeek, Mistral, Qwen, Grok, Perplexity, Kimi, GLM, Command, Nova, or Phi) and loads that one model's official strategy — so the optimization always matches the model that actually runs it. Activate with /prompt-refine. Use when users want better answers without learning prompt engineering.

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Leverages BigQuery's built-in machine learning and GenAI capabilities for advanced data analytics. Use when you need to write SQL queries that perform time-series forecasting, predict values, detect outliers or anomalies, find key drivers, perform semantic search or vector search, classify text, calculate similarity, summarize content, translate language, evaluate models, filter by semantic conditions, measure the causal effect of an intervention, compute correlations between columns, detect change points or structural breaks, extract trend or seasonality components, or leverage generative AI capabilities in BigQuery. Do not use for general BigQuery dataset, table, or job management requests.

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Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.

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Use when learning, designing, or explaining Salesforce Reports and Dashboards from first principles — report types, custom report types, groupings, bucket fields, summary formulas, charts, dashboard components, dynamic dashboards, report subscriptions, and folder permissions. Triggers: 'how do I build a report', 'what report type should I use', 'set up a dashboard', 'joined report limits', 'bucket field vs formula', 'dynamic dashboard running user'. NOT for CRM Analytics (use crm-analytics-* skills). NOT for Einstein Discovery predictive analytics. NOT for troubleshooting missing report data caused by sharing model issues (use admin/reports-and-dashboards for that).

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Deep learning framework development with tinygrad - a minimal tensor library with autograd, JIT compilation, and multi-device support. Use when writing neural networks, training models, implementing tensor operations, working with UOps/PatternMatcher for graph transformations, or contributing to tinygrad internals. Triggers on tinygrad imports, Tensor operations, nn modules, optimizer usage, schedule/codegen work, or device backends.

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。

Expert in proof techniques, mathematical reasoning, and rigorous argumentation for students learning to write proofs

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Expert in vector spaces, matrices, linear transformations, eigenvalues, and applications to data science and machine learning

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。

Comprehensive Spanish language expert covering grammar, conversation, regional dialects, and language learning strategies

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。

Kanji specialist covering character origins, radical systems, mnemonic techniques, and historical development

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。