データ分析、機械学習、LLMアプリ、プロンプト設計（9,105 件）
Use dspy.RLM (Recursive Language Model) for reasoning over contexts too large to fit in an LLM's working window — entire codebases, long logs, massive documents, or multi-step data exploration that needs a sandboxed Python REPL. Use when the input is >100k tokens, needs recursive chunking, or benefits from the LLM writing and running code to probe data.
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Write idiomatic DSPy 3.2.x programs — typed Signatures, dspy.Module subclasses, Predict/ChainOfThought/ReAct/ProgramOfThought, and save/load. Use this when starting any new DSPy project or when fixing non-idiomatic DSPy code (hard-coded prompts, ad-hoc string templates, untyped outputs, non-serializable classes).
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。
The only memory skill that watches on its own. No database. No vectors. No manual saves. Just an LLM observer that compresses your conversations into prioritised notes, consolidates when they grow, and recovers anything missed. Five layers of redundancy, zero maintenance. ~$0.00/month (using free-tier models). While other memory skills ask you to remember to remember, this one just pays attention.
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AI creative director with recursive self-assessment. Generates concepts using world-class methodologies (SIT, TRIZ, Lateral Thinking, bisociation), scores against 6 weighted criteria with Cannes/D&AD/HumanKind calibration, and recursively refines until the 9+ threshold is reached. Accepts briefs in any format — text, voice transcript, PDF, or raw notes. Use when the user asks to generate creative concepts, brainstorm campaign ideas, develop a Big Idea or campaign platform, evaluate or critique existing creative work, find consumer insights, or shares a brief for ideation — including activations, PR-stunts, brand utility, experiential, and non-advertising ideas. Calibrates against a library of 569 legendary campaigns (P01-P18 pattern map) to detect saturation and ensure originality. Do not use for media planning, production budgeting, brand identity/logo design, copywriting final drafts, or market research data collection.
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Analyzes CSV files, generates summary stats, and plots quick visualizations using Python and pandas.
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。
QA an analysis before sharing with stakeholders — methodology checks, accuracy verification, and bias detection. Use when reviewing an analysis for errors, checking for survivorship bias, validating aggregation logic, or preparing documentation for reproducibility.
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。
Build self-contained interactive HTML dashboards with Chart.js, dropdown filters, and professional styling. Use when creating dashboards, building interactive reports, or generating shareable HTML files with charts and filters that work without a server.
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Create effective data visualizations with Python (matplotlib, seaborn, plotly). Use when building charts, choosing the right chart type for a dataset, creating publication-quality figures, or applying design principles like accessibility and color theory.
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Create Quarterly Business Reviews with account health scores, usage analytics, ROI analysis, expansion opportunities, and risk mitigation
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Apply statistical methods including descriptive stats, trend analysis, outlier detection, and hypothesis testing. Use when analyzing distributions, testing for significance, detecting anomalies, computing correlations, or interpreting statistical results.
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Write correct, performant SQL across all major data warehouse dialects (Snowflake, BigQuery, Databricks, PostgreSQL, etc.). Use when writing queries, optimizing slow SQL, translating between dialects, or building complex analytical queries with CTEs, window functions, or aggregations.
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Execute read-only SQL queries against multiple PostgreSQL databases. Use when: (1) querying PostgreSQL databases, (2) exploring database schemas/tables, (3) running SELECT queries for data analysis, (4) checking database contents. Supports multiple database connections with descriptions for intelligent auto-selection. Blocks all write operations (INSERT, UPDATE, DELETE, DROP, etc.) for safety.
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Generate or improve a company-specific data analysis skill by extracting tribal knowledge from analysts. BOOTSTRAP MODE - Triggers: "Create a data context skill", "Set up data analysis for our warehouse", "Help me create a skill for our database", "Generate a data skill for [company]" → Discovers schemas, asks key questions, generates initial skill with reference files ITERATION MODE - Triggers: "Add context about [domain]", "The skill needs more info about [topic]", "Update the data skill with [metrics/tables/terminology]", "Improve the [domain] reference" → Loads existing skill, asks targeted questions, appends/updates reference files Use when data analysts want Claude to understand their company's specific data warehouse, terminology, metrics definitions, and common query patterns.
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Write detailed feature specifications with functional requirements, edge cases, data models, API contracts, and UX flows. Create comprehensive technical specifications that enable clear implementation.
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Profile and explore datasets to understand their shape, quality, and patterns before analysis. Use when encountering a new dataset, assessing data quality, discovering column distributions, identifying nulls and outliers, or deciding which dimensions to analyze.
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Generate project status reports with RAG status, milestones, risks, blockers, resource utilization, and executive summaries
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TRIGGER THIS when researching market salaries, building compensation strategies, creating salary bands, analyzing pay equity, conducting salary reviews, preparing compensation proposals, or ensuring competitive compensation. Analyzes market data, builds equity-focused salary structures, identifies pay gaps, and creates data-driven compensation recommendations.
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。
Azuqua integration. Manage data, records, and automate workflows. Use when the user wants to interact with Azuqua data.
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。
Basecone integration. Manage data, records, and automate workflows. Use when the user wants to interact with Basecone data.
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。
BigID integration. Manage data, records, and automate workflows. Use when the user wants to interact with BigID data.
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。
BigBox integration. Manage data, records, and automate workflows. Use when the user wants to interact with BigBox data.
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。
Beeple integration. Manage data, records, and automate workflows. Use when the user wants to interact with Beeple data.
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。
Backlog integration. Manage Projects. Use when the user wants to interact with Backlog data.
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。
Baremetrics integration. Manage data, records, and automate workflows. Use when the user wants to interact with Baremetrics data.
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。