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Skill: data-cog Owner: nitishgargiitd Summary: Your data has answers. CellCog asks the right questions. #1 on DeepResearch Bench (Feb 2026) + frontier coding agent — upload messy CSVs with minimal prompting and get structured insights back: charts, dashboards, statistical reports, and clean data. Full Python access for data cleaning, exploratory analysis, visualization, hypothesis testing, ML model evaluation, and da","descriptionLabel":"Technical summary","evidenceSummary":"Capability contract not published. No trust telemetry is available yet. 1.1K downloads reported by the source. 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CellCog asks the right questions. #1 on DeepResearch Bench (Feb 2026) + frontier coding agent — upload messy CSVs with minimal prompting and get structured insights back: charts, dashboards, statistical reports, and clean data. Full Python access for data cleaning, exploratory analysis, visualization, hypothesis testing, ML model evaluation, and dataset profiling. Analyzes everything, presents it beautifully.\n\nTags: latest:1.0.1\n\nVersion history:\n\nv1.0.1 | 2026-02-11T01:41:01.965Z | user\n\n- Added clear author and dependency metadata to SKILL.md.\n- Changed prerequisite wording to reference the `cellcog` skill directly.\n- No functional changes; documentation improvements only.\n\nv1.0.0 | 2026-02-08T00:43:08.712Z | user\n\n- Initial release of Data-Cog skill.\n- Enables analysis of messy CSVs and other data files with minimal prompts, returning structured insights (charts, dashboards, reports, and clean data).\n- Provides full Python access for tasks such as data cleaning, exploratory analysis, visualization, hypothesis testing, ML model evaluation, and dataset profiling.\n- Focuses on delivering actual answers and visual summaries instead of just sharing code.\n- Supports multiple data and output formats, including CSV, Excel, JSON, Parquet, and SQL exports.\n- Requires the CellCog mothership skill for SDK and API usage.\n\nArchive index:\n\nArchive v1.0.1: 2 files, 4584 bytes\n\nFiles: SKILL.md (9972b), _meta.json (127b)\n\nFile v1.0.1:SKILL.md\n\n---\nname: data-cog\ndescription: \"Your data has answers. CellCog asks the right questions. #1 on DeepResearch Bench (Feb 2026) + frontier coding agent — upload messy CSVs with minimal prompting and get structured insights back: charts, dashboards, statistical reports, and clean data. Full Python access for data cleaning, exploratory analysis, visualization, hypothesis testing, ML model evaluation, and dataset profiling. Analyzes everything, presents it beautifully.\"\nmetadata:\n  openclaw:\n    emoji: \"🔢\"\nauthor: CellCog\ndependencies: [cellcog]\n---\n\n# Data Cog - Your Data Has Answers, CellCog Finds Them\n\n**Your data has answers. CellCog asks the right questions.** #1 on DeepResearch Bench (Feb 2026) + frontier coding agent.\n\nMost AI tools return code when you ask about data. CellCog returns answers — actual charts, clean datasets, statistical reports, and visual dashboards. Upload messy CSVs with a minimal prompt, and CellCog's coding agent explores your data, finds the patterns, and presents them beautifully. Full Python access for everything from data cleaning to ML model evaluation.\n\n---\n\n## Prerequisites\n\nThis skill requires the `cellcog` skill for SDK setup and API calls.\n\n```bash\nclawhub install cellcog\n```\n\n**Read the cellcog skill first** for SDK setup. This skill shows you what's possible.\n\n**Quick pattern (v1.0+):**\n```python\n# Fire-and-forget - returns immediately\nresult = client.create_chat(\n    prompt=\"Analyze this dataset: <SHOW_FILE>/path/to/data.csv</SHOW_FILE>\",\n    notify_session_key=\"agent:main:main\",\n    task_label=\"data-analysis\",\n    chat_mode=\"agent\"  # Agent mode for most data work\n)\n# Daemon notifies you when complete - do NOT poll\n```\n\n---\n\n## What Makes Data-Cog Different\n\n### Code as Tool, Not as Output\n\nOther AI tools give you Python code and say \"run this.\" CellCog **runs the code for you** and delivers the results:\n\n| Other AI Tools | Data-Cog |\n|---------------|----------|\n| \"Here's a pandas script to analyze your data\" | Here are your actual insights with charts |\n| \"Run this matplotlib code to see the chart\" | Here's the chart, annotated with findings |\n| \"This SQL query will find outliers\" | Found 23 outliers, here's what they mean |\n| \"You'll need scikit-learn for this\" | Model trained, here's accuracy and feature importance |\n\nYou upload data. You get answers. The code runs behind the scenes.\n\n---\n\n## What Data Work You Can Do\n\n### Exploratory Data Analysis\n\nUnderstand your data fast:\n\n- **Dataset Profiling**: \"Analyze this CSV — distributions, missing values, outliers, correlations, and data quality summary\"\n- **Pattern Discovery**: \"What patterns and trends exist in this sales data? Surprise me.\"\n- **Anomaly Detection**: \"Find unusual patterns in this server log data — what looks abnormal?\"\n- **Relationship Analysis**: \"What factors most strongly correlate with customer churn in this dataset?\"\n\n**Example prompt:**\n> \"Analyze this dataset:\n> <SHOW_FILE>/path/to/customer_data.csv</SHOW_FILE>\n> \n> I don't know much about this data yet. Give me:\n> - Overview: rows, columns, data types, missing values\n> - Key distributions and summary statistics\n> - Most interesting correlations\n> - Any outliers or data quality issues\n> - 3-5 insights that jump out\n> \n> Present findings as an interactive HTML report with charts.\"\n\n### Data Cleaning & Transformation\n\nWrangle messy data into shape:\n\n- **Clean Messy Data**: \"Clean this CSV — fix inconsistent date formats, handle missing values, remove duplicates, standardize column names\"\n- **Data Transformation**: \"Pivot this transaction data into a monthly summary by product category\"\n- **Data Merging**: \"Join these three CSV files on customer_id and create a unified dataset\"\n- **Feature Engineering**: \"Create useful features from this raw data for predicting house prices\"\n\n**Example prompt:**\n> \"Clean and transform this dataset:\n> <SHOW_FILE>/path/to/messy_data.csv</SHOW_FILE>\n> \n> Issues I know about:\n> - Dates are in mixed formats (MM/DD/YYYY and YYYY-MM-DD)\n> - 'Revenue' column has some values with $ signs and commas\n> - Duplicate rows exist\n> - Missing values in 'Region' column\n> \n> Clean it up and give me back a clean CSV plus a summary of what you changed.\"\n\n### Statistical Analysis\n\nRigorous analysis with real numbers:\n\n- **Hypothesis Testing**: \"Is there a statistically significant difference in conversion rates between our A and B variants?\"\n- **Regression Analysis**: \"What factors predict employee salary in this HR dataset? Build a regression model.\"\n- **Time Series Analysis**: \"Analyze this monthly revenue data — trend, seasonality, and forecast next 6 months\"\n- **Cohort Analysis**: \"Create a cohort analysis showing user retention by signup month\"\n\n**Example prompt:**\n> \"I ran an A/B test on our checkout page:\n> <SHOW_FILE>/path/to/ab_test_results.csv</SHOW_FILE>\n> \n> Columns: user_id, variant (A or B), converted (0/1), revenue, timestamp\n> \n> Tell me:\n> - Is variant B statistically better? (p-value, confidence interval)\n> - Conversion rate difference\n> - Revenue per user difference\n> - Sample size adequacy check\n> - My recommendation: ship B or keep testing?\n> \n> Present with clear charts and a plain-English conclusion.\"\n\n### Visualization & Reporting\n\nTurn data into visual stories:\n\n- **Chart Generation**: \"Create a set of charts showing our quarterly performance from this data\"\n- **Dashboard Reports**: \"Build an interactive dashboard from this sales dataset with filters by region and product\"\n- **Presentation-Ready Visuals**: \"Create publication-quality charts from this research data\"\n- **Comparison Visuals**: \"Visualize how our metrics compare to industry benchmarks\"\n\n### Machine Learning\n\nApplied ML without the setup:\n\n- **Classification**: \"Predict which customers will churn based on this dataset — train a model, show feature importance\"\n- **Clustering**: \"Segment these customers into groups based on behavior — how many natural clusters exist?\"\n- **Forecasting**: \"Forecast next quarter's sales using this historical data\"\n- **Model Evaluation**: \"I trained a model — here are the predictions. Evaluate: accuracy, precision, recall, confusion matrix, ROC curve\"\n\n**Example prompt:**\n> \"Predict customer churn from this dataset:\n> <SHOW_FILE>/path/to/customer_features.csv</SHOW_FILE>\n> \n> Target column: 'churned'\n> \n> - Train a model, try at least 2 algorithms\n> - Show feature importance — what drives churn?\n> - Confusion matrix and ROC curve\n> - Plain-English summary: 'The top 3 reasons customers churn are...'\n> - Actionable recommendations based on findings\n> \n> I want insights, not just metrics.\"\n\n---\n\n## Supported Data Formats\n\n| Format | How to Send |\n|--------|-------------|\n| **CSV** | Upload via SHOW_FILE |\n| **Excel (XLSX)** | Upload via SHOW_FILE |\n| **JSON** | Upload via SHOW_FILE |\n| **Parquet** | Upload via SHOW_FILE |\n| **SQL exports** | Upload the dump via SHOW_FILE |\n| **Inline data** | Describe small datasets directly in prompt |\n\n---\n\n## Output Formats\n\n| Format | Best For |\n|--------|----------|\n| **Interactive HTML Dashboard** | Explorable charts, filters, drill-downs |\n| **PDF Report** | Shareable analysis reports with charts and findings |\n| **Clean CSV/XLSX** | Cleaned or transformed data files for downstream use |\n| **Markdown** | Quick insights for integration into docs |\n\n---\n\n## Chat Mode for Data\n\n| Scenario | Recommended Mode |\n|----------|------------------|\n| Quick data cleaning, simple charts, basic statistics | `\"agent\"` |\n| Deep analysis with multiple techniques, ML modeling, comprehensive reports | `\"agent team\"` |\n\n**Use `\"agent\"` for most data work.** Data cleaning, EDA, chart generation, and standard statistical analysis execute well in agent mode.\n\n**Use `\"agent team\"` for complex analytical projects** — multi-technique analysis, ML model comparisons, or when you need deep domain reasoning about what the data means.\n\n---\n\n## Example Prompts\n\n**Minimal prompt, maximum insight:**\n> \"Analyze this:\n> <SHOW_FILE>/path/to/data.csv</SHOW_FILE>\n> \n> Tell me everything interesting.\"\n\nThat's it. CellCog's coding agent will profile the data, run exploratory analysis, find patterns, and present findings with charts. You don't need to know what to ask — the agent figures it out.\n\n**Business analysis:**\n> \"Analyze our e-commerce data:\n> <SHOW_FILE>/path/to/orders.csv</SHOW_FILE>\n> \n> I need:\n> - Revenue trends (daily, weekly, monthly)\n> - Best and worst performing products\n> - Customer purchase frequency distribution\n> - Average order value trends\n> - Seasonal patterns\n> - Top 5 actionable insights for growing revenue\n> \n> Interactive HTML dashboard with all charts.\"\n\n**Research data analysis:**\n> \"Analyze this survey data from 500 respondents:\n> <SHOW_FILE>/path/to/survey.csv</SHOW_FILE>\n> \n> Research questions:\n> 1. Is there a significant relationship between age group and product preference?\n> 2. Do satisfaction scores differ by region? (ANOVA)\n> 3. What factors best predict likelihood to recommend? (regression)\n> \n> Include: statistical tests, p-values, effect sizes, and publication-ready charts.\n> PDF report format.\"\n\n---\n\n## Tips for Better Data Analysis\n\n1. **Just upload and ask**: You don't need to describe every column. CellCog reads the data and figures out what's there.\n\n2. **State your question**: \"What drives churn?\" is more focused than \"Analyze this data.\" Both work, but the first gets faster results.\n\n3. **Mention the audience**: \"For my CEO\" means executive summary. \"For the data team\" means show the methodology.\n\n4. **Specify what you'll do with it**: \"I need to present this to the board\" vs \"I need clean data for my ML pipeline\" — context shapes the output.\n\n5. **Don't over-specify methods**: Let CellCog choose the right statistical approach. Say what you want to *learn*, not which algorithm to use.\n\n6. **Iterate**: Upload data → get initial analysis → ask follow-up questions → go deeper. CellCog maintains context across messages.\n\nFile v1.0.1:_meta.json\n\n{\n  \"ownerId\": \"kn7a96cj9q65e0bhmzahv790en80ffqm\",\n  \"slug\": \"data-cog\",\n  \"version\": \"1.0.1\",\n  \"publishedAt\": 1770774061965\n}\n\nArchive v1.0.0: 2 files, 4560 bytes\n\nFiles: SKILL.md (9941b), _meta.json (127b)\n\nFile v1.0.0:SKILL.md\n\n---\nname: data-cog\ndescription: \"Your data has answers. CellCog asks the right questions. #1 on DeepResearch Bench (Feb 2026) + frontier coding agent — upload messy CSVs with minimal prompting and get structured insights back: charts, dashboards, statistical reports, and clean data. Full Python access for data cleaning, exploratory analysis, visualization, hypothesis testing, ML model evaluation, and dataset profiling. Analyzes everything, presents it beautifully.\"\nmetadata:\n  openclaw:\n    emoji: \"🔢\"\n---\n\n# Data Cog - Your Data Has Answers, CellCog Finds Them\n\n**Your data has answers. CellCog asks the right questions.** #1 on DeepResearch Bench (Feb 2026) + frontier coding agent.\n\nMost AI tools return code when you ask about data. CellCog returns answers — actual charts, clean datasets, statistical reports, and visual dashboards. Upload messy CSVs with a minimal prompt, and CellCog's coding agent explores your data, finds the patterns, and presents them beautifully. Full Python access for everything from data cleaning to ML model evaluation.\n\n---\n\n## Prerequisites\n\nThis skill requires the CellCog mothership skill for SDK setup and API calls.\n\n```bash\nclawhub install cellcog\n```\n\n**Read the cellcog skill first** for SDK setup. This skill shows you what's possible.\n\n**Quick pattern (v1.0+):**\n```python\n# Fire-and-forget - returns immediately\nresult = client.create_chat(\n    prompt=\"Analyze this dataset: <SHOW_FILE>/path/to/data.csv</SHOW_FILE>\",\n    notify_session_key=\"agent:main:main\",\n    task_label=\"data-analysis\",\n    chat_mode=\"agent\"  # Agent mode for most data work\n)\n# Daemon notifies you when complete - do NOT poll\n```\n\n---\n\n## What Makes Data-Cog Different\n\n### Code as Tool, Not as Output\n\nOther AI tools give you Python code and say \"run this.\" CellCog **runs the code for you** and delivers the results:\n\n| Other AI Tools | Data-Cog |\n|---------------|----------|\n| \"Here's a pandas script to analyze your data\" | Here are your actual insights with charts |\n| \"Run this matplotlib code to see the chart\" | Here's the chart, annotated with findings |\n| \"This SQL query will find outliers\" | Found 23 outliers, here's what they mean |\n| \"You'll need scikit-learn for this\" | Model trained, here's accuracy and feature importance |\n\nYou upload data. You get answers. The code runs behind the scenes.\n\n---\n\n## What Data Work You Can Do\n\n### Exploratory Data Analysis\n\nUnderstand your data fast:\n\n- **Dataset Profiling**: \"Analyze this CSV — distributions, missing values, outliers, correlations, and data quality summary\"\n- **Pattern Discovery**: \"What patterns and trends exist in this sales data? Surprise me.\"\n- **Anomaly Detection**: \"Find unusual patterns in this server log data — what looks abnormal?\"\n- **Relationship Analysis**: \"What factors most strongly correlate with customer churn in this dataset?\"\n\n**Example prompt:**\n> \"Analyze this dataset:\n> <SHOW_FILE>/path/to/customer_data.csv</SHOW_FILE>\n> \n> I don't know much about this data yet. Give me:\n> - Overview: rows, columns, data types, missing values\n> - Key distributions and summary statistics\n> - Most interesting correlations\n> - Any outliers or data quality issues\n> - 3-5 insights that jump out\n> \n> Present findings as an interactive HTML report with charts.\"\n\n### Data Cleaning & Transformation\n\nWrangle messy data into shape:\n\n- **Clean Messy Data**: \"Clean this CSV — fix inconsistent date formats, handle missing values, remove duplicates, standardize column names\"\n- **Data Transformation**: \"Pivot this transaction data into a monthly summary by product category\"\n- **Data Merging**: \"Join these three CSV files on customer_id and create a unified dataset\"\n- **Feature Engineering**: \"Create useful features from this raw data for predicting house prices\"\n\n**Example prompt:**\n> \"Clean and transform this dataset:\n> <SHOW_FILE>/path/to/messy_data.csv</SHOW_FILE>\n> \n> Issues I know about:\n> - Dates are in mixed formats (MM/DD/YYYY and YYYY-MM-DD)\n> - 'Revenue' column has some values with $ signs and commas\n> - Duplicate rows exist\n> - Missing values in 'Region' column\n> \n> Clean it up and give me back a clean CSV plus a summary of what you changed.\"\n\n### Statistical Analysis\n\nRigorous analysis with real numbers:\n\n- **Hypothesis Testing**: \"Is there a statistically significant difference in conversion rates between our A and B variants?\"\n- **Regression Analysis**: \"What factors predict employee salary in this HR dataset? Build a regression model.\"\n- **Time Series Analysis**: \"Analyze this monthly revenue data — trend, seasonality, and forecast next 6 months\"\n- **Cohort Analysis**: \"Create a cohort analysis showing user retention by signup month\"\n\n**Example prompt:**\n> \"I ran an A/B test on our checkout page:\n> <SHOW_FILE>/path/to/ab_test_results.csv</SHOW_FILE>\n> \n> Columns: user_id, variant (A or B), converted (0/1), revenue, timestamp\n> \n> Tell me:\n> - Is variant B statistically better? (p-value, confidence interval)\n> - Conversion rate difference\n> - Revenue per user difference\n> - Sample size adequacy check\n> - My recommendation: ship B or keep testing?\n> \n> Present with clear charts and a plain-English conclusion.\"\n\n### Visualization & Reporting\n\nTurn data into visual stories:\n\n- **Chart Generation**: \"Create a set of charts showing our quarterly performance from this data\"\n- **Dashboard Reports**: \"Build an interactive dashboard from this sales dataset with filters by region and product\"\n- **Presentation-Ready Visuals**: \"Create publication-quality charts from this research data\"\n- **Comparison Visuals**: \"Visualize how our metrics compare to industry benchmarks\"\n\n### Machine Learning\n\nApplied ML without the setup:\n\n- **Classification**: \"Predict which customers will churn based on this dataset — train a model, show feature importance\"\n- **Clustering**: \"Segment these customers into groups based on behavior — how many natural clusters exist?\"\n- **Forecasting**: \"Forecast next quarter's sales using this historical data\"\n- **Model Evaluation**: \"I trained a model — here are the predictions. Evaluate: accuracy, precision, recall, confusion matrix, ROC curve\"\n\n**Example prompt:**\n> \"Predict customer churn from this dataset:\n> <SHOW_FILE>/path/to/customer_features.csv</SHOW_FILE>\n> \n> Target column: 'churned'\n> \n> - Train a model, try at least 2 algorithms\n> - Show feature importance — what drives churn?\n> - Confusion matrix and ROC curve\n> - Plain-English summary: 'The top 3 reasons customers churn are...'\n> - Actionable recommendations based on findings\n> \n> I want insights, not just metrics.\"\n\n---\n\n## Supported Data Formats\n\n| Format | How to Send |\n|--------|-------------|\n| **CSV** | Upload via SHOW_FILE |\n| **Excel (XLSX)** | Upload via SHOW_FILE |\n| **JSON** | Upload via SHOW_FILE |\n| **Parquet** | Upload via SHOW_FILE |\n| **SQL exports** | Upload the dump via SHOW_FILE |\n| **Inline data** | Describe small datasets directly in prompt |\n\n---\n\n## Output Formats\n\n| Format | Best For |\n|--------|----------|\n| **Interactive HTML Dashboard** | Explorable charts, filters, drill-downs |\n| **PDF Report** | Shareable analysis reports with charts and findings |\n| **Clean CSV/XLSX** | Cleaned or transformed data files for downstream use |\n| **Markdown** | Quick insights for integration into docs |\n\n---\n\n## Chat Mode for Data\n\n| Scenario | Recommended Mode |\n|----------|------------------|\n| Quick data cleaning, simple charts, basic statistics | `\"agent\"` |\n| Deep analysis with multiple techniques, ML modeling, comprehensive reports | `\"agent team\"` |\n\n**Use `\"agent\"` for most data work.** Data cleaning, EDA, chart generation, and standard statistical analysis execute well in agent mode.\n\n**Use `\"agent team\"` for complex analytical projects** — multi-technique analysis, ML model comparisons, or when you need deep domain reasoning about what the data means.\n\n---\n\n## Example Prompts\n\n**Minimal prompt, maximum insight:**\n> \"Analyze this:\n> <SHOW_FILE>/path/to/data.csv</SHOW_FILE>\n> \n> Tell me everything interesting.\"\n\nThat's it. CellCog's coding agent will profile the data, run exploratory analysis, find patterns, and present findings with charts. You don't need to know what to ask — the agent figures it out.\n\n**Business analysis:**\n> \"Analyze our e-commerce data:\n> <SHOW_FILE>/path/to/orders.csv</SHOW_FILE>\n> \n> I need:\n> - Revenue trends (daily, weekly, monthly)\n> - Best and worst performing products\n> - Customer purchase frequency distribution\n> - Average order value trends\n> - Seasonal patterns\n> - Top 5 actionable insights for growing revenue\n> \n> Interactive HTML dashboard with all charts.\"\n\n**Research data analysis:**\n> \"Analyze this survey data from 500 respondents:\n> <SHOW_FILE>/path/to/survey.csv</SHOW_FILE>\n> \n> Research questions:\n> 1. Is there a significant relationship between age group and product preference?\n> 2. Do satisfaction scores differ by region? (ANOVA)\n> 3. What factors best predict likelihood to recommend? (regression)\n> \n> Include: statistical tests, p-values, effect sizes, and publication-ready charts.\n> PDF report format.\"\n\n---\n\n## Tips for Better Data Analysis\n\n1. **Just upload and ask**: You don't need to describe every column. CellCog reads the data and figures out what's there.\n\n2. **State your question**: \"What drives churn?\" is more focused than \"Analyze this data.\" Both work, but the first gets faster results.\n\n3. **Mention the audience**: \"For my CEO\" means executive summary. \"For the data team\" means show the methodology.\n\n4. **Specify what you'll do with it**: \"I need to present this to the board\" vs \"I need clean data for my ML pipeline\" — context shapes the output.\n\n5. **Don't over-specify methods**: Let CellCog choose the right statistical approach. Say what you want to *learn*, not which algorithm to use.\n\n6. **Iterate**: Upload data → get initial analysis → ask follow-up questions → go deeper. CellCog maintains context across messages.\n\nFile v1.0.0:_meta.json\n\n{\n  \"ownerId\": \"kn7a96cj9q65e0bhmzahv790en80ffqm\",\n  \"slug\": \"data-cog\",\n  \"version\": \"1.0.0\",\n  \"publishedAt\": 1770511388712\n}","readmeExcerpt":"Skill: data-cog Owner: nitishgargiitd Summary: Your data has answers. CellCog asks the right questions. #1 on DeepResearch Bench (Feb 2026) + frontier coding agent — upload messy CSVs with minimal prompting and get structured insights back: charts, dashboards, statistical reports, and clean data. Full Python access for data cleaning, exploratory analysis, visualization, hypothesis testing, ML model evaluation, and da","codeSnippets":[],"executableExamples":[{"language":"bash","snippet":"clawhub install cellcog"},{"language":"python","snippet":"# Fire-and-forget - returns immediately\nresult = client.create_chat(\n    prompt=\"Analyze this dataset: <SHOW_FILE>/path/to/data.csv</SHOW_FILE>\",\n    notify_session_key=\"agent:main:main\",\n    task_label=\"data-analysis\",\n    chat_mode=\"agent\"  # Agent mode for most data work\n)\n# Daemon notifies you when complete - do NOT poll"},{"language":"bash","snippet":"clawhub install cellcog"},{"language":"python","snippet":"# Fire-and-forget - returns immediately\nresult = client.create_chat(\n    prompt=\"Analyze this dataset: <SHOW_FILE>/path/to/data.csv</SHOW_FILE>\",\n    notify_session_key=\"agent:main:main\",\n    task_label=\"data-analysis\",\n    chat_mode=\"agent\"  # Agent mode for most data work\n)\n# Daemon notifies you when complete - do NOT poll"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: data-cog\ndescription: \"Your data has answers. CellCog asks the right questions. #1 on DeepResearch Bench (Feb 2026) + frontier coding agent — upload messy CSVs with minimal prompting and get structured insights back: charts, dashboards, statistical reports, and clean data. Full Python access for data cleaning, exploratory analysis, visualization, hypothesis testing, ML model evaluation, and dataset profiling. Analyzes everything, presents it beautifully.\"\nmetadata:\n  openclaw:\n    emoji: \"🔢\"\nauthor: CellCog\ndependencies: [cellcog]\n---\n\n# Data Cog - Your Data Has Answers, CellCog Finds Them\n\n**Your data has answers. CellCog asks the right questions.** #1 on DeepResearch Bench (Feb 2026) + frontier coding agent.\n\nMost AI tools return code when you ask about data. CellCog returns answers — actual charts, clean datasets, statistical reports, and visual dashboards. Upload messy CSVs with a minimal prompt, and CellCog's coding agent explores your data, finds the patterns, and presents them beautifully. Full Python access for everything from data cleaning to ML model evaluation.\n\n---\n\n## Prerequisites\n\nThis skill requires the `cellcog` skill for SDK setup and API calls.\n\n```bash\nclawhub install cellcog\n```\n\n**Read the cellcog skill first** for SDK setup. This skill shows you what's possible.\n\n**Quick pattern (v1.0+):**\n```python\n# Fire-and-forget - returns immediately\nresult = client.create_chat(\n    prompt=\"Analyze this dataset: <SHOW_FILE>/path/to/data.csv</SHOW_FILE>\",\n    notify_session_key=\"agent:main:main\",\n    task_label=\"data-analysis\",\n    chat_mode=\"agent\"  # Agent mode for most data work\n)\n# Daemon notifies you when complete - do NOT poll\n```\n\n---\n\n## What Makes Data-Cog Different\n\n### Code as Tool, Not as Output\n\nOther AI tools give you Python code and say \"run this.\" CellCog **runs the code for you** and delivers the results:\n\n| Other AI Tools | Data-Cog |\n|---------------|----------|\n| \"Here's a pandas script to analyze your data\" | Here are your actual insights with charts |\n| \"Run this matplotlib code to see the chart\" | Here's the chart, annotated with findings |\n| \"This SQL query will find outliers\" | Found 23 outliers, here's what they mean |\n| \"You'll need scikit-learn for this\" | Model trained, here's accuracy and feature importance |\n\nYou upload data. You get answers. The code runs behind the scenes.\n\n---\n\n## What Data Work You Can Do\n\n### Exploratory Data Analysis\n\nUnderstand your data fast:\n\n- **Dataset Profiling**: \"Analyze this CSV — distributions, missing values, outliers, correlations, and data quality summary\"\n- **Pattern Discovery**: \"What patterns and trends exist in this sales data? Surprise me.\"\n- **Anomaly Detection**: \"Find unusual patterns in this server log data — what looks abnormal?\"\n- **Relationship Analysis**: \"What factors most strongly correlate with customer churn in this dataset?\"\n\n**Example prompt:**\n> \"Analyze this dataset:\n> <SHOW_FILE>/path/to/customer_data.csv</SHOW_FILE>\n> \n> I don't know muc"},{"path":"_meta.json","content":"{\n  \"ownerId\": \"kn7a96cj9q65e0bhmzahv790en80ffqm\",\n  \"slug\": \"data-cog\",\n  \"version\": \"1.0.1\",\n  \"publishedAt\": 1770774061965\n}"}],"languages":[],"docsSourceLabel":"CLAWHUB","editorialOverview":"Your data has answers. CellCog asks the right questions. #1 on DeepResearch Bench (Feb 2026) + frontier coding agent — upload messy CSVs with minimal prompting and get structured insights back: charts, dashboards, statistical reports, and clean data. Full Python access for data cleaning, exploratory analysis, visualization, hypothesis testing, ML model evaluation, and dataset profiling. Analyzes everything, presents it beautifully. Skill: data-cog Owner: nitishgargiitd Summary: Your data has answers. CellCog asks the right questions. #1 on DeepResearch Bench (Feb 2026) + frontier coding agent — upload messy CSVs with minimal prompting and get structured insights back: charts, dashboards, statistical reports, and clean data. Full Python access for data cleaning, exploratory analysis, visualization, hypothesis testing, ML model evaluation, and da","editorialQuality":{"score":100,"threshold":65,"status":"ready","wordCount":900,"uniquenessScore":49,"reasons":[]}},"media":{"evidence":{"source":"no-media","verified":false,"confidence":"low","updatedAt":"2026-04-15T00:45:39.800Z","emptyReason":"No screenshots, media assets, or demo links are available."},"primaryImageUrl":null,"mediaAssetCount":0,"assets":[],"demoUrl":null},"ownerResources":{"evidence":{"source":"unclaimed","verified":false,"confidence":"low","updatedAt":"2026-04-15T00:45:39.800Z","emptyReason":"This page has not been claimed by the agent owner."},"hasCustomPage":false,"customPageUpdatedAt":null,"customLinks":[],"structuredLinks":{"docsUrl":null,"demoUrl":null,"supportUrl":null,"pricingUrl":null,"statusUrl":null},"customPage":null},"relatedAgents":{"evidence":{"source":"protocol-neighbors","verified":false,"confidence":"medium","updatedAt":"2026-10-09T13:30:54.672Z","emptyReason":null},"items":[{"id":"b917f68a-ebff-438e-84f8-3f4b2494c0bc","entityType":"agent","canonicalPath":"/agent/activepieces-activepieces","slug":"activepieces-activepieces","name":"activepieces","description":"AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents","url":"https://github.com/activepieces/activepieces","homepage":"https://www.activepieces.com","source":"GITHUB_REPOS","protocols":["OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-04-15T02:22:12.426Z","createdAt":"2026-02-25T03:38:12.412Z","downloads":null},{"id":"5cb26759-3a39-483f-94cf-276a98c13bb8","entityType":"agent","canonicalPath":"/agent/cherryhq-cherry-studio","slug":"cherryhq-cherry-studio","name":"cherry-studio","description":"AI productivity studio with smart chat, autonomous agents, and 300+ assistants. 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