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Use this skill whenever the user mentions: analyzin...\n\nTags: latest:1.0.0\n\nVersion history:\n\nv1.0.0 | 2026-04-21T04:02:02.289Z | user\n\ninit\n\nArchive index:\n\nArchive v1.0.0: 4 files, 12345 bytes\n\nFiles: evals/evals.json (2462b), skill-card.md (2051b), SKILL.md (24095b), _meta.json (147b)\n\nFile v1.0.0:SKILL.md\n\n---\nname: data-analysis\nversion: 1.0.0\ndescription: |\n Comprehensive data analysis workflow for CSV files with interactive guidance and flexible output formats.\n Use this skill whenever the user mentions: analyzing data, CSV files, data insights, generating reports,\n data cleaning, exploratory analysis, business metrics, sales analysis, user behavior analysis, data visualization,\n creating dashboards, or asks questions like \"what does this data tell us?\" or \"analyze this dataset\".\n Also trigger when the user provides a CSV file path and asks for any kind of analysis or summary.\n This skill provides a professional 7-step workflow with quality checks, interactive cleaning strategy selection,\n and multiple output formats (Markdown report, interactive HTML, or full dashboard).\nmetadata:\n openclaw:\n emoji: \"πŸ“Š\"\n tags: [\"data\", \"analysis\", \"csv\", \"visualization\", \"insights\", \"dashboard\"]\n---\n\n# Data Analysis Skill\n\nA comprehensive, interactive data analysis workflow that transforms CSV data into actionable business insights. This skill guides you through professional data analysis from initial exploration to final deliverable, with quality gates and user confirmations at key decision points.\n\n## Core Workflow\n\nThis skill follows a 7-step methodology with 3 interaction points:\n\n```\nInput β†’ Business Understanding β†’ Data Inspection β†’ Cleaning Strategy β†’ EDA β†’ Deep Analysis β†’ Insights β†’ Output\n ↓ ↓ ↓ ↓ ↓ ↓ ↓\nRequired Interaction 1 Quality Gate Interaction 2 Auto-run Auto-run Interaction 3\n```\n\n---\n\n## Input Requirements\n\n**Required:**\n- CSV file path (absolute or relative)\n- Business question or analysis goal\n\n**Optional:**\n- Data dictionary (field descriptions)\n- Analysis depth: `--quick` (basic stats), `--standard` (default), or `--deep` (advanced modeling)\n- Auto mode: `--auto` (skip interactions, use recommended strategies)\n- Output preference: `--format=markdown|html|dashboard`\n\n**Usage examples:**\n```\n\"Analyze sales_data.csv - I want to know which channels have the best conversion rates\"\n\"Help me understand customer_behavior.csv, specifically looking at retention patterns\"\n\"Quick analysis of Q4_results.csv --quick --auto\"\n```\n\n---\n\n## Step 1: Business Understanding (Interaction Point 1)\n\n### Your Actions\n\n1. **Parse the business question** and identify:\n - Key metrics mentioned (revenue, conversion rate, churn, etc.)\n - Analysis type needed (trend analysis, comparison, attribution, prediction)\n - Expected dimensions (time, geography, customer segments, channels)\n - Chart types that would best illustrate the answer\n\n2. **Generate an analysis plan** in this format:\n ```markdown\n ## Analysis Plan\n\n **Core Question:** [Restate the user's goal in one sentence]\n\n **Key Metrics to Calculate:**\n - [Metric 1: e.g., Monthly conversion rate by channel]\n - [Metric 2: e.g., Average order value trend]\n\n **Analysis Dimensions:**\n - [e.g., Channel, Time period, Customer segment]\n\n **Expected Deliverables:**\n - [e.g., Comparison chart showing channel performance]\n - [e.g., Trend line with annotations for key events]\n ```\n\n### Interaction Point 1\n\nPresent your analysis plan and ask:\n```\nDoes this match what you're looking for?\nIf you'd like me to focus on different aspects or add something, let me know.\n```\n\n**If the business goal is unclear**, offer templates:\n```\nI can help with common scenarios:\n1. Sales Analysis (channel comparison, trend forecasting, top products)\n2. User Behavior (funnel analysis, retention cohorts, churn prediction)\n3. Operations (ROI calculation, campaign effectiveness, resource allocation)\n\nWhich best describes what you need, or would you like to describe it differently?\n```\n\nWait for user confirmation before proceeding.\n\n---\n\n## Step 2: Data Inspection (Auto-run with Quality Gate)\n\n### Your Actions\n\n1. **Load the CSV file:**\n ```python\n import pandas as pd\n import numpy as np\n\n # Try UTF-8 first, fall back to other encodings if needed\n try:\n df = pd.read_csv(file_path, encoding='utf-8')\n except UnicodeDecodeError:\n df = pd.read_csv(file_path, encoding='latin-1')\n ```\n\n2. **Generate a data overview report:**\n ```\n ## Data Overview\n\n πŸ“Š Dimensions: {rows:,} rows Γ— {cols} columns\n πŸ’Ύ Memory: {size} MB\n\n πŸ“‹ Columns:\n | Column Name | Data Type | Sample Value |\n |-------------|-----------|--------------|\n | ... | ... | ... |\n\n πŸ” Preview (first 5 rows):\n [Display formatted table]\n ```\n\n3. **Perform quality checks:**\n - **Missing values:** Count and percentage per column\n - **Duplicates:** Check for fully duplicate rows\n - **Data types:** Verify numeric columns aren't stored as strings, dates are parseable\n - **Outliers (quick check):** Flag columns with extreme values using IQR method\n\n4. **Calculate a data quality score (0-100):**\n ```\n Score = 100 - (missing_penalty + duplicate_penalty + type_mismatch_penalty)\n\n Where:\n - missing_penalty = min(40, missing_rate * 100)\n - duplicate_penalty = min(20, duplicate_rate * 100)\n - type_mismatch_penalty = 10 per column with wrong type\n ```\n\n### Quality Gate 1\n\nBased on the quality score, present findings:\n\n**Score β‰₯ 80 (Good):**\n```\nβœ… Data quality looks good (Score: {score}/100)\nMinor issues found: [list if any]\nProceeding to analysis...\n```\n\n**Score 60-79 (Fair):**\n```\n⚠️ Data has some quality issues (Score: {score}/100)\nIssues found: [list]\nI can still analyze this, but results may be limited. Continue?\n```\n\n**Score < 60 (Poor):**\n```\n🚨 Data quality is concerning (Score: {score}/100)\nMajor issues:\n- [Issue 1 with impact]\n- [Issue 2 with impact]\n\nRecommendation: Contact the data provider or provide a data dictionary.\nWould you like me to proceed with limited analysis, or should we address these issues first?\n```\n\n---\n\n## Step 3: Data Cleaning Strategy (Interaction Point 2)\n\n### Your Actions\n\n**If quality score β‰₯ 80 and issues are minor**, apply automatic fixes and report:\n```\n🧹 Applied automatic cleaning:\n- Standardized date format in 'OrderDate' column\n- Trimmed whitespace from text fields\nReady to analyze!\n```\n\n**If quality score < 80**, present issues with specific strategy options:\n\n```markdown\n## Data Cleaning Recommendations\n\n### Issue 1: Missing Values in 'Age' Column (20% missing)\n**Strategy options:**\nA. Delete rows with missing Age (lose 20% of data) ← Recommended if Age is critical\nB. Fill with median age (35 years)\nC. Fill with group average (median by Gender)\nD. Keep as-is and exclude Age from analysis\n\n### Issue 2: Outliers in 'Price' Column (3 negative values)\n**Strategy options:**\nA. Remove the 3 rows ← Recommended\nB. Set negative values to 0\nC. Set to the minimum valid price\n\n### Issue 3: Date Format Inconsistency in 'PurchaseDate'\n**Strategy options:**\nA. Standardize to YYYY-MM-DD format ← Recommended (automatic)\n```\n\n### Interaction Point 2\n\nAsk the user:\n```\nPlease choose a strategy for each issue (e.g., \"1A, 2A, 3A\"),\nor type \"recommended\" to use all recommended strategies,\nor type \"auto\" to let me decide.\n```\n\n**In auto mode (`--auto` flag):** Skip this interaction and use all recommended strategies.\n\n### Execute Cleaning\n\n1. Apply the chosen strategies\n2. Log all changes made\n3. Report the results:\n ```\n βœ… Cleaning completed:\n - Age: Filled 1,234 missing values with median (35)\n - Price: Removed 3 rows with negative values\n - PurchaseDate: Standardized format for all 6,000 rows\n\n πŸ“Š Final dataset: {final_rows:,} rows Γ— {cols} columns (was {original_rows:,} rows)\n ```\n\n4. **Save the cleaned data:**\n ```python\n cleaned_path = output_dir / 'cleaned_data.csv'\n df_clean.to_csv(cleaned_path, index=False)\n ```\n\n---\n\n## Step 4: Exploratory Data Analysis (Auto-run)\n\n### Your Actions\n\n1. **Descriptive statistics:**\n - For numeric columns: mean, median, std, min, max, quartiles\n - For categorical columns: value counts, unique values, mode\n\n2. **Single-variable analysis:**\n - **Numeric:** Generate histograms, identify distribution shape (normal, skewed, multimodal)\n - **Categorical:** Generate bar charts showing frequency distribution\n\n3. **Multi-variable analysis:**\n - **Correlation matrix:** For all numeric columns (use heatmap visualization)\n - **Cross-tabulation:** For key categorical dimensions from Step 1\n - **Scatter plots:** For top 3 correlated pairs related to the business question\n\n4. **Generate initial insights:**\n Extract Top 3-5 preliminary findings, such as:\n - \"Channel A has 3x the conversion rate of Channel B\"\n - \"Sales show a strong upward trend since March\"\n - \"Age and purchase amount have a weak negative correlation (-0.23)\"\n\n### Output\n\nSave all visualizations as PNG files:\n```python\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n# Example\nfig, ax = plt.subplots(figsize=(10, 6))\nsns.histplot(data=df, x='Age', bins=30, ax=ax)\nplt.title('Age Distribution')\nplt.savefig(output_dir / 'age_distribution.png', dpi=300, bbox_inches='tight')\nplt.close()\n```\n\nReport the findings in a structured format:\n```markdown\n## Exploratory Findings\n\n### Distribution Overview\n- [Key observation about data distribution]\n\n### Preliminary Insights\n1. **[Insight 1]:** [Data supporting it]\n2. **[Insight 2]:** [Data supporting it]\n3. **[Insight 3]:** [Data supporting it]\n\nπŸ“Š Visualizations saved: age_distribution.png, correlation_heatmap.png, channel_comparison.png\n```\n\n---\n\n## Step 5: Deep Analysis (Auto-run)\n\n### Your Actions\n\nBased on the business question type identified in Step 1, automatically choose and apply the appropriate analysis method:\n\n| Business Question Type | Analysis Method |\n|------------------------|-----------------|\n| **Trend over time** | Time series analysis with moving averages, seasonality detection |\n| **Attribution/cause** | Grouped comparison, contribution breakdown (e.g., which factor drives 80% of variance) |\n| **User behavior** | Funnel analysis (conversion at each step), cohort retention analysis |\n| **Customer value** | RFM model (Recency, Frequency, Monetary), clustering into segments |\n| **Forecasting** | Simple linear regression or exponential smoothing for trend extrapolation |\n\n### Example: Trend Analysis\n\n```python\n# Time series with moving average\ndf['Date'] = pd.to_datetime(df['Date'])\ndf = df.sort_values('Date')\ndf['7d_MA'] = df['Revenue'].rolling(window=7).mean()\n\n# Detect trend\nfrom scipy.stats import linregress\nslope, intercept, r_value, p_value, std_err = linregress(\n df['Date'].map(pd.Timestamp.toordinal),\n df['Revenue']\n)\n\nif p_value < 0.05:\n trend = \"increasing\" if slope > 0 else \"decreasing\"\n print(f\"Statistically significant {trend} trend detected (p={p_value:.4f})\")\n```\n\n### Example: RFM Analysis\n\n```python\n# Calculate RFM scores\ncurrent_date = df['PurchaseDate'].max()\nrfm = df.groupby('CustomerID').agg({\n 'PurchaseDate': lambda x: (current_date - x.max()).days, # Recency\n 'OrderID': 'count', # Frequency\n 'Amount': 'sum' # Monetary\n}).rename(columns={\n 'PurchaseDate': 'Recency',\n 'OrderID': 'Frequency',\n 'Amount': 'Monetary'\n})\n\n# Score customers (1-5 scale)\nrfm['R_Score'] = pd.qcut(rfm['Recency'], 5, labels=[5, 4, 3, 2, 1])\nrfm['F_Score'] = pd.qcut(rfm['Frequency'].rank(method='first'), 5, labels=[1, 2, 3, 4, 5])\nrfm['M_Score'] = pd.qcut(rfm['Monetary'], 5, labels=[1, 2, 3, 4, 5])\n\n# Segment customers\nrfm['Segment'] = rfm['R_Score'].astype(str) + rfm['F_Score'].astype(str) + rfm['M_Score'].astype(str)\n```\n\n### Output\n\nReport deep analysis results with specific numbers:\n```markdown\n## Deep Analysis Results\n\n### [Analysis Type: e.g., \"Channel Performance Attribution\"]\n\n**Key Metric Calculated:** [e.g., Conversion Rate by Channel]\n\n| Channel | Orders | Conversion Rate | Contribution to Revenue |\n|---------|--------|-----------------|-------------------------|\n| A | 5,234 | 8.5% | 45% |\n| B | 3,102 | 3.7% | 28% |\n| C | 1,876 | 2.1% | 27% |\n\n**Statistical Finding:**\nChannel A's conversion rate is 2.3x higher than Channel B (p < 0.001), indicating significantly better targeting or user experience.\n\nπŸ“Š Visualization saved: channel_performance.png\n```\n\n---\n\n## Step 6: Insights Generation (Auto-run)\n\n### Your Actions\n\nSynthesize all findings into a structured narrative following the What β†’ So What β†’ Now What framework:\n\n```markdown\n## Analysis Report\n\n### πŸ” Core Findings (What)\nObjective facts from the data:\n1. **[Finding 1]:** [Specific numbers and context]\n2. **[Finding 2]:** [Specific numbers and context]\n3. **[Finding 3]:** [Specific numbers and context]\n\n### πŸ’‘ Business Insights (So What)\nInterpretation and implications:\n1. **[Insight 1]:** Why this matters for the business\n - Impact: [Quantify if possible: e.g., \"Could increase revenue by 15%\"]\n - Root cause hypothesis: [Why you think this is happening]\n\n2. **[Insight 2]:** Why this matters for the business\n - Impact: [...]\n - Root cause hypothesis: [...]\n\n### 🎯 Action Recommendations (Now What)\nPrioritized next steps:\n1. **[Action 1 - High Priority]:** What to do, expected outcome\n - Timeline: [Immediate / This quarter / Long-term]\n - Owner: [Which team should handle this]\n\n2. **[Action 2 - Medium Priority]:** What to do, expected outcome\n - Timeline: [...]\n - Owner: [...]\n```\n\n### Chart Selection\n\nFor each finding, choose the most effective visualization:\n- **Trend over time** β†’ Line chart with annotations\n- **Comparison** β†’ Bar chart (horizontal if many categories)\n- **Part-to-whole** β†’ Pie chart or stacked bar\n- **Correlation** β†’ Scatter plot with trendline\n- **Distribution** β†’ Histogram or box plot\n\nEnsure all charts have:\n- Clear title stating the main message (not just \"Sales Chart\")\n- Labeled axes with units\n- Legend if multiple series\n- Data labels for key points\n\n---\n\n## Step 7: Output Delivery (Interaction Point 3)\n\n### Interaction Point 3\n\n**If user specified format in initial request** (e.g., `--format=html`), skip this and use that format.\n\n**Otherwise, present options:**\n```\n## Analysis Complete! πŸŽ‰\n\nYour analysis is ready. Please choose an output format:\n\n1. **Quick Report** - Markdown document with embedded PNG charts\n - Best for: Sharing via email, documentation, GitHub\n - Generation time: ~10 seconds\n\n2. **Interactive Report** - Single-page HTML with embedded Chart.js\n - Best for: Presentations, exploring data interactively\n - Generation time: ~30 seconds\n\n3. **Full Dashboard** - Multi-page web application (Tailwind CSS + Chart.js)\n - Best for: Sharing with stakeholders, ongoing monitoring\n - Generation time: ~2 minutes\n\nEnter 1, 2, or 3:\n```\n\n### Generate Output\n\n#### Option 1: Quick Report (Markdown)\n\n```python\nimport shutil\n\nreport_path = output_dir / 'analysis_report.md'\n\nwith open(report_path, 'w', encoding='utf-8') as f:\n f.write(f\"# Data Analysis Report\\n\\n\")\n f.write(f\"**Dataset:** {csv_filename}\\n\")\n f.write(f\"**Analysis Date:** {datetime.now().strftime('%Y-%m-%d')}\\n\\n\")\n f.write(f\"---\\n\\n\")\n f.write(f\"## Business Question\\n\\n{business_question}\\n\\n\")\n f.write(f\"## Data Overview\\n\\n{data_overview}\\n\\n\")\n f.write(f\"## Findings\\n\\n{findings}\\n\\n\")\n f.write(f\"## Insights\\n\\n{insights}\\n\\n\")\n f.write(f\"## Recommendations\\n\\n{recommendations}\\n\\n\")\n f.write(f\"---\\n\\n\")\n f.write(f\"## Appendix: Visualizations\\n\\n\")\n for chart in chart_files:\n f.write(f\"![{chart.stem}]({chart.name})\\n\\n\")\n\n# Copy all chart PNGs to the output directory\nfor chart in chart_files:\n shutil.copy(chart, output_dir)\n```\n\n#### Option 2: Interactive Report (Single HTML)\n\nUse this template structure:\n\n```html\n\n\n\n \n \n Data Analysis Report\n \n \n\n\n
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Data Analysis Report

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Business Question

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πŸ” Key Findings

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\n\n \n\n\n```\n\n#### Option 3: Full Dashboard (Multi-page)\n\nCreate a file structure:\n```\ndashboard/\nβ”œβ”€β”€ index.html (overview page)\nβ”œβ”€β”€ data.html (detailed data explorer)\nβ”œβ”€β”€ insights.html (findings and recommendations)\n└── assets/\n β”œβ”€β”€ data.json (exported data for interactivity)\n └── styles.css (custom styling)\n```\n\n### Final Delivery\n\nPresent the deliverables to the user:\n\n```markdown\n## βœ… Analysis Complete!\n\nYour analysis has been saved to: `{output_directory}`\n\n### Deliverables:\n- πŸ“„ Analysis Report: `analysis_report.{md|html}`\n- πŸ“Š Visualizations: {list of chart files}\n- 🧹 Cleaned Data: `cleaned_data.csv`\n- πŸ’Ύ Analysis Code: `analysis_notebook.ipynb` (optional)\n\n### Key Takeaways:\n1. [One-sentence summary of finding 1]\n2. [One-sentence summary of finding 2]\n3. [One-sentence summary of finding 3]\n\n[If HTML/Dashboard]\nYou can open `{report_filename}` in your browser to explore the interactive report.\n\nWould you like me to explain any part of the analysis in more detail, or make changes to the report?\n```\n\n---\n\n## Exception Handling\n\n### Large Datasets (> 100,000 rows)\n\nDetect large files early and present options:\n```\nThis dataset has {rows:,} rows. For faster analysis, I can:\n1. Analyze a random sample (10%, ~{sample_size:,} rows) ← Recommended\n2. Aggregate by {suggested_dimension} before analyzing\n3. Process the full dataset (will take longer)\n\nWhich would you prefer?\n```\n\n### Very Wide Datasets (> 50 columns)\n\n```\nThis dataset has {cols} columns. To focus the analysis, I can:\n1. Use only columns relevant to your question: {relevant_cols}\n2. Let you select which columns to include\n3. Analyze all columns (report will be lengthy)\n\nWhich approach works best?\n```\n\n### Unclear Business Goal\n\nIf the user just says \"analyze this CSV\" without a specific question:\n```\nI can analyze this data for you! To make it most useful, what would you like to understand?\n\nCommon scenarios:\n- 🎯 Performance: \"Which {dimension} performs best on {metric}?\"\n- πŸ“ˆ Trends: \"How has {metric} changed over time?\"\n- πŸ” Patterns: \"What factors correlate with {outcome}?\"\n- πŸ“Š Overview: \"Give me a general summary of this data\"\n\nOr describe what you're trying to figure out in your own words.\n```\n\n### Poor Data Quality (Score < 40)\n\n```\n⚠️ This data has significant quality issues (Score: {score}/100):\n- {issue_1}\n- {issue_2}\n\nRecommendations:\n1. Contact the data source to get a cleaner version\n2. Provide a data dictionary so I can better interpret the fields\n3. I can proceed with a limited analysis, but findings will be marked as \"low confidence\"\n\nHow would you like to proceed?\n```\n\n---\n\n## Technical Stack\n\n**Data Processing:**\n- `pandas` - Primary data manipulation\n- `numpy` - Numerical operations\n- `scipy` - Statistical tests\n\n**Visualization:**\n- `matplotlib` - Static charts (PNG output)\n- `seaborn` - Statistical visualizations\n- Chart.js (via CDN) - Interactive HTML charts\n\n**Output Generation:**\n- Markdown for quick reports\n- HTML + Tailwind CSS for styled reports\n- Chart.js for interactive visualizations\n\n**Always install required packages if missing:**\n```python\nimport subprocess\nimport sys\n\ndef ensure_package(package):\n try:\n __import__(package)\n except ImportError:\n subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", package])\n\n# Ensure core packages\nfor pkg in ['pandas', 'numpy', 'scipy', 'matplotlib', 'seaborn']:\n ensure_package(pkg)\n```\n\n---\n\n## Quality Principles\n\n1. **Always explain the \"why\"**: Don't just report numbersβ€”interpret what they mean for the business\n2. **Use concrete numbers**: \"Channel A converts at 8.5%\" beats \"Channel A converts better\"\n3. **Visualize effectively**: Choose chart types that make patterns immediately obvious\n4. **Acknowledge uncertainty**: If data quality is poor or sample size is small, say so\n5. **Prioritize recommendations**: Mark actions as High/Medium/Low priority with expected impact\n6. **Save intermediate outputs**: Always save the cleaned data and analysis code for reproducibility\n\n---\n\n## Working Directory\n\nAll outputs are saved to: `./data-analysis-results/{timestamp}/`\n\nStructure:\n```\ndata-analysis-results/\n└── 2024-03-29_14-30-45/\n β”œβ”€β”€ analysis_report.md (or .html)\n β”œβ”€β”€ cleaned_data.csv\n β”œβ”€β”€ charts/\n β”‚ β”œβ”€β”€ age_distribution.png\n β”‚ β”œβ”€β”€ correlation_heatmap.png\n β”‚ └── channel_comparison.png\n └── analysis_notebook.ipynb (if requested)\n```\n\nCreate the timestamped directory at the start:\n```python\nfrom datetime import datetime\nfrom pathlib import Path\n\ntimestamp = datetime.now().strftime('%Y-%m-%d_%H-%M-%S')\noutput_dir = Path(f'./data-analysis-results/{timestamp}')\noutput_dir.mkdir(parents=True, exist_ok=True)\n(output_dir / 'charts').mkdir(exist_ok=True)\n```\n\n---\n\n## Tips for Success\n\n- **Read the CSV early**: Don't waitβ€”load it in Step 1 to validate the file exists and is readable\n- **Keep the user informed**: After each step, give a brief status update\n- **Don't over-engineer**: If the data is clean and the question is simple, don't force complex analysis\n- **Reuse code patterns**: Common operations (loading CSV, quality checks, generating charts) should follow consistent patterns to maintain reliability\n- **Test visualizations**: Always check that charts are readable and the main message is obvious at a glance\n\nFile v1.0.0:_meta.json\n\n{\n \"ownerId\": \"kn7070e6kjytarngxs9g8h1c018577bj\",\n \"slug\": \"yz6214589-hash-data-analysis\",\n \"version\": \"1.0.0\",\n \"publishedAt\": 1776744122289\n}\n\nFile v1.0.0:skill-card.md\n\n## Description:\n\nComprehensive data analysis workflow for CSV files with interactive guidance and flexible output formats.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[yz6214589-hash](https://clawhub.ai/user/yz6214589-hash)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nExternal users and developers use this skill to guide CSV data analysis, data cleaning, exploratory analysis, visualization, insight generation, and report or dashboard delivery.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: The skill can modify the Python environment by installing analysis packages.\n\nMitigation: Run it in an isolated environment and preinstall reviewed dependencies when possible.\n\nRisk: Generated HTML reports can contact external CDNs for Chart.js and Tailwind CSS.\n\nMitigation: Use Markdown output for sensitive data, or review and self-host required assets before sharing HTML reports.\n\n## Reference(s):\n\n- [ClawHub skill page](https://clawhub.ai/yz6214589-hash/skills/yz6214589-hash-data-analysis)\n- [Chart.js CDN script used by generated HTML reports](https://cdn.jsdelivr.net/npm/chart.js@4.4.0/dist/chart.umd.min.js)\n- [Tailwind CSS CDN script used by generated HTML reports](https://cdn.tailwindcss.com)\n\n## Skill Output:\n\n**Output Type(s):** [text, markdown, code, shell commands, configuration, guidance]\n\n**Output Format:** [Markdown reports, interactive HTML reports, dashboard files, charts, cleaned CSV data, and analysis guidance.]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [May create timestamped data-analysis-results directories containing reports, charts, cleaned data, and optional analysis code.]\n\n## Skill Version(s):\n\n1.0.0 (source: frontmatter and server release evidence)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.\n\nFile v1.0.0:evals/evals.json\n\n{\n \"skill_name\": \"data-analysis\",\n \"evals\": [\n {\n \"id\": 1,\n \"prompt\": \"I need to analyze ~/.claude/skills/data-analysis-workspace/test-data/sales_channels.csv - specifically, I want to know which marketing channel has the best conversion rate and ROI. Can you help me figure out where we should invest more budget? Use --auto mode and give me a markdown report.\",\n \"expected_output\": \"Analysis report that: (1) correctly calculates conversion metrics by channel, (2) identifies Organic Search or Email Campaign as top performers, (3) calculates ROI (Revenue/Cost) per channel, (4) provides specific budget allocation recommendations with numbers, (5) includes at least 2 visualizations (channel comparison chart, trend over time), (6) generates a markdown report with embedded charts\",\n \"files\": []\n },\n {\n \"id\": 2,\n \"prompt\": \"Help me understand user retention in this file: ~/.claude/skills/data-analysis-workspace/test-data/user_behavior.csv. I'm worried about users who sign up but never make a purchase, and also want to see if there's any pattern in how long people stay active. The Age column has some missing values - just use the median to fill those. Give me an interactive HTML report.\",\n \"expected_output\": \"Analysis report that: (1) identifies the percentage of users who never purchased (FirstPurchaseDate is null), (2) calculates average days from signup to first purchase for converters, (3) analyzes retention by cohort or time period, (4) handles missing Age values as requested (median fill), (5) provides insights about user segments (e.g., by age, country, spending level), (6) generates an interactive HTML report with Chart.js visualizations\",\n \"files\": []\n },\n {\n \"id\": 3,\n \"prompt\": \"quick analysis of ~/.claude/skills/data-analysis-workspace/test-data/product_inventory.csv --quick --auto - just want to see what products are out of stock and if there are any weird prices. don't need anything fancy, just the key issues\",\n \"expected_output\": \"Quick analysis report that: (1) identifies all products with Stock=0 (out of stock items), (2) detects the negative price anomaly (P016 has -25.99), (3) flags missing values in Price and Stock columns, (4) provides a data quality score, (5) lists specific recommendations for each issue, (6) completes in quick mode without extensive visualizations, (7) outputs a concise markdown summary\",\n \"files\": []\n }\n ]\n}","readmeExcerpt":"Skill: Data Analysis Owner: yz6214589-hash Summary: Comprehensive data analysis workflow for CSV files with interactive guidance and flexible output formats. Use this skill whenever the user mentions: analyzin... Tags: latest:1.0.0 Version history: v1.0.0 | 2026-04-21T04:02:02.289Z | user init Archive index: Archive v1.0.0: 4 files, 12345 bytes Files: evals/evals.json (2462b), skill-card.md (2051b), SKILL.md (24095b)","codeSnippets":[],"executableExamples":[{"language":"text","snippet":"Input β†’ Business Understanding β†’ Data Inspection β†’ Cleaning Strategy β†’ EDA β†’ Deep Analysis β†’ Insights β†’ Output\n ↓ ↓ ↓ ↓ ↓ ↓ ↓\nRequired Interaction 1 Quality Gate Interaction 2 Auto-run Auto-run Interaction 3"},{"language":"text","snippet":"\"Analyze sales_data.csv - I want to know which channels have the best conversion rates\"\n\"Help me understand customer_behavior.csv, specifically looking at retention patterns\"\n\"Quick analysis of Q4_results.csv --quick --auto\""},{"language":"markdown","snippet":"## Analysis Plan\n\n **Core Question:** [Restate the user's goal in one sentence]\n\n **Key Metrics to Calculate:**\n - [Metric 1: e.g., Monthly conversion rate by channel]\n - [Metric 2: e.g., Average order value trend]\n\n **Analysis Dimensions:**\n - [e.g., Channel, Time period, Customer segment]\n\n **Expected Deliverables:**\n - [e.g., Comparison chart showing channel performance]\n - [e.g., Trend line with annotations for key events]"},{"language":"text","snippet":"Does this match what you're looking for?\nIf you'd like me to focus on different aspects or add something, let me know."},{"language":"text","snippet":"I can help with common scenarios:\n1. Sales Analysis (channel comparison, trend forecasting, top products)\n2. User Behavior (funnel analysis, retention cohorts, churn prediction)\n3. Operations (ROI calculation, campaign effectiveness, resource allocation)\n\nWhich best describes what you need, or would you like to describe it differently?"},{"language":"python","snippet":"import pandas as pd\n import numpy as np\n\n # Try UTF-8 first, fall back to other encodings if needed\n try:\n df = pd.read_csv(file_path, encoding='utf-8')\n except UnicodeDecodeError:\n df = pd.read_csv(file_path, encoding='latin-1')"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: data-analysis\nversion: 1.0.0\ndescription: |\n Comprehensive data analysis workflow for CSV files with interactive guidance and flexible output formats.\n Use this skill whenever the user mentions: analyzing data, CSV files, data insights, generating reports,\n data cleaning, exploratory analysis, business metrics, sales analysis, user behavior analysis, data visualization,\n creating dashboards, or asks questions like \"what does this data tell us?\" or \"analyze this dataset\".\n Also trigger when the user provides a CSV file path and asks for any kind of analysis or summary.\n This skill provides a professional 7-step workflow with quality checks, interactive cleaning strategy selection,\n and multiple output formats (Markdown report, interactive HTML, or full dashboard).\nmetadata:\n openclaw:\n emoji: \"πŸ“Š\"\n tags: [\"data\", \"analysis\", \"csv\", \"visualization\", \"insights\", \"dashboard\"]\n---\n\n# Data Analysis Skill\n\nA comprehensive, interactive data analysis workflow that transforms CSV data into actionable business insights. This skill guides you through professional data analysis from initial exploration to final deliverable, with quality gates and user confirmations at key decision points.\n\n## Core Workflow\n\nThis skill follows a 7-step methodology with 3 interaction points:\n\n```\nInput β†’ Business Understanding β†’ Data Inspection β†’ Cleaning Strategy β†’ EDA β†’ Deep Analysis β†’ Insights β†’ Output\n ↓ ↓ ↓ ↓ ↓ ↓ ↓\nRequired Interaction 1 Quality Gate Interaction 2 Auto-run Auto-run Interaction 3\n```\n\n---\n\n## Input Requirements\n\n**Required:**\n- CSV file path (absolute or relative)\n- Business question or analysis goal\n\n**Optional:**\n- Data dictionary (field descriptions)\n- Analysis depth: `--quick` (basic stats), `--standard` (default), or `--deep` (advanced modeling)\n- Auto mode: `--auto` (skip interactions, use recommended strategies)\n- Output preference: `--format=markdown|html|dashboard`\n\n**Usage examples:**\n```\n\"Analyze sales_data.csv - I want to know which channels have the best conversion rates\"\n\"Help me understand customer_behavior.csv, specifically looking at retention patterns\"\n\"Quick analysis of Q4_results.csv --quick --auto\"\n```\n\n---\n\n## Step 1: Business Understanding (Interaction Point 1)\n\n### Your Actions\n\n1. **Parse the business question** and identify:\n - Key metrics mentioned (revenue, conversion rate, churn, etc.)\n - Analysis type needed (trend analysis, comparison, attribution, prediction)\n - Expected dimensions (time, geography, customer segments, channels)\n - Chart types that would best illustrate the answer\n\n2. **Generate an analysis plan** in this format:\n ```markdown\n ## Analysis Plan\n\n **Core Question:** [Restate the user's goal in one sentence]\n\n **Key Metrics to Calculate:**\n - [Metric 1: e.g., Monthly conversion rate by channel]\n - [Metric 2: e.g., Average order value trend]\n\n **Analysis Dimensions:**\n"},{"path":"_meta.json","content":"{\n \"ownerId\": \"kn7070e6kjytarngxs9g8h1c018577bj\",\n \"slug\": \"yz6214589-hash-data-analysis\",\n \"version\": \"1.0.0\",\n \"publishedAt\": 1776744122289\n}"},{"path":"skill-card.md","content":"## Description:\n\nComprehensive data analysis workflow for CSV files with interactive guidance and flexible output formats.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[yz6214589-hash](https://clawhub.ai/user/yz6214589-hash)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nExternal users and developers use this skill to guide CSV data analysis, data cleaning, exploratory analysis, visualization, insight generation, and report or dashboard delivery.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: The skill can modify the Python environment by installing analysis packages.\n\nMitigation: Run it in an isolated environment and preinstall reviewed dependencies when possible.\n\nRisk: Generated HTML reports can contact external CDNs for Chart.js and Tailwind CSS.\n\nMitigation: Use Markdown output for sensitive data, or review and self-host required assets before sharing HTML reports.\n\n## Reference(s):\n\n- [ClawHub skill page](https://clawhub.ai/yz6214589-hash/skills/yz6214589-hash-data-analysis)\n- [Chart.js CDN script used by generated HTML reports](https://cdn.jsdelivr.net/npm/chart.js@4.4.0/dist/chart.umd.min.js)\n- [Tailwind CSS CDN script used by generated HTML reports](https://cdn.tailwindcss.com)\n\n## Skill Output:\n\n**Output Type(s):** [text, markdown, code, shell commands, configuration, guidance]\n\n**Output Format:** [Markdown reports, interactive HTML reports, dashboard files, charts, cleaned CSV data, and analysis guidance.]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [May create timestamped data-analysis-results directories containing reports, charts, cleaned data, and optional analysis code.]\n\n## Skill Version(s):\n\n1.0.0 (source: frontmatter and server release evidence)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment."},{"path":"evals/evals.json","content":"{\n \"skill_name\": \"data-analysis\",\n \"evals\": [\n {\n \"id\": 1,\n \"prompt\": \"I need to analyze ~/.claude/skills/data-analysis-workspace/test-data/sales_channels.csv - specifically, I want to know which marketing channel has the best conversion rate and ROI. Can you help me figure out where we should invest more budget? Use --auto mode and give me a markdown report.\",\n \"expected_output\": \"Analysis report that: (1) correctly calculates conversion metrics by channel, (2) identifies Organic Search or Email Campaign as top performers, (3) calculates ROI (Revenue/Cost) per channel, (4) provides specific budget allocation recommendations with numbers, (5) includes at least 2 visualizations (channel comparison chart, trend over time), (6) generates a markdown report with embedded charts\",\n \"files\": []\n },\n {\n \"id\": 2,\n \"prompt\": \"Help me understand user retention in this file: ~/.claude/skills/data-analysis-workspace/test-data/user_behavior.csv. I'm worried about users who sign up but never make a purchase, and also want to see if there's any pattern in how long people stay active. The Age column has some missing values - just use the median to fill those. Give me an interactive HTML report.\",\n \"expected_output\": \"Analysis report that: (1) identifies the percentage of users who never purchased (FirstPurchaseDate is null), (2) calculates average days from signup to first purchase for converters, (3) analyzes retention by cohort or time period, (4) handles missing Age values as requested (median fill), (5) provides insights about user segments (e.g., by age, country, spending level), (6) generates an interactive HTML report with Chart.js visualizations\",\n \"files\": []\n },\n {\n \"id\": 3,\n \"prompt\": \"quick analysis of ~/.claude/skills/data-analysis-workspace/test-data/product_inventory.csv --quick --auto - just want to see what products are out of stock and if there are any weird prices. don't need anything fancy, just the key issues\",\n \"expected_output\": \"Quick analysis report that: (1) identifies all products with Stock=0 (out of stock items), (2) detects the negative price anomaly (P016 has -25.99), (3) flags missing values in Price and Stock columns, (4) provides a data quality score, (5) lists specific recommendations for each issue, (6) completes in quick mode without extensive visualizations, (7) outputs a concise markdown summary\",\n \"files\": []\n }\n ]\n}"}],"languages":[],"docsSourceLabel":"CLAWHUB","editorialOverview":"Comprehensive data analysis workflow for CSV files with interactive guidance and flexible output formats. Use this skill whenever the user mentions: analyzin... Skill: Data Analysis Owner: yz6214589-hash Summary: Comprehensive data analysis workflow for CSV files with interactive guidance and flexible output formats. Use this skill whenever the user mentions: analyzin... 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