[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ACKMR9GK8V8EKQWJS35AR7":3,"skill-same:01M6ACKMR9GK8V8EKQWJS35AR7":87},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":16,"sourceHtmlUrl":17,"categories":18,"keywords":24,"publishedAt":30,"linkOnly":31,"markdownContent":32,"markdownBody":33,"frontMatter":34,"refName":43,"commitSha":44,"skillDirectory":45,"forksCount":46,"repositoryHtmlUrl":47,"repositoryHomepageUrl":6,"ownerHtmlUrl":48,"ownerBlogUrl":49,"ownerDescription":6,"updatedAt":50,"install":51,"resources":64},"01M6ACKMR9GK8V8EKQWJS35AR7","dataset-exploration",null,"Profile and explore a dataset to understand its shape, quality, and patterns. Use when encountering a new table or file, checking null rates and column distributions, spotting data quality issues like duplicates or suspicious values, or deciding which dimensions and metrics to analyze.","Significant-Gravitas\u002Fskills-catalog","Significant-Gravitas","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F130738209?v=4","01M6AC83EPDGBFN19MWYRREJD0","AutoGPT","01M6ACEY98TKHDWJH0B93PHNMG",2,"Apache-2.0","https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fdataset-exploration\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fdataset-exploration\u002FSKILL.md",[19],{"categoryNo":20,"categoryName":21,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[25,26,27,28,29],"dataset exploration","suspicious","github","issues","metrics","2026-11-10 10:12:30",false,"---\nname: dataset-exploration\ndescription: Profile and explore a dataset to understand its shape, quality, and patterns. Use when encountering a new table or file, checking null rates and column distributions, spotting data quality issues like duplicates or suspicious values, or deciding which dimensions and metrics to analyze.\nargument-hint: \u003Ctable or file>\nlicense: Apache-2.0\nmetadata:\n  author: Anthropic\n  source: anthropics\u002Fknowledge-work-plugins\u002Fdata\u002Fskills\u002Fexplore-data\n  source_url: https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins\u002Fblob\u002F1c7187c4fc17feefa6cde39517f12dae1249e6c4\u002Fdata\u002Fskills\u002Fexplore-data\u002FSKILL.md\n  upstream-commit: 1c7187c4fc17feefa6cde39517f12dae1249e6c4\n  original-name: explore-data\n  packaged-by: AutoGPT\n  packaged-on: '2026-09-25'\n---\n\n> Packaging adaptation by AutoGPT, 2026-09-25. Original authorship and licence are retained. Changes are limited to the recorded name, metadata and file references; see ATTRIBUTION.md in this skill package.\n\n\n# \u002Fexplore-data - Profile and Explore a Dataset\n\n> If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](references\u002FCONNECTORS.md).\n\nGenerate a comprehensive data profile for a table or uploaded file. Understand its shape, quality, and patterns before diving into analysis.\n\n## Usage\n\n```\n\u002Fexplore-data \u003Ctable_name or file>\n```\n\n## Workflow\n\n### 1. Access the Data\n\n**If a data warehouse MCP server is connected:**\n\n1. Resolve the table name (handle schema prefixes, suggest matches if ambiguous)\n2. Query table metadata: column names, types, descriptions if available\n3. Run profiling queries against the live data\n\n**If a file is provided (CSV, Excel, Parquet, JSON):**\n\n1. Read the file and load into a working dataset\n2. Infer column types from the data\n\n**If neither:**\n\n1. Ask the user to provide a table name (with their warehouse connected) or upload a file\n2. If they describe a table schema, provide guidance on what profiling queries to run\n\n### 2. Understand Structure\n\nBefore analyzing any data, understand its structure:\n\n**Table-level questions:**\n- How many rows and columns?\n- What is the grain (one row per what)?\n- What is the primary key? Is it unique?\n- When was the data last updated?\n- How far back does the data go?\n\n**Column classification** — categorize each column as one of:\n- **Identifier**: Unique keys, foreign keys, entity IDs\n- **Dimension**: Categorical attributes for grouping\u002Ffiltering (status, type, region, category)\n- **Metric**: Quantitative values for measurement (revenue, count, duration, score)\n- **Temporal**: Dates and timestamps (created_at, updated_at, event_date)\n- **Text**: Free-form text fields (description, notes, name)\n- **Boolean**: True\u002Ffalse flags\n- **Structural**: JSON, arrays, nested structures\n\n### 3. Generate Data Profile\n\nRun the following profiling checks:\n\n**Table-level metrics:**\n- Total row count\n- Column count and types breakdown\n- Approximate table size (if available from metadata)\n- Date range coverage (min\u002Fmax of date columns)\n\n**All columns:**\n- Null count and null rate\n- Distinct count and cardinality ratio (distinct \u002F total)\n- Most common values (top 5-10 with frequencies)\n- Least common values (bottom 5 to spot anomalies)\n\n**Numeric columns (metrics):**\n```\nmin, max, mean, median (p50)\nstandard deviation\npercentiles: p1, p5, p25, p75, p95, p99\nzero count\nnegative count (if unexpected)\n```\n\n**String columns (dimensions, text):**\n```\nmin length, max length, avg length\nempty string count\npattern analysis (do values follow a format?)\ncase consistency (all upper, all lower, mixed?)\nleading\u002Ftrailing whitespace count\n```\n\n**Date\u002Ftimestamp columns:**\n```\nmin date, max date\nnull dates\nfuture dates (if unexpected)\ndistribution by month\u002Fweek\ngaps in time series\n```\n\n**Boolean columns:**\n```\ntrue count, false count, null count\ntrue rate\n```\n\n**Present the profile as a clean summary table**, grouped by column type (dimensions, metrics, dates, IDs).\n\n### 4. Identify Data Quality Issues\n\nApply the quality assessment framework below. Flag potential problems:\n\n- **High null rates**: Columns with >5% nulls (warn), >20% nulls (alert)\n- **Low cardinality surprises**: Columns that should be high-cardinality but aren't (e.g., a \"user_id\" with only 50 distinct values)\n- **High cardinality surprises**: Columns that should be categorical but have too many distinct values\n- **Suspicious values**: Negative amounts where only positive expected, future dates in historical data, obviously placeholder values (e.g., \"N\u002FA\", \"TBD\", \"test\", \"999999\")\n- **Duplicate detection**: Check if there's a natural key and whether it has duplicates\n- **Distribution skew**: Extremely skewed numeric distributions that could affect averages\n- **Encoding issues**: Mixed case in categorical fields, trailing whitespace, inconsistent formats\n\n### 5. Discover Relationships and Patterns\n\nAfter profiling individual columns:\n\n- **Foreign key candidates**: ID columns that might link to other tables\n- **Hierarchies**: Columns that form natural drill-down paths (country > state > city)\n- **Correlations**: Numeric columns that move together\n- **Derived columns**: Columns that appear to be computed from others\n- **Redundant columns**: Columns with identical or near-identical information\n\n### 6. Suggest Interesting Dimensions and Metrics\n\nBased on the column profile, recommend:\n\n- **Best dimension columns** for slicing data (categorical columns with reasonable cardinality, 3-50 values)\n- **Key metric columns** for measurement (numeric columns with meaningful distributions)\n- **Time columns** suitable for trend analysis\n- **Natural groupings** or hierarchies apparent in the data\n- **Potential join keys** linking to other tables (ID columns, foreign keys)\n\n### 7. Recommend Follow-Up Analyses\n\nSuggest 3-5 specific analyses the user could run next:\n\n- \"Trend analysis on [metric] by [time_column] grouped by [dimension]\"\n- \"Distribution deep-dive on [skewed_column] to understand outliers\"\n- \"Data quality investigation on [problematic_column]\"\n- \"Correlation analysis between [metric_a] and [metric_b]\"\n- \"Cohort analysis using [date_column] and [status_column]\"\n\n## Output Format\n\n```\n## Data Profile: [table_name]\n\n### Overview\n- Rows: 2,340,891\n- Columns: 23 (8 dimensions, 6 metrics, 4 dates, 5 IDs)\n- Date range: 2021-03-15 to 2024-01-22\n\n### Column Details\n[summary table]\n\n### Data Quality Issues\n[flagged issues with severity]\n\n### Recommended Explorations\n[numbered list of suggested follow-up analyses]\n```\n\n---\n\n## Quality Assessment Framework\n\n### Completeness Score\n\nRate each column:\n- **Complete** (>99% non-null): Green\n- **Mostly complete** (95-99%): Yellow -- investigate the nulls\n- **Incomplete** (80-95%): Orange -- understand why and whether it matters\n- **Sparse** (\u003C80%): Red -- may not be usable without imputation\n\n### Consistency Checks\n\nLook for:\n- **Value format inconsistency**: Same concept represented differently (\"USA\", \"US\", \"United States\", \"us\")\n- **Type inconsistency**: Numbers stored as strings, dates in various formats\n- **Referential integrity**: Foreign keys that don't match any parent record\n- **Business rule violations**: Negative quantities, end dates before start dates, percentages > 100\n- **Cross-column consistency**: Status = \"completed\" but completed_at is null\n\n### Accuracy Indicators\n\nRed flags that suggest accuracy issues:\n- **Placeholder values**: 0, -1, 999999, \"N\u002FA\", \"TBD\", \"test\", \"xxx\"\n- **Default values**: Suspiciously high frequency of a single value\n- **Stale data**: Updated_at shows no recent changes in an active system\n- **Impossible values**: Ages > 150, dates in the far future, negative durations\n- **Round number bias**: All values ending in 0 or 5 (suggests estimation, not measurement)\n\n### Timeliness Assessment\n\n- When was the table last updated?\n- What is the expected update frequency?\n- Is there a lag between event time and load time?\n- Are there gaps in the time series?\n\n## Pattern Discovery Techniques\n\n### Distribution Analysis\n\nFor numeric columns, characterize the distribution:\n- **Normal**: Mean and median are close, bell-shaped\n- **Skewed right**: Long tail of high values (common for revenue, session duration)\n- **Skewed left**: Long tail of low values (less common)\n- **Bimodal**: Two peaks (suggests two distinct populations)\n- **Power law**: Few very large values, many small ones (common for user activity)\n- **Uniform**: Roughly equal frequency across range (often synthetic or random)\n\n### Temporal Patterns\n\nFor time series data, look for:\n- **Trend**: Sustained upward or downward movement\n- **Seasonality**: Repeating patterns (weekly, monthly, quarterly, annual)\n- **Day-of-week effects**: Weekday vs. weekend differences\n- **Holiday effects**: Drops or spikes around known holidays\n- **Change points**: Sudden shifts in level or trend\n- **Anomalies**: Individual data points that break the pattern\n\n### Segmentation Discovery\n\nIdentify natural segments by:\n- Finding categorical columns with 3-20 distinct values\n- Comparing metric distributions across segment values\n- Looking for segments with significantly different behavior\n- Testing whether segments are homogeneous or contain sub-segments\n\n### Correlation Exploration\n\nBetween numeric columns:\n- Compute correlation matrix for all metric pairs\n- Flag strong correlations (|r| > 0.7) for investigation\n- Note: Correlation does not imply causation -- flag this explicitly\n- Check for non-linear relationships (e.g., quadratic, logarithmic)\n\n## Schema Understanding and Documentation\n\n### Schema Documentation Template\n\nWhen documenting a dataset for team use:\n\n```markdown\n## Table: [schema.table_name]\n\n**Description**: [What this table represents]\n**Grain**: [One row per...]\n**Primary Key**: [column(s)]\n**Row Count**: [approximate, with date]\n**Update Frequency**: [real-time \u002F hourly \u002F daily \u002F weekly]\n**Owner**: [team or person responsible]\n\n### Key Columns\n\n| Column | Type | Description | Example Values | Notes |\n|--------|------|-------------|----------------|-------|\n| user_id | STRING | Unique user identifier | \"usr_abc123\" | FK to users.id |\n| event_type | STRING | Type of event | \"click\", \"view\", \"purchase\" | 15 distinct values |\n| revenue | DECIMAL | Transaction revenue in USD | 29.99, 149.00 | Null for non-purchase events |\n| created_at | TIMESTAMP | When the event occurred | 2024-01-15 14:23:01 | Partitioned on this column |\n\n### Relationships\n- Joins to `users` on `user_id`\n- Joins to `products` on `product_id`\n- Parent of `event_details` (1:many on event_id)\n\n### Known Issues\n- [List any known data quality issues]\n- [Note any gotchas for analysts]\n\n### Common Query Patterns\n- [Typical use cases for this table]\n```\n\n### Schema Exploration Queries\n\nWhen connected to a data warehouse, use these patterns to discover schema:\n\n```sql\n-- List all tables in a schema (PostgreSQL)\nSELECT table_name, table_type\nFROM information_schema.tables\nWHERE table_schema = 'public'\nORDER BY table_name;\n\n-- Column details (PostgreSQL)\nSELECT column_name, data_type, is_nullable, column_default\nFROM information_schema.columns\nWHERE table_name = 'my_table'\nORDER BY ordinal_position;\n\n-- Table sizes (PostgreSQL)\nSELECT relname, pg_size_pretty(pg_total_relation_size(relid))\nFROM pg_catalog.pg_statio_user_tables\nORDER BY pg_total_relation_size(relid) DESC;\n\n-- Row counts for all tables (general pattern)\n-- Run per-table: SELECT COUNT(*) FROM table_name\n```\n\n### Lineage and Dependencies\n\nWhen exploring an unfamiliar data environment:\n\n1. Start with the \"output\" tables (what reports or dashboards consume)\n2. Trace upstream: What tables feed into them?\n3. Identify raw\u002Fstaging\u002Fmart layers\n4. Map the transformation chain from raw data to analytical tables\n5. Note where data is enriched, filtered, or aggregated\n\n## Tips\n\n- For very large tables (100M+ rows), profiling queries use sampling by default -- mention if you need exact counts\n- If exploring a new dataset for the first time, this command gives you the lay of the land before writing specific queries\n- The quality flags are heuristic -- not every flag is a real problem, but each is worth a quick look\n","> Packaging adaptation by AutoGPT, 2026-09-25. Original authorship and licence are retained. Changes are limited to the recorded name, metadata and file references; see ATTRIBUTION.md in this skill package.\n\n\n# \u002Fexplore-data - Profile and Explore a Dataset\n\n> If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](references\u002FCONNECTORS.md).\n\nGenerate a comprehensive data profile for a table or uploaded file. Understand its shape, quality, and patterns before diving into analysis.\n\n## Usage\n\n```\n\u002Fexplore-data \u003Ctable_name or file>\n```\n\n## Workflow\n\n### 1. Access the Data\n\n**If a data warehouse MCP server is connected:**\n\n1. Resolve the table name (handle schema prefixes, suggest matches if ambiguous)\n2. Query table metadata: column names, types, descriptions if available\n3. Run profiling queries against the live data\n\n**If a file is provided (CSV, Excel, Parquet, JSON):**\n\n1. Read the file and load into a working dataset\n2. Infer column types from the data\n\n**If neither:**\n\n1. Ask the user to provide a table name (with their warehouse connected) or upload a file\n2. If they describe a table schema, provide guidance on what profiling queries to run\n\n### 2. Understand Structure\n\nBefore analyzing any data, understand its structure:\n\n**Table-level questions:**\n- How many rows and columns?\n- What is the grain (one row per what)?\n- What is the primary key? Is it unique?\n- When was the data last updated?\n- How far back does the data go?\n\n**Column classification** — categorize each column as one of:\n- **Identifier**: Unique keys, foreign keys, entity IDs\n- **Dimension**: Categorical attributes for grouping\u002Ffiltering (status, type, region, category)\n- **Metric**: Quantitative values for measurement (revenue, count, duration, score)\n- **Temporal**: Dates and timestamps (created_at, updated_at, event_date)\n- **Text**: Free-form text fields (description, notes, name)\n- **Boolean**: True\u002Ffalse flags\n- **Structural**: JSON, arrays, nested structures\n\n### 3. Generate Data Profile\n\nRun the following profiling checks:\n\n**Table-level metrics:**\n- Total row count\n- Column count and types breakdown\n- Approximate table size (if available from metadata)\n- Date range coverage (min\u002Fmax of date columns)\n\n**All columns:**\n- Null count and null rate\n- Distinct count and cardinality ratio (distinct \u002F total)\n- Most common values (top 5-10 with frequencies)\n- Least common values (bottom 5 to spot anomalies)\n\n**Numeric columns (metrics):**\n```\nmin, max, mean, median (p50)\nstandard deviation\npercentiles: p1, p5, p25, p75, p95, p99\nzero count\nnegative count (if unexpected)\n```\n\n**String columns (dimensions, text):**\n```\nmin length, max length, avg length\nempty string count\npattern analysis (do values follow a format?)\ncase consistency (all upper, all lower, mixed?)\nleading\u002Ftrailing whitespace count\n```\n\n**Date\u002Ftimestamp columns:**\n```\nmin date, max date\nnull dates\nfuture dates (if unexpected)\ndistribution by month\u002Fweek\ngaps in time series\n```\n\n**Boolean columns:**\n```\ntrue count, false count, null count\ntrue rate\n```\n\n**Present the profile as a clean summary table**, grouped by column type (dimensions, metrics, dates, IDs).\n\n### 4. Identify Data Quality Issues\n\nApply the quality assessment framework below. Flag potential problems:\n\n- **High null rates**: Columns with >5% nulls (warn), >20% nulls (alert)\n- **Low cardinality surprises**: Columns that should be high-cardinality but aren't (e.g., a \"user_id\" with only 50 distinct values)\n- **High cardinality surprises**: Columns that should be categorical but have too many distinct values\n- **Suspicious values**: Negative amounts where only positive expected, future dates in historical data, obviously placeholder values (e.g., \"N\u002FA\", \"TBD\", \"test\", \"999999\")\n- **Duplicate detection**: Check if there's a natural key and whether it has duplicates\n- **Distribution skew**: Extremely skewed numeric distributions that could affect averages\n- **Encoding issues**: Mixed case in categorical fields, trailing whitespace, inconsistent formats\n\n### 5. Discover Relationships and Patterns\n\nAfter profiling individual columns:\n\n- **Foreign key candidates**: ID columns that might link to other tables\n- **Hierarchies**: Columns that form natural drill-down paths (country > state > city)\n- **Correlations**: Numeric columns that move together\n- **Derived columns**: Columns that appear to be computed from others\n- **Redundant columns**: Columns with identical or near-identical information\n\n### 6. Suggest Interesting Dimensions and Metrics\n\nBased on the column profile, recommend:\n\n- **Best dimension columns** for slicing data (categorical columns with reasonable cardinality, 3-50 values)\n- **Key metric columns** for measurement (numeric columns with meaningful distributions)\n- **Time columns** suitable for trend analysis\n- **Natural groupings** or hierarchies apparent in the data\n- **Potential join keys** linking to other tables (ID columns, foreign keys)\n\n### 7. Recommend Follow-Up Analyses\n\nSuggest 3-5 specific analyses the user could run next:\n\n- \"Trend analysis on [metric] by [time_column] grouped by [dimension]\"\n- \"Distribution deep-dive on [skewed_column] to understand outliers\"\n- \"Data quality investigation on [problematic_column]\"\n- \"Correlation analysis between [metric_a] and [metric_b]\"\n- \"Cohort analysis using [date_column] and [status_column]\"\n\n## Output Format\n\n```\n## Data Profile: [table_name]\n\n### Overview\n- Rows: 2,340,891\n- Columns: 23 (8 dimensions, 6 metrics, 4 dates, 5 IDs)\n- Date range: 2021-03-15 to 2024-01-22\n\n### Column Details\n[summary table]\n\n### Data Quality Issues\n[flagged issues with severity]\n\n### Recommended Explorations\n[numbered list of suggested follow-up analyses]\n```\n\n---\n\n## Quality Assessment Framework\n\n### Completeness Score\n\nRate each column:\n- **Complete** (>99% non-null): Green\n- **Mostly complete** (95-99%): Yellow -- investigate the nulls\n- **Incomplete** (80-95%): Orange -- understand why and whether it matters\n- **Sparse** (\u003C80%): Red -- may not be usable without imputation\n\n### Consistency Checks\n\nLook for:\n- **Value format inconsistency**: Same concept represented differently (\"USA\", \"US\", \"United States\", \"us\")\n- **Type inconsistency**: Numbers stored as strings, dates in various formats\n- **Referential integrity**: Foreign keys that don't match any parent record\n- **Business rule violations**: Negative quantities, end dates before start dates, percentages > 100\n- **Cross-column consistency**: Status = \"completed\" but completed_at is null\n\n### Accuracy Indicators\n\nRed flags that suggest accuracy issues:\n- **Placeholder values**: 0, -1, 999999, \"N\u002FA\", \"TBD\", \"test\", \"xxx\"\n- **Default values**: Suspiciously high frequency of a single value\n- **Stale data**: Updated_at shows no recent changes in an active system\n- **Impossible values**: Ages > 150, dates in the far future, negative durations\n- **Round number bias**: All values ending in 0 or 5 (suggests estimation, not measurement)\n\n### Timeliness Assessment\n\n- When was the table last updated?\n- What is the expected update frequency?\n- Is there a lag between event time and load time?\n- Are there gaps in the time series?\n\n## Pattern Discovery Techniques\n\n### Distribution Analysis\n\nFor numeric columns, characterize the distribution:\n- **Normal**: Mean and median are close, bell-shaped\n- **Skewed right**: Long tail of high values (common for revenue, session duration)\n- **Skewed left**: Long tail of low values (less common)\n- **Bimodal**: Two peaks (suggests two distinct populations)\n- **Power law**: Few very large values, many small ones (common for user activity)\n- **Uniform**: Roughly equal frequency across range (often synthetic or random)\n\n### Temporal Patterns\n\nFor time series data, look for:\n- **Trend**: Sustained upward or downward movement\n- **Seasonality**: Repeating patterns (weekly, monthly, quarterly, annual)\n- **Day-of-week effects**: Weekday vs. weekend differences\n- **Holiday effects**: Drops or spikes around known holidays\n- **Change points**: Sudden shifts in level or trend\n- **Anomalies**: Individual data points that break the pattern\n\n### Segmentation Discovery\n\nIdentify natural segments by:\n- Finding categorical columns with 3-20 distinct values\n- Comparing metric distributions across segment values\n- Looking for segments with significantly different behavior\n- Testing whether segments are homogeneous or contain sub-segments\n\n### Correlation Exploration\n\nBetween numeric columns:\n- Compute correlation matrix for all metric pairs\n- Flag strong correlations (|r| > 0.7) for investigation\n- Note: Correlation does not imply causation -- flag this explicitly\n- Check for non-linear relationships (e.g., quadratic, logarithmic)\n\n## Schema Understanding and Documentation\n\n### Schema Documentation Template\n\nWhen documenting a dataset for team use:\n\n```markdown\n## Table: [schema.table_name]\n\n**Description**: [What this table represents]\n**Grain**: [One row per...]\n**Primary Key**: [column(s)]\n**Row Count**: [approximate, with date]\n**Update Frequency**: [real-time \u002F hourly \u002F daily \u002F weekly]\n**Owner**: [team or person responsible]\n\n### Key Columns\n\n| Column | Type | Description | Example Values | Notes |\n|--------|------|-------------|----------------|-------|\n| user_id | STRING | Unique user identifier | \"usr_abc123\" | FK to users.id |\n| event_type | STRING | Type of event | \"click\", \"view\", \"purchase\" | 15 distinct values |\n| revenue | DECIMAL | Transaction revenue in USD | 29.99, 149.00 | Null for non-purchase events |\n| created_at | TIMESTAMP | When the event occurred | 2024-01-15 14:23:01 | Partitioned on this column |\n\n### Relationships\n- Joins to `users` on `user_id`\n- Joins to `products` on `product_id`\n- Parent of `event_details` (1:many on event_id)\n\n### Known Issues\n- [List any known data quality issues]\n- [Note any gotchas for analysts]\n\n### Common Query Patterns\n- [Typical use cases for this table]\n```\n\n### Schema Exploration Queries\n\nWhen connected to a data warehouse, use these patterns to discover schema:\n\n```sql\n-- List all tables in a schema (PostgreSQL)\nSELECT table_name, table_type\nFROM information_schema.tables\nWHERE table_schema = 'public'\nORDER BY table_name;\n\n-- Column details (PostgreSQL)\nSELECT column_name, data_type, is_nullable, column_default\nFROM information_schema.columns\nWHERE table_name = 'my_table'\nORDER BY ordinal_position;\n\n-- Table sizes (PostgreSQL)\nSELECT relname, pg_size_pretty(pg_total_relation_size(relid))\nFROM pg_catalog.pg_statio_user_tables\nORDER BY pg_total_relation_size(relid) DESC;\n\n-- Row counts for all tables (general pattern)\n-- Run per-table: SELECT COUNT(*) FROM table_name\n```\n\n### Lineage and Dependencies\n\nWhen exploring an unfamiliar data environment:\n\n1. Start with the \"output\" tables (what reports or dashboards consume)\n2. Trace upstream: What tables feed into them?\n3. Identify raw\u002Fstaging\u002Fmart layers\n4. Map the transformation chain from raw data to analytical tables\n5. Note where data is enriched, filtered, or aggregated\n\n## Tips\n\n- For very large tables (100M+ rows), profiling queries use sampling by default -- mention if you need exact counts\n- If exploring a new dataset for the first time, this command gives you the lay of the land before writing specific queries\n- The quality flags are heuristic -- not every flag is a real problem, but each is worth a quick look\n",{"name":5,"license":15,"metadata":35,"description":7,"argument-hint":42},{"author":36,"source":37,"source_url":38,"packaged-by":12,"packaged-on":39,"original-name":40,"upstream-commit":41},"Anthropic","anthropics\u002Fknowledge-work-plugins\u002Fdata\u002Fskills\u002Fexplore-data","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins\u002Fblob\u002F1c7187c4fc17feefa6cde39517f12dae1249e6c4\u002Fdata\u002Fskills\u002Fexplore-data\u002FSKILL.md","2026-09-25","explore-data","1c7187c4fc17feefa6cde39517f12dae1249e6c4","\u003Ctable or file>","main","7ed3b73314d5186120e8d4006febfd7e1c92cf08","skills\u002Fdataset-exploration",0,"https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002Fskills-catalog","https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas","agpt.co","2026-11-10 22:38:50",{"source":8,"skillName":5,"commands":52,"prompt":62,"zipAvailable":63},[53,56,59],{"tool":54,"command":55},"npx","npx skills add Significant-Gravitas\u002Fskills-catalog --skill dataset-exploration",{"tool":57,"command":58},"bunx","bunx skills add Significant-Gravitas\u002Fskills-catalog --skill dataset-exploration",{"tool":60,"command":61},"pnpm","pnpm dlx skills add Significant-Gravitas\u002Fskills-catalog --skill dataset-exploration","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fdataset-exploration\u002FSKILL.md",true,[65,71,75,79,83],{"path":66,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":69,"sourceHtmlUrl":70},"ATTRIBUTION.md","REFERENCE","参考文档",4704,"https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fdataset-exploration\u002FATTRIBUTION.md",{"path":72,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":74,"sourceHtmlUrl":16},"LICENSE","许可证",11358,{"path":76,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":77,"sourceHtmlUrl":78},"references\u002Fbatching-categories.md",5971,"https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Freact18-batching-patterns\u002Freferences\u002Fbatching-categories.md",{"path":80,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":81,"sourceHtmlUrl":82},"references\u002FCONNECTORS.md",1401,"https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fdataset-exploration\u002Freferences\u002FCONNECTORS.md",{"path":84,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":85,"sourceHtmlUrl":86},"references\u002FflushSync-guide.md",2469,"https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Freact18-batching-patterns\u002Freferences\u002FflushSync-guide.md",[88,101,127,137,151,169],{"skillNo":89,"name":90,"nameZh":6,"description":91,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":92,"sourceHtmlUrl":93,"categories":94,"keywords":95,"publishedAt":30},"01M6AD3S89TGF74S7MK4K5CGC7","workplace-memory-management","Two-tier memory system that makes Claude a true workplace collaborator. Decodes shorthand, acronyms, nicknames, and internal language so Claude understands requests like a colleague would. CLAUDE.md for working memory, memory\u002F directory for the full knowledge base.","https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fworkplace-memory-management\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fworkplace-memory-management\u002FSKILL.md",[],[96,97,98,99,27,100],"workplace memory management","memory","plugins","https","directory",{"skillNo":102,"name":103,"nameZh":6,"description":104,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":105,"licenseFileUrl":106,"sourceHtmlUrl":107,"categories":108,"keywords":120,"publishedAt":30},"01M6AD3F5X5DYW1BABD49BQKP8","website-seo-audit","Audit a website, investigate its real search opportunities, and deliver a short data-backed report on the few changes most likely to grow organic traffic that converts.","MIT","https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fwebsite-seo-audit\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fwebsite-seo-audit\u002FSKILL.md",[109,114,117],{"categoryNo":110,"categoryName":111,"taxonomyType":112,"taxonomyTypeDesc":113},"01JBSEEDCAT000000000000019","营销推广","SCENARIO","使用场景",{"categoryNo":115,"categoryName":116,"taxonomyType":112,"taxonomyTypeDesc":113},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":118,"categoryName":119,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000035","市场营销",[121,122,123,27,98,99,124,125,126],"website seo audit","audit","website","search","report","plugin",{"skillNo":128,"name":129,"nameZh":6,"description":130,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":105,"licenseFileUrl":131,"sourceHtmlUrl":132,"categories":133,"keywords":134,"publishedAt":30},"01M6AD32RYMK03P50DM0T184NJ","verification-before-completion","Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always","https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fverification-before-completion\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fverification-before-completion\u002FSKILL.md",[],[135,136,99,27],"verification before completion","verification",{"skillNo":138,"name":139,"nameZh":6,"description":140,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":141,"sourceHtmlUrl":142,"categories":143,"keywords":144,"publishedAt":30},"01M6AD2WSQG7BGPXA7XP4DFZX8","vendor-evaluation","Evaluate a vendor — cost analysis, risk assessment, and recommendation. Use when reviewing a new vendor proposal, deciding whether to renew or replace a contract, comparing two vendors side-by-side, or building a TCO breakdown and negotiation points before procurement sign-off.","https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fvendor-evaluation\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fvendor-evaluation\u002FSKILL.md",[],[145,146,98,99,147,148,149,27,150],"vendor evaluation","negotiation","assessment","risk assessment","cost analysis","review",{"skillNo":152,"name":153,"nameZh":6,"description":154,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":155,"sourceHtmlUrl":156,"categories":157,"keywords":164,"publishedAt":30},"01M6AD2RBT6004C07RZRDSTA52","vendor-contract-status","Check the status of existing agreements with a vendor across all connected systems — CLM, CRM, email, and document storage — with gap analysis and upcoming deadlines. Use when onboarding or renewing a vendor, when you need a consolidated view of what's signed and what's missing (MSA, DPA, SOW), or when checking for approaching expirations and surviving obligations.","https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fvendor-contract-status\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fvendor-contract-status\u002FSKILL.md",[158,161],{"categoryNo":159,"categoryName":160,"taxonomyType":112,"taxonomyTypeDesc":113},"01JBSEEDCAT000000000000005","邮件与沟通",{"categoryNo":162,"categoryName":163,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000037","律师与法务",[165,166,27,167,98,168,99],"vendor contract status","status","storage","onboarding",{"skillNo":170,"name":171,"nameZh":6,"description":172,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":105,"licenseFileUrl":173,"sourceHtmlUrl":174,"categories":175,"keywords":177,"publishedAt":30},"01M6AD2KPNXN5CNS3CA4FRWT97","user-cohort-analysis","Perform cohort analysis on user engagement data — retention curves, feature adoption trends, and segment-level insights. Use when analyzing user retention by cohort, studying feature adoption over time, investigating churn patterns, or identifying engagement trends.","https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fuser-cohort-analysis\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fuser-cohort-analysis\u002FSKILL.md",[176],{"categoryNo":20,"categoryName":21,"taxonomyType":22,"taxonomyTypeDesc":23},[178,179,99,27],"user cohort analysis","analytics"]