[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M718AA6TYBW3DT6HX5G84NBS":3,"skill-same:01M718AA6TYBW3DT6HX5G84NBS":72},{"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":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":50,"commitSha":51,"skillDirectory":52,"forksCount":53,"repositoryHtmlUrl":54,"repositoryHomepageUrl":6,"ownerHtmlUrl":55,"ownerBlogUrl":56,"ownerDescription":6,"updatedAt":57,"install":58,"resources":71},"01M718AA6TYBW3DT6HX5G84NBS","explore-data",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.","anthropics\u002Fknowledge-work-plugins","anthropics","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F76263028?v=4","01M697XGDPXJAVQ8391V2BCYTE","Anthropic","01M7188MPC48111TN566QNQE3X",35660,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins\u002Fblob\u002Fmain\u002Fdata\u002Fskills\u002Fexplore-data\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"data","explore data","explore","data quality","values","file","use","metrics","table","patterns","suspicious","analyze","distributions","dimensions","dataset","when","new","issues","duplicates","which","2026-11-10 10:12:30",false,"---\nname: explore-data\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>\"\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](..\u002F..\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","# \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](..\u002F..\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,"description":7,"argument-hint":49},"\u003Ctable or file>","main","e605d8ddbfb688da57dfaaceebd93f12a64ef1d3","data\u002Fskills\u002Fexplore-data",3944,"https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins","https:\u002F\u002Fgithub.com\u002Fanthropics","https:\u002F\u002Fanthropic.com","2026-11-14 06:21:52",{"source":8,"skillName":5,"commands":59,"prompt":69,"zipAvailable":70},[60,63,66],{"tool":61,"command":62},"npx","npx skills add anthropics\u002Fknowledge-work-plugins --skill explore-data",{"tool":64,"command":65},"bunx","bunx skills add anthropics\u002Fknowledge-work-plugins --skill explore-data",{"tool":67,"command":68},"pnpm","pnpm dlx skills add anthropics\u002Fknowledge-work-plugins --skill explore-data","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins\u002Fblob\u002Fmain\u002Fdata\u002Fskills\u002Fexplore-data\u002FSKILL.md",true,[],[73,99,123,146,172,198],{"skillNo":74,"name":75,"nameZh":6,"description":76,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":77,"categories":78,"keywords":79,"publishedAt":44},"01M718Y97HCGW12Q53PSJDY03W","ticket-deflector","Reads a forwarded customer email or ticket, pulls order and refund status from a payments connector (PayPal, Square, or Stripe) or Shopify, account history from the CRM, and open tickets from a support desk (Zoho Desk), drafts a tone-matched reply in the owner's writing voice, and can issue a refund through the payments connector with explicit owner approval. With Shopify connected it also runs a proactive order-triage mode that surfaces orders needing attention — unfulfilled past the promised window, payment problems, pending refunds, stuck shipments — and drafts the next action for each before the customer has to ask. Use when the user says \"draft a response,\" \"answer this customer,\" \"where's my order,\" \"I want a refund,\" \"check my orders,\" or \"anything about to blow up.\"","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins\u002Fblob\u002Fmain\u002Fsmall-business\u002Fskills\u002Fticket-deflector\u002FSKILL.md",[],[80,81,82,83,84,85,86,39,87,88,89,90,91,92,93,94,95,96,97,98],"ticket","ticket deflector","mode","up","payments","open","writing","workflow","ask","Connector","text","mail","window","next","customer","support","crm","past","read",{"skillNo":100,"name":101,"nameZh":6,"description":102,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":103,"categories":104,"keywords":105,"publishedAt":44},"01M718Y55HFAN5705ME5VEDZ9K","tax-season-organizer","Prepares tax-season materials for the owner's accountant, not tax advice. US federal tax; a non-US business gets its closed-books packet instead. Two modes: (1) quarterly estimated tax from YTD net income in the ledger (MYOB, NetSuite, QuickBooks, Xero, or Zoho Books); (2) year-end 1099 prep, scanning the ledger, PayPal, and Stripe for contractors paid over USD 600 into a 1099-NEC list with missing W-9 flags.\nAny tax request routes first to \u002Ftax-prep, which confirms the books are closed and reconciled before running this skill. Use this skill directly only when the owner says the period's books are already closed: \"books are closed, now do the 1099s,\" \"run the quarterly estimate off the closed numbers,\" or \"just the contractor W-9 list.\"","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins\u002Fblob\u002Fmain\u002Fsmall-business\u002Fskills\u002Ftax-season-organizer\u002FSKILL.md",[],[106,107,108,43,109,110,111,112,113,114,39,115,116,117,118,119,98,120,121,122],"tax season organizer","tax","us federal","netsuite","paid","end","usd","only","now","books","xero","scanning","run","request","flags","list","stripe",{"skillNo":124,"name":125,"nameZh":6,"description":126,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":127,"categories":128,"keywords":129,"publishedAt":44},"01M718Y13VVPHDD9NTDBVFJ2AZ","tax-prep","Prepares tax materials as a two-link chain — month-end-prep confirms the books are closed and reconciled first, then tax-season-organizer calculates the quarterly estimated payment or builds the year-end 1099-NEC list and accountant packet from those closed numbers. Requires a ledger (MYOB, NetSuite, QuickBooks, Xero, or Zoho Books); uses Gusto, PayPal, and Stripe when connected, else CSV upload. US federal tax math; a non-US business gets the closed-books packet. Prep material for a CPA, never tax advice, nothing filed. Trigger on \"quarterly taxes,\" \"estimated tax payment,\" \"how much should I set aside,\" \"1099s,\" \"1099-NEC,\" \"W-9s,\" \"year-end tax prep,\" \"get my books ready for my accountant,\" any phrasing that suggests a tax deadline is coming, or a question about net profit or YTD income that sounds like worry about a tax bill. An owner who says the period's books are already closed routes to tax-season-organizer directly.","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins\u002Fblob\u002Fmain\u002Fsmall-business\u002Fskills\u002Ftax-prep\u002FSKILL.md",[],[130,107,131,132,108,133,134,109,135,136,137,138,139,140,141,111,142,143,144,145],"tax prep","prep","SET","as","ready","never","zoho books","get","chain","profit","for","trigger","material","question","paypal","business",{"skillNo":147,"name":148,"nameZh":6,"description":149,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":150,"categories":151,"keywords":155,"publishedAt":44},"01M718XWTWQZ01GYSG9T8RW8TW","speed-to-lead","Makes sure no inbound lead goes unanswered. Watches web forms, the shared inbox, and the CRM for new inquiries, qualifies each one against the owner's criteria, drafts a reply in the owner's voice with real meeting times for the owner to approve and send, routes anything hot to a human right away, and logs the whole thing to the CRM. Never sends on its own. Built to run on a schedule so overnight inquiries are drafted and sorted by morning, and works from forwarded or pasted inquiries when no connector is available. Use this whenever the owner is worried about inbound leads or response time — including phrasings like \"leads are going cold,\" \"did anyone answer that inquiry,\" \"I keep missing form submissions,\" \"nothing falls through the cracks,\" \"handle my inbound,\" \"respond to this lead,\" or \"we lose people because nobody gets back fast enough.\" Reach for it for a single unanswered inquiry as readily as for a standing setup.","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins\u002Fblob\u002Fmain\u002Fsmall-business\u002Fskills\u002Fspeed-to-lead\u002FSKILL.md",[152],{"categoryNo":153,"categoryName":154,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000034","行政与办公人员",[156,157,158,159,160,161,135,162,163,164,165,40,166,167,39,168,169,89,170,171],"speed to lead","lead","speed","shared","back","cold","time","inbox","send","respond","web forms","people","schedule","logs","sure","human",{"skillNo":173,"name":174,"nameZh":6,"description":175,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":176,"categories":177,"keywords":181,"publishedAt":44},"01M718XQYMN1KY3TPS33967FK0","social-content-engine","Runs the owner's content operation as a standing thing rather than a one-off: keeps a rolling posting calendar, learns the owner's voice, generates on-brand Canva graphics, writes captions that sound like them, repurposes one asset into every channel format, and stages posts for approval instead of publishing on its own. Pulls product photos and prices from Shopify when connected; with no connectors, drafts the calendar and asset briefs for manual posting. Use this whenever the owner talks about posting, content, or social — including \"I need to post more,\" \"we've gone quiet on Instagram,\" \"what should we post this month,\" \"make the content,\" \"give me a month of posts,\" \"write this in my voice,\" \"repurpose this,\" or \"set up our content calendar.\" Reach for it for a single post as readily as for a full quarter. One handoff: a finished, approved campaign brief goes to canva-creator; this skill owns the standing operation.","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins\u002Fblob\u002Fmain\u002Fsmall-business\u002Fskills\u002Fsocial-content-engine\u002FSKILL.md",[178],{"categoryNo":179,"categoryName":180,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000036","内容创作者",[182,183,184,185,186,187,132,188,189,190,191,83,192,39,193,194,195,98,196,197],"social","social content engine","engine","content engine","social content","content","brief","posts","Captions","prices","campaign","me","product","calendar","write","drafts",{"skillNo":199,"name":200,"nameZh":6,"description":201,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":202,"categories":203,"keywords":204,"publishedAt":44},"01M718XMPJVH0EVRVP998BVZPC","smb-router","The front door to the Small Business plugin. Listens to what the owner needs right now — vague or specific — and routes them to the best skill or slash command for the moment. Also serves as a guide: explains what's available, suggests what to try next, and adapts recommendations based on stored business context. Trigger whenever the owner asks \"what can you do,\" \"help me with my business,\" \"what should I focus on,\" \"I don't know where to start,\" or any open-ended business request that doesn't clearly match a single skill.","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins\u002Fblob\u002Fmain\u002Fsmall-business\u002Fskills\u002Fsmb-router\u002FSKILL.md",[],[205,206,207,133,208,140,141,209,85,210,114,145,211,212,213,214,215,193,216,93],"router","smb","smb router","do","front","single","you","vague","can","with","plugin","focus"]