[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-anthropic-explore-data":3,"mdc--ym19z0-key":34,"related-org-anthropic-explore-data":2211,"related-repo-anthropic-explore-data":2400},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":12,"stars":23,"repoUrl":24,"updatedAt":25,"license":26,"forks":27,"topics":28,"repo":29,"sourceUrl":32,"mdContent":33},"explore-data","profile and explore datasets","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.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},"anthropic","Anthropic","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fanthropic.png","anthropics",[13,17,20],{"name":14,"slug":15,"type":16},"Data Quality","data-quality","tag",{"name":18,"slug":19,"type":16},"Data Analysis","data-analysis",{"name":21,"slug":22,"type":16},"Analytics","analytics",22885,"https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins","2026-04-06T17:57:26.645403",null,2736,[],{"repoUrl":24,"stars":23,"forks":27,"topics":30,"description":31},[],"Open source repository of plugins primarily intended for knowledge workers to use in Claude Cowork","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins\u002Ftree\u002FHEAD\u002Fdata\u002Fskills\u002Fexplore-data","---\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 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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",{"data":35,"body":37},{"name":4,"description":6,"argument-hint":36},"\u003Ctable or file>",{"type":38,"children":39},"root",[40,49,68,73,80,93,99,106,115,135,143,156,164,177,183,188,196,225,235,308,314,319,327,350,358,381,389,398,406,415,423,432,440,449,459,465,470,543,549,554,607,613,618,671,677,682,770,776,785,789,795,801,806,849,855,860,913,919,924,977,983,1006,1012,1018,1023,1086,1092,1097,1160,1166,1171,1194,1200,1205,1228,1234,1240,1245,1973,1979,1984,2142,2148,2153,2181,2187,2205],{"type":41,"tag":42,"props":43,"children":45},"element","h1",{"id":44},"explore-data-profile-and-explore-a-dataset",[46],{"type":47,"value":48},"text","\u002Fexplore-data - Profile and Explore a Dataset",{"type":41,"tag":50,"props":51,"children":52},"blockquote",{},[53],{"type":41,"tag":54,"props":55,"children":56},"p",{},[57,59,66],{"type":47,"value":58},"If you see unfamiliar placeholders or need to check which tools are connected, see ",{"type":41,"tag":60,"props":61,"children":63},"a",{"href":62},"..\u002F..\u002FCONNECTORS.md",[64],{"type":47,"value":65},"CONNECTORS.md",{"type":47,"value":67},".",{"type":41,"tag":54,"props":69,"children":70},{},[71],{"type":47,"value":72},"Generate a comprehensive data profile for a table or uploaded file. 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analysts]\n",{"type":41,"tag":691,"props":1940,"children":1942},{"class":1256,"line":1941},27,[1943],{"type":41,"tag":691,"props":1944,"children":1945},{"emptyLinePlaceholder":1293},[1946],{"type":47,"value":1296},{"type":41,"tag":691,"props":1948,"children":1950},{"class":1256,"line":1949},28,[1951,1955],{"type":41,"tag":691,"props":1952,"children":1953},{"style":1261},[1954],{"type":47,"value":1462},{"type":41,"tag":691,"props":1956,"children":1957},{"style":1267},[1958],{"type":47,"value":1959},"Common Query Patterns\n",{"type":41,"tag":691,"props":1961,"children":1963},{"class":1256,"line":1962},29,[1964,1968],{"type":41,"tag":691,"props":1965,"children":1966},{"style":1261},[1967],{"type":47,"value":1780},{"type":41,"tag":691,"props":1969,"children":1970},{"style":1319},[1971],{"type":47,"value":1972}," [Typical use cases for this table]\n",{"type":41,"tag":100,"props":1974,"children":1976},{"id":1975},"schema-exploration-queries",[1977],{"type":47,"value":1978},"Schema Exploration Queries",{"type":41,"tag":54,"props":1980,"children":1981},{},[1982],{"type":47,"value":1983},"When connected to a data warehouse, use these patterns to discover schema:",{"type":41,"tag":81,"props":1985,"children":1989},{"className":1986,"code":1987,"language":1988,"meta":90,"style":90},"language-sql shiki shiki-themes material-theme-lighter material-theme material-theme-palenight","-- 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","sql",[1990],{"type":41,"tag":88,"props":1991,"children":1992},{"__ignoreMap":90},[1993,2001,2009,2017,2025,2033,2040,2048,2056,2064,2072,2080,2087,2095,2103,2111,2119,2126,2134],{"type":41,"tag":691,"props":1994,"children":1995},{"class":1256,"line":1257},[1996],{"type":41,"tag":691,"props":1997,"children":1998},{},[1999],{"type":47,"value":2000},"-- List all tables in a schema (PostgreSQL)\n",{"type":41,"tag":691,"props":2002,"children":2003},{"class":1256,"line":1289},[2004],{"type":41,"tag":691,"props":2005,"children":2006},{},[2007],{"type":47,"value":2008},"SELECT table_name, table_type\n",{"type":41,"tag":691,"props":2010,"children":2011},{"class":1256,"line":1299},[2012],{"type":41,"tag":691,"props":2013,"children":2014},{},[2015],{"type":47,"value":2016},"FROM information_schema.tables\n",{"type":41,"tag":691,"props":2018,"children":2019},{"class":1256,"line":1325},[2020],{"type":41,"tag":691,"props":2021,"children":2022},{},[2023],{"type":47,"value":2024},"WHERE table_schema = 'public'\n",{"type":41,"tag":691,"props":2026,"children":2027},{"class":1256,"line":1347},[2028],{"type":41,"tag":691,"props":2029,"children":2030},{},[2031],{"type":47,"value":2032},"ORDER BY table_name;\n",{"type":41,"tag":691,"props":2034,"children":2035},{"class":1256,"line":1382},[2036],{"type":41,"tag":691,"props":2037,"children":2038},{"emptyLinePlaceholder":1293},[2039],{"type":47,"value":1296},{"type":41,"tag":691,"props":2041,"children":2042},{"class":1256,"line":1404},[2043],{"type":41,"tag":691,"props":2044,"children":2045},{},[2046],{"type":47,"value":2047},"-- Column details (PostgreSQL)\n",{"type":41,"tag":691,"props":2049,"children":2050},{"class":1256,"line":1426},[2051],{"type":41,"tag":691,"props":2052,"children":2053},{},[2054],{"type":47,"value":2055},"SELECT column_name, data_type, is_nullable, column_default\n",{"type":41,"tag":691,"props":2057,"children":2058},{"class":1256,"line":1448},[2059],{"type":41,"tag":691,"props":2060,"children":2061},{},[2062],{"type":47,"value":2063},"FROM information_schema.columns\n",{"type":41,"tag":691,"props":2065,"children":2066},{"class":1256,"line":1456},[2067],{"type":41,"tag":691,"props":2068,"children":2069},{},[2070],{"type":47,"value":2071},"WHERE table_name = 'my_table'\n",{"type":41,"tag":691,"props":2073,"children":2074},{"class":1256,"line":1470},[2075],{"type":41,"tag":691,"props":2076,"children":2077},{},[2078],{"type":47,"value":2079},"ORDER BY ordinal_position;\n",{"type":41,"tag":691,"props":2081,"children":2082},{"class":1256,"line":1478},[2083],{"type":41,"tag":691,"props":2084,"children":2085},{"emptyLinePlaceholder":1293},[2086],{"type":47,"value":1296},{"type":41,"tag":691,"props":2088,"children":2089},{"class":1256,"line":1533},[2090],{"type":41,"tag":691,"props":2091,"children":2092},{},[2093],{"type":47,"value":2094},"-- Table sizes (PostgreSQL)\n",{"type":41,"tag":691,"props":2096,"children":2097},{"class":1256,"line":1542},[2098],{"type":41,"tag":691,"props":2099,"children":2100},{},[2101],{"type":47,"value":2102},"SELECT relname, pg_size_pretty(pg_total_relation_size(relid))\n",{"type":41,"tag":691,"props":2104,"children":2105},{"class":1256,"line":1595},[2106],{"type":41,"tag":691,"props":2107,"children":2108},{},[2109],{"type":47,"value":2110},"FROM pg_catalog.pg_statio_user_tables\n",{"type":41,"tag":691,"props":2112,"children":2113},{"class":1256,"line":1647},[2114],{"type":41,"tag":691,"props":2115,"children":2116},{},[2117],{"type":47,"value":2118},"ORDER BY pg_total_relation_size(relid) DESC;\n",{"type":41,"tag":691,"props":2120,"children":2121},{"class":1256,"line":1700},[2122],{"type":41,"tag":691,"props":2123,"children":2124},{"emptyLinePlaceholder":1293},[2125],{"type":47,"value":1296},{"type":41,"tag":691,"props":2127,"children":2128},{"class":1256,"line":1753},[2129],{"type":41,"tag":691,"props":2130,"children":2131},{},[2132],{"type":47,"value":2133},"-- Row counts for all tables (general pattern)\n",{"type":41,"tag":691,"props":2135,"children":2136},{"class":1256,"line":1761},[2137],{"type":41,"tag":691,"props":2138,"children":2139},{},[2140],{"type":47,"value":2141},"-- Run per-table: SELECT COUNT(*) FROM table_name\n",{"type":41,"tag":100,"props":2143,"children":2145},{"id":2144},"lineage-and-dependencies",[2146],{"type":47,"value":2147},"Lineage and Dependencies",{"type":41,"tag":54,"props":2149,"children":2150},{},[2151],{"type":47,"value":2152},"When exploring an unfamiliar data environment:",{"type":41,"tag":116,"props":2154,"children":2155},{},[2156,2161,2166,2171,2176],{"type":41,"tag":120,"props":2157,"children":2158},{},[2159],{"type":47,"value":2160},"Start with the \"output\" tables (what reports or dashboards consume)",{"type":41,"tag":120,"props":2162,"children":2163},{},[2164],{"type":47,"value":2165},"Trace upstream: What tables feed into them?",{"type":41,"tag":120,"props":2167,"children":2168},{},[2169],{"type":47,"value":2170},"Identify raw\u002Fstaging\u002Fmart layers",{"type":41,"tag":120,"props":2172,"children":2173},{},[2174],{"type":47,"value":2175},"Map the transformation chain from raw data to analytical tables",{"type":41,"tag":120,"props":2177,"children":2178},{},[2179],{"type":47,"value":2180},"Note where data is enriched, filtered, or aggregated",{"type":41,"tag":74,"props":2182,"children":2184},{"id":2183},"tips",[2185],{"type":47,"value":2186},"Tips",{"type":41,"tag":197,"props":2188,"children":2189},{},[2190,2195,2200],{"type":41,"tag":120,"props":2191,"children":2192},{},[2193],{"type":47,"value":2194},"For very large tables (100M+ rows), profiling queries use sampling by default -- mention if you need exact counts",{"type":41,"tag":120,"props":2196,"children":2197},{},[2198],{"type":47,"value":2199},"If exploring a new dataset for the first time, this command gives you the lay of the land before writing specific queries",{"type":41,"tag":120,"props":2201,"children":2202},{},[2203],{"type":47,"value":2204},"The quality flags are heuristic -- not every flag is a real problem, but each is worth a quick look",{"type":41,"tag":2206,"props":2207,"children":2208},"style",{},[2209],{"type":47,"value":2210},"html .light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html.light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"items":2212,"total":2399},[2213,2234,2248,2260,2279,2292,2313,2333,2347,2362,2370,2383],{"slug":2214,"name":2214,"fn":2215,"description":2216,"org":2217,"tags":2218,"stars":2231,"repoUrl":2232,"updatedAt":2233},"algorithmic-art","create algorithmic art with p5.js","Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2219,2222,2225,2228],{"name":2220,"slug":2221,"type":16},"Creative","creative",{"name":2223,"slug":2224,"type":16},"Design","design",{"name":2226,"slug":2227,"type":16},"Generative Art","generative-art",{"name":2229,"slug":2230,"type":16},"JavaScript","javascript",161831,"https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fskills","2026-04-06T17:56:15.455818",{"slug":2235,"name":2235,"fn":2236,"description":2237,"org":2238,"tags":2239,"stars":2231,"repoUrl":2232,"updatedAt":2247},"brand-guidelines","apply Anthropic brand colors and typography","Applies Anthropic's official brand colors and typography to any sort of artifact that may benefit from having Anthropic's look-and-feel. Use it when brand colors or style guidelines, visual formatting, or company design standards apply.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2240,2243,2244],{"name":2241,"slug":2242,"type":16},"Branding","branding",{"name":2223,"slug":2224,"type":16},{"name":2245,"slug":2246,"type":16},"Typography","typography","2026-04-06T17:56:05.042852",{"slug":2249,"name":2249,"fn":2250,"description":2251,"org":2252,"tags":2253,"stars":2231,"repoUrl":2232,"updatedAt":2259},"canvas-design","create posters and visual art as PNG or PDF","Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2254,2255,2256],{"name":2220,"slug":2221,"type":16},{"name":2223,"slug":2224,"type":16},{"name":2257,"slug":2258,"type":16},"PDF","pdf","2026-04-06T17:56:03.794732",{"slug":2261,"name":2261,"fn":2262,"description":2263,"org":2264,"tags":2265,"stars":2231,"repoUrl":2232,"updatedAt":2278},"claude-api","build apps with the Claude API","Reference for the Claude API \u002F Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration.\nTRIGGER — read BEFORE opening the target file; don't skip because it \"looks like a one-liner\" — whenever: the prompt names Claude\u002FAnthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing\u002Fmodel choice\u002Flimits\u002Fcaching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent\u002FMCP\u002Ftool-definition\u002Fmulti-agent\u002FRAG\u002FLLM-judge\u002Fcomputer-use; generate\u002Fsummarize\u002Fextract\u002Fclassify\u002Frewrite\u002Fconverse over NL; debugging refusals\u002Fcutoffs\u002Fstreaming\u002Ftool-calls\u002Ftokens).\nSKIP only when another provider is being worked on (overrides all triggers): OpenAI\u002FGPT\u002FGemini\u002FLlama\u002FMistral\u002FCohere\u002FOllama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2266,2269,2270,2273,2275],{"name":2267,"slug":2268,"type":16},"Agents","agents",{"name":9,"slug":8,"type":16},{"name":2271,"slug":2272,"type":16},"Anthropic SDK","anthropic-sdk",{"name":2274,"slug":2261,"type":16},"Claude API",{"name":2276,"slug":2277,"type":16},"LLM","llm","2026-07-28T05:36:08.213335",{"slug":2280,"name":2280,"fn":2281,"description":2282,"org":2283,"tags":2284,"stars":2231,"repoUrl":2232,"updatedAt":2291},"doc-coauthoring","co-author documentation and technical specs","Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation tasks.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2285,2288],{"name":2286,"slug":2287,"type":16},"Documentation","documentation",{"name":2289,"slug":2290,"type":16},"Technical Writing","technical-writing","2026-04-06T17:56:14.18897",{"slug":2293,"name":2293,"fn":2294,"description":2295,"org":2296,"tags":2297,"stars":2231,"repoUrl":2232,"updatedAt":2312},"docx","create and edit Word documents","Use this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files) or Word templates (.dotx files). Triggers include: any mention of 'Word doc', 'word document', '.docx', '.dotx', or requests to produce professional documents with formatting like tables of contents, headings, page numbers, or letterheads. Also use when extracting or reorganizing content from .docx or .dotx files, inserting or replacing images in documents, performing find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a 'report', 'memo', 'letter', 'template', or similar deliverable as a Word or .docx file, use this skill. Do NOT use for PDFs, spreadsheets, Google Docs, or general coding tasks unrelated to document generation.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2298,2301,2303,2306,2309],{"name":2299,"slug":2300,"type":16},"Documents","documents",{"name":2302,"slug":2293,"type":16},"DOCX",{"name":2304,"slug":2305,"type":16},"Office","office",{"name":2307,"slug":2308,"type":16},"Templates","templates",{"name":2310,"slug":2311,"type":16},"Word","word","2026-07-18T05:16:23.136271",{"slug":2314,"name":2314,"fn":2315,"description":2316,"org":2317,"tags":2318,"stars":2231,"repoUrl":2232,"updatedAt":2332},"frontend-design","design production-grade frontend interfaces","Guidance for distinctive, intentional visual design when building new UI or reshaping an existing one. Helps with aesthetic direction, typography, and making choices that don't read as templated defaults.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2319,2320,2323,2326,2329],{"name":2223,"slug":2224,"type":16},{"name":2321,"slug":2322,"type":16},"Frontend","frontend",{"name":2324,"slug":2325,"type":16},"React","react",{"name":2327,"slug":2328,"type":16},"Tailwind CSS","tailwind-css",{"name":2330,"slug":2331,"type":16},"UI Components","ui-components","2026-04-06T17:56:16.723469",{"slug":2334,"name":2334,"fn":2335,"description":2336,"org":2337,"tags":2338,"stars":2231,"repoUrl":2232,"updatedAt":2346},"internal-comms","write internal company communications","A set of resources to help me write all kinds of internal communications, using the formats that my company likes to use. Claude should use this skill whenever asked to write some sort of internal communications (status reports, leadership updates, 3P updates, company newsletters, FAQs, incident reports, project updates, etc.).",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2339,2342,2343],{"name":2340,"slug":2341,"type":16},"Communications","communications",{"name":2307,"slug":2308,"type":16},{"name":2344,"slug":2345,"type":16},"Writing","writing","2026-04-06T17:56:20.695522",{"slug":2348,"name":2348,"fn":2349,"description":2350,"org":2351,"tags":2352,"stars":2231,"repoUrl":2232,"updatedAt":2361},"mcp-builder","build MCP servers","Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node\u002FTypeScript (MCP SDK).",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2353,2354,2357,2358],{"name":2267,"slug":2268,"type":16},{"name":2355,"slug":2356,"type":16},"API Development","api-development",{"name":2276,"slug":2277,"type":16},{"name":2359,"slug":2360,"type":16},"MCP","mcp","2026-04-06T17:56:10.357665",{"slug":2258,"name":2258,"fn":2363,"description":2364,"org":2365,"tags":2366,"stars":2231,"repoUrl":2232,"updatedAt":2369},"read edit and manipulate PDF files","Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text\u002Ftables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting\u002Fdecrypting PDFs, extracting images, and OCR on scanned PDFs to make them searchable. If the user mentions a .pdf file or asks to produce one, use this skill.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2367,2368],{"name":2299,"slug":2300,"type":16},{"name":2257,"slug":2258,"type":16},"2026-04-06T17:56:02.483316",{"slug":2371,"name":2371,"fn":2372,"description":2373,"org":2374,"tags":2375,"stars":2231,"repoUrl":2232,"updatedAt":2382},"pptx","create and edit PowerPoint presentations","Use this skill any time a .pptx or .potx file is involved in any way — as input, output, or both. This includes: creating slide decks, pitch decks, or presentations; reading, parsing, or extracting text from any .pptx or .potx file (even if the extracted content will be used elsewhere, like in an email or summary); editing, modifying, or updating existing presentations; combining or splitting slide files; working with templates (.potx), layouts, speaker notes, or comments. Trigger whenever the user mentions \"deck,\" \"slides,\" \"presentation,\" or references a .pptx or .potx filename, regardless of what they plan to do with the content afterward. If a .pptx or .potx file needs to be opened, created, or touched, use this skill.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2376,2379],{"name":2377,"slug":2378,"type":16},"PowerPoint","powerpoint",{"name":2380,"slug":2381,"type":16},"Presentations","presentations","2026-07-18T05:16:24.1471",{"slug":2384,"name":2384,"fn":2385,"description":2386,"org":2387,"tags":2388,"stars":2231,"repoUrl":2232,"updatedAt":2398},"skill-creator","create and optimize agent skills","Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2389,2390,2391,2394,2397],{"name":2267,"slug":2268,"type":16},{"name":2286,"slug":2287,"type":16},{"name":2392,"slug":2393,"type":16},"Evals","evals",{"name":2395,"slug":2396,"type":16},"Performance","performance",{"name":2289,"slug":2290,"type":16},"2026-04-19T06:45:40.804",490,{"items":2401,"total":2503},[2402,2416,2432,2443,2459,2478,2490],{"slug":2403,"name":2403,"fn":2404,"description":2405,"org":2406,"tags":2407,"stars":23,"repoUrl":24,"updatedAt":2415},"accessibility-review","run WCAG accessibility audits","Run a WCAG 2.1 AA accessibility audit on a design or page. Trigger with \"audit accessibility\", \"check a11y\", \"is this accessible?\", or when reviewing a design for color contrast, keyboard navigation, touch target size, or screen reader behavior before handoff.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2408,2411,2412],{"name":2409,"slug":2410,"type":16},"Accessibility","accessibility",{"name":2223,"slug":2224,"type":16},{"name":2413,"slug":2414,"type":16},"WCAG","wcag","2026-04-06T17:58:05.682394",{"slug":2417,"name":2417,"fn":2418,"description":2419,"org":2420,"tags":2421,"stars":23,"repoUrl":24,"updatedAt":2431},"account-research","research accounts for sales intel","Research a company or person and get actionable sales intel. Works standalone with web search, supercharged when you connect enrichment tools or your CRM. Trigger with \"research [company]\", \"look up [person]\", \"intel on [prospect]\", \"who is [name] at [company]\", or \"tell me about [company]\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2422,2425,2428],{"name":2423,"slug":2424,"type":16},"CRM","crm",{"name":2426,"slug":2427,"type":16},"Research","research",{"name":2429,"slug":2430,"type":16},"Sales","sales","2026-04-06T17:56:41.410418",{"slug":2433,"name":2433,"fn":2434,"description":2435,"org":2436,"tags":2437,"stars":23,"repoUrl":24,"updatedAt":2442},"analyze","answer data questions and run analyses","Answer data questions -- from quick lookups to full analyses. Use when looking up a single metric, investigating what's driving a trend or drop, comparing segments over time, or preparing a formal data report for stakeholders.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2438,2439,2440],{"name":21,"slug":22,"type":16},{"name":18,"slug":19,"type":16},{"name":2441,"slug":1988,"type":16},"SQL","2026-04-06T17:57:21.593647",{"slug":2444,"name":2444,"fn":2445,"description":2446,"org":2447,"tags":2448,"stars":23,"repoUrl":24,"updatedAt":2458},"architecture","create and evaluate architecture decision records","Create or evaluate an architecture decision record (ADR). Use when choosing between technologies (e.g., Kafka vs SQS), documenting a design decision with trade-offs and consequences, reviewing a system design proposal, or designing a new component from requirements and constraints.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2449,2452,2454,2455],{"name":2450,"slug":2451,"type":16},"ADR","adr",{"name":2453,"slug":2444,"type":16},"Architecture",{"name":2286,"slug":2287,"type":16},{"name":2456,"slug":2457,"type":16},"Engineering","engineering","2026-04-06T17:57:49.26444",{"slug":2460,"name":2460,"fn":2461,"description":2462,"org":2463,"tags":2464,"stars":23,"repoUrl":24,"updatedAt":2477},"audit-support","support SOX 404 control testing","Support SOX 404 compliance with control testing methodology, sample selection, and documentation standards. Use when generating testing workpapers, selecting audit samples, classifying control deficiencies, or preparing for internal or external audits.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2465,2468,2471,2474],{"name":2466,"slug":2467,"type":16},"Audit","audit",{"name":2469,"slug":2470,"type":16},"Finance","finance",{"name":2472,"slug":2473,"type":16},"Regulatory Compliance","regulatory-compliance",{"name":2475,"slug":2476,"type":16},"SOX","sox","2026-04-06T17:57:36.714815",{"slug":2479,"name":2479,"fn":2480,"description":2481,"org":2482,"tags":2483,"stars":23,"repoUrl":24,"updatedAt":2489},"brand-review","review content against brand voice","Review content against your brand voice, style guide, and messaging pillars, flagging deviations by severity with specific before\u002Fafter fixes. Use when checking a draft before it ships, when auditing copy for voice consistency and terminology, or when screening for unsubstantiated claims, missing disclaimers, and other legal flags.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2484,2485,2488],{"name":2241,"slug":2242,"type":16},{"name":2486,"slug":2487,"type":16},"Marketing","marketing",{"name":2344,"slug":2345,"type":16},"2026-04-06T17:58:19.548331",{"slug":2491,"name":2491,"fn":2492,"description":2493,"org":2494,"tags":2495,"stars":23,"repoUrl":24,"updatedAt":2502},"brand-voice-enforcement","enforce brand voice in content","This skill applies brand guidelines to content creation. It should be used when the user asks to \"write an email\", \"draft a proposal\", \"create a pitch deck\", \"write a LinkedIn post\", \"draft a presentation\", \"write a Slack message\", \"draft sales content\", or any content creation request where brand voice should be applied. Also triggers on \"on-brand\", \"brand voice\", \"enforce voice\", \"apply brand guidelines\", \"brand-aligned content\", \"write in our voice\", \"use our brand tone\", \"make this sound like us\", \"rewrite this in our tone\", or \"this doesn't sound on-brand\". Not for generating guidelines from scratch (use guideline-generation) or discovering brand materials (use discover-brand).\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2496,2497,2498,2501],{"name":2241,"slug":2242,"type":16},{"name":2340,"slug":2341,"type":16},{"name":2499,"slug":2500,"type":16},"Content Creation","content-creation",{"name":2344,"slug":2345,"type":16},"2026-04-06T18:00:23.528956",200]