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It's optimized for fast analytical queries on large datasets.\n\n**Key Features:**\n- Column-oriented storage\n- Data compression\n- Parallel query execution\n- Distributed queries\n- Real-time analytics\n\n## Table Design Patterns\n\n### MergeTree Engine (Most Common)\n\n```sql\nCREATE TABLE markets_analytics (\n    date Date,\n    market_id String,\n    market_name String,\n    volume UInt64,\n    trades UInt32,\n    unique_traders UInt32,\n    avg_trade_size Float64,\n    created_at DateTime\n) ENGINE = MergeTree()\nPARTITION BY toYYYYMM(date)\nORDER BY (date, market_id)\nSETTINGS index_granularity = 8192;\n```\n\n### ReplacingMergeTree (Deduplication)\n\n```sql\n-- For data that may have duplicates (e.g., from multiple sources)\nCREATE TABLE user_events (\n    event_id String,\n    user_id String,\n    event_type String,\n    timestamp DateTime,\n    properties String\n) ENGINE = ReplacingMergeTree()\nPARTITION BY toYYYYMM(timestamp)\nORDER BY (user_id, event_id, timestamp)\nPRIMARY KEY (user_id, event_id);\n```\n\n### AggregatingMergeTree (Pre-aggregation)\n\n```sql\n-- For maintaining aggregated metrics\nCREATE TABLE market_stats_hourly (\n    hour DateTime,\n    market_id String,\n    total_volume AggregateFunction(sum, UInt64),\n    total_trades AggregateFunction(count, UInt32),\n    unique_users AggregateFunction(uniq, String)\n) ENGINE = AggregatingMergeTree()\nPARTITION BY toYYYYMM(hour)\nORDER BY (hour, market_id);\n\n-- Query aggregated data\nSELECT\n    hour,\n    market_id,\n    sumMerge(total_volume) AS volume,\n    countMerge(total_trades) AS trades,\n    uniqMerge(unique_users) AS users\nFROM market_stats_hourly\nWHERE hour >= toStartOfHour(now() - INTERVAL 24 HOUR)\nGROUP BY hour, market_id\nORDER BY hour DESC;\n```\n\n## Query Optimization Patterns\n\n### Efficient Filtering\n\n```sql\n-- ✅ GOOD: Use indexed columns first\nSELECT *\nFROM markets_analytics\nWHERE date >= '2025-01-01'\n  AND market_id = 'market-123'\n  AND volume > 1000\nORDER BY date DESC\nLIMIT 100;\n\n-- ❌ BAD: Filter on non-indexed columns first\nSELECT *\nFROM markets_analytics\nWHERE volume > 1000\n  AND market_name LIKE '%election%'\n  AND date >= '2025-01-01';\n```\n\n### Aggregations\n\n```sql\n-- ✅ GOOD: Use ClickHouse-specific aggregation functions\nSELECT\n    toStartOfDay(created_at) AS day,\n    market_id,\n    sum(volume) AS total_volume,\n    count() AS total_trades,\n    uniq(trader_id) AS unique_traders,\n    avg(trade_size) AS avg_size\nFROM trades\nWHERE created_at >= today() - INTERVAL 7 DAY\nGROUP BY day, market_id\nORDER BY day DESC, total_volume DESC;\n\n-- ✅ Use quantile for percentiles (more efficient than percentile)\nSELECT\n    quantile(0.50)(trade_size) AS median,\n    quantile(0.95)(trade_size) AS p95,\n    quantile(0.99)(trade_size) AS p99\nFROM trades\nWHERE created_at >= now() - INTERVAL 1 HOUR;\n```\n\n### Window Functions\n\n```sql\n-- Calculate running totals\nSELECT\n    date,\n    market_id,\n    volume,\n    sum(volume) OVER (\n        PARTITION BY market_id\n        ORDER BY date\n        ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW\n    ) AS cumulative_volume\nFROM markets_analytics\nWHERE date >= today() - INTERVAL 30 DAY\nORDER BY market_id, date;\n```\n\n## Data Insertion Patterns\n\n### Bulk Insert (Recommended)\n\n```typescript\nimport { ClickHouse } from 'clickhouse'\n\nconst clickhouse = new ClickHouse({\n  url: process.env.CLICKHOUSE_URL,\n  port: 8123,\n  basicAuth: {\n    username: process.env.CLICKHOUSE_USER,\n    password: process.env.CLICKHOUSE_PASSWORD\n  }\n})\n\n\u002F\u002F ✅ Batch insert (efficient)\nasync function bulkInsertTrades(trades: Trade[]) {\n  const values = trades.map(trade => `(\n    '${trade.id}',\n    '${trade.market_id}',\n    '${trade.user_id}',\n    ${trade.amount},\n    '${trade.timestamp.toISOString()}'\n  )`).join(',')\n\n  await clickhouse.query(`\n    INSERT INTO trades (id, market_id, user_id, amount, timestamp)\n    VALUES ${values}\n  `).toPromise()\n}\n\n\u002F\u002F ❌ Individual inserts (slow)\nasync function insertTrade(trade: Trade) {\n  \u002F\u002F Don't do this in a loop!\n  await clickhouse.query(`\n    INSERT INTO trades VALUES ('${trade.id}', ...)\n  `).toPromise()\n}\n```\n\n### Streaming Insert\n\n```typescript\n\u002F\u002F For continuous data ingestion\nimport { createWriteStream } from 'fs'\nimport { pipeline } from 'stream\u002Fpromises'\n\nasync function streamInserts() {\n  const stream = clickhouse.insert('trades').stream()\n\n  for await (const batch of dataSource) {\n    stream.write(batch)\n  }\n\n  await stream.end()\n}\n```\n\n## Materialized Views\n\n### Real-time Aggregations\n\n```sql\n-- Create materialized view for hourly stats\nCREATE MATERIALIZED VIEW market_stats_hourly_mv\nTO market_stats_hourly\nAS SELECT\n    toStartOfHour(timestamp) AS hour,\n    market_id,\n    sumState(amount) AS total_volume,\n    countState() AS total_trades,\n    uniqState(user_id) AS unique_users\nFROM trades\nGROUP BY hour, market_id;\n\n-- Query the materialized view\nSELECT\n    hour,\n    market_id,\n    sumMerge(total_volume) AS volume,\n    countMerge(total_trades) AS trades,\n    uniqMerge(unique_users) AS users\nFROM market_stats_hourly\nWHERE hour >= now() - INTERVAL 24 HOUR\nGROUP BY hour, market_id;\n```\n\n## Performance Monitoring\n\n### Query Performance\n\n```sql\n-- Check slow queries\nSELECT\n    query_id,\n    user,\n    query,\n    query_duration_ms,\n    read_rows,\n    read_bytes,\n    memory_usage\nFROM system.query_log\nWHERE type = 'QueryFinish'\n  AND query_duration_ms > 1000\n  AND event_time >= now() - INTERVAL 1 HOUR\nORDER BY query_duration_ms DESC\nLIMIT 10;\n```\n\n### Table Statistics\n\n```sql\n-- Check table sizes\nSELECT\n    database,\n    table,\n    formatReadableSize(sum(bytes)) AS size,\n    sum(rows) AS rows,\n    max(modification_time) AS latest_modification\nFROM system.parts\nWHERE active\nGROUP BY database, table\nORDER BY sum(bytes) DESC;\n```\n\n## Common Analytics Queries\n\n### Time Series Analysis\n\n```sql\n-- Daily active users\nSELECT\n    toDate(timestamp) AS date,\n    uniq(user_id) AS daily_active_users\nFROM events\nWHERE timestamp >= today() - INTERVAL 30 DAY\nGROUP BY date\nORDER BY date;\n\n-- Retention analysis\nSELECT\n    signup_date,\n    countIf(days_since_signup = 0) AS day_0,\n    countIf(days_since_signup = 1) AS day_1,\n    countIf(days_since_signup = 7) AS day_7,\n    countIf(days_since_signup = 30) AS day_30\nFROM (\n    SELECT\n        user_id,\n        min(toDate(timestamp)) AS signup_date,\n        toDate(timestamp) AS activity_date,\n        dateDiff('day', signup_date, activity_date) AS days_since_signup\n    FROM events\n    GROUP BY user_id, activity_date\n)\nGROUP BY signup_date\nORDER BY signup_date DESC;\n```\n\n### Funnel Analysis\n\n```sql\n-- Conversion funnel\nSELECT\n    countIf(step = 'viewed_market') AS viewed,\n    countIf(step = 'clicked_trade') AS clicked,\n    countIf(step = 'completed_trade') AS completed,\n    round(clicked \u002F viewed * 100, 2) AS view_to_click_rate,\n    round(completed \u002F clicked * 100, 2) AS click_to_completion_rate\nFROM (\n    SELECT\n        user_id,\n        session_id,\n        event_type AS step\n    FROM events\n    WHERE event_date = today()\n)\nGROUP BY session_id;\n```\n\n### Cohort Analysis\n\n```sql\n-- User cohorts by signup month\nSELECT\n    toStartOfMonth(signup_date) AS cohort,\n    toStartOfMonth(activity_date) AS month,\n    dateDiff('month', cohort, month) AS months_since_signup,\n    count(DISTINCT user_id) AS active_users\nFROM (\n    SELECT\n        user_id,\n        min(toDate(timestamp)) OVER (PARTITION BY user_id) AS signup_date,\n        toDate(timestamp) AS activity_date\n    FROM events\n)\nGROUP BY cohort, month, months_since_signup\nORDER BY cohort, months_since_signup;\n```\n\n## Data Pipeline Patterns\n\n### ETL Pattern\n\n```typescript\n\u002F\u002F Extract, Transform, Load\nasync function etlPipeline() {\n  \u002F\u002F 1. Extract from source\n  const rawData = await extractFromPostgres()\n\n  \u002F\u002F 2. Transform\n  const transformed = rawData.map(row => ({\n    date: new Date(row.created_at).toISOString().split('T')[0],\n    market_id: row.market_slug,\n    volume: parseFloat(row.total_volume),\n    trades: parseInt(row.trade_count)\n  }))\n\n  \u002F\u002F 3. Load to ClickHouse\n  await bulkInsertToClickHouse(transformed)\n}\n\n\u002F\u002F Run periodically\nsetInterval(etlPipeline, 60 * 60 * 1000)  \u002F\u002F Every hour\n```\n\n### Change Data Capture (CDC)\n\n```typescript\n\u002F\u002F Listen to PostgreSQL changes and sync to ClickHouse\nimport { Client } from 'pg'\n\nconst pgClient = new Client({ connectionString: process.env.DATABASE_URL })\n\npgClient.query('LISTEN market_updates')\n\npgClient.on('notification', async (msg) => {\n  const update = JSON.parse(msg.payload)\n\n  await clickhouse.insert('market_updates', [\n    {\n      market_id: update.id,\n      event_type: update.operation,  \u002F\u002F INSERT, UPDATE, DELETE\n      timestamp: new Date(),\n      data: JSON.stringify(update.new_data)\n    }\n  ])\n})\n```\n\n## Best Practices\n\n### 1. Partitioning Strategy\n- Partition by time (usually month or day)\n- Avoid too many partitions (performance impact)\n- Use DATE type for partition key\n\n### 2. Ordering Key\n- Put most frequently filtered columns first\n- Consider cardinality (high cardinality first)\n- Order impacts compression\n\n### 3. Data Types\n- Use smallest appropriate type (UInt32 vs UInt64)\n- Use LowCardinality for repeated strings\n- Use Enum for categorical data\n\n### 4. Avoid\n- SELECT * (specify columns)\n- FINAL (merge data before query instead)\n- Too many JOINs (denormalize for analytics)\n- Small frequent inserts (batch instead)\n\n### 5. Monitoring\n- Track query performance\n- Monitor disk usage\n- Check merge operations\n- Review slow query log\n\n**Remember**: ClickHouse excels at analytical workloads. Design tables for your query patterns, batch inserts, and leverage materialized views for real-time aggregations.","# ClickHouse Analytics Patterns\n\nClickHouse-specific patterns for high-performance analytics and data engineering.\n\n## Overview\n\nClickHouse is a column-oriented database management system (DBMS) for online analytical processing (OLAP). It's optimized for fast analytical queries on large datasets.\n\n**Key Features:**\n- Column-oriented storage\n- Data compression\n- Parallel query execution\n- Distributed queries\n- Real-time analytics\n\n## Table Design Patterns\n\n### MergeTree Engine (Most Common)\n\n```sql\nCREATE TABLE markets_analytics (\n    date Date,\n    market_id String,\n    market_name String,\n    volume UInt64,\n    trades UInt32,\n    unique_traders UInt32,\n    avg_trade_size Float64,\n    created_at DateTime\n) ENGINE = MergeTree()\nPARTITION BY toYYYYMM(date)\nORDER BY (date, market_id)\nSETTINGS index_granularity = 8192;\n```\n\n### ReplacingMergeTree (Deduplication)\n\n```sql\n-- For data that may have duplicates (e.g., from multiple sources)\nCREATE TABLE user_events (\n    event_id String,\n    user_id String,\n    event_type String,\n    timestamp DateTime,\n    properties String\n) ENGINE = ReplacingMergeTree()\nPARTITION BY toYYYYMM(timestamp)\nORDER BY (user_id, event_id, timestamp)\nPRIMARY KEY (user_id, event_id);\n```\n\n### AggregatingMergeTree (Pre-aggregation)\n\n```sql\n-- For maintaining aggregated metrics\nCREATE TABLE market_stats_hourly (\n    hour DateTime,\n    market_id String,\n    total_volume AggregateFunction(sum, UInt64),\n    total_trades AggregateFunction(count, UInt32),\n    unique_users AggregateFunction(uniq, String)\n) ENGINE = AggregatingMergeTree()\nPARTITION BY toYYYYMM(hour)\nORDER BY (hour, market_id);\n\n-- Query aggregated data\nSELECT\n    hour,\n    market_id,\n    sumMerge(total_volume) AS volume,\n    countMerge(total_trades) AS trades,\n    uniqMerge(unique_users) AS users\nFROM market_stats_hourly\nWHERE hour >= toStartOfHour(now() - INTERVAL 24 HOUR)\nGROUP BY hour, market_id\nORDER BY hour DESC;\n```\n\n## Query Optimization Patterns\n\n### Efficient Filtering\n\n```sql\n-- ✅ GOOD: Use indexed columns first\nSELECT *\nFROM markets_analytics\nWHERE date >= '2025-01-01'\n  AND market_id = 'market-123'\n  AND volume > 1000\nORDER BY date DESC\nLIMIT 100;\n\n-- ❌ BAD: Filter on non-indexed columns first\nSELECT *\nFROM markets_analytics\nWHERE volume > 1000\n  AND market_name LIKE '%election%'\n  AND date >= '2025-01-01';\n```\n\n### Aggregations\n\n```sql\n-- ✅ GOOD: Use ClickHouse-specific aggregation functions\nSELECT\n    toStartOfDay(created_at) AS day,\n    market_id,\n    sum(volume) AS total_volume,\n    count() AS total_trades,\n    uniq(trader_id) AS unique_traders,\n    avg(trade_size) AS avg_size\nFROM trades\nWHERE created_at >= today() - INTERVAL 7 DAY\nGROUP BY day, market_id\nORDER BY day DESC, total_volume DESC;\n\n-- ✅ Use quantile for percentiles (more efficient than percentile)\nSELECT\n    quantile(0.50)(trade_size) AS median,\n    quantile(0.95)(trade_size) AS p95,\n    quantile(0.99)(trade_size) AS p99\nFROM trades\nWHERE created_at >= now() - INTERVAL 1 HOUR;\n```\n\n### Window Functions\n\n```sql\n-- Calculate running totals\nSELECT\n    date,\n    market_id,\n    volume,\n    sum(volume) OVER (\n        PARTITION BY market_id\n        ORDER BY date\n        ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW\n    ) AS cumulative_volume\nFROM markets_analytics\nWHERE date >= today() - INTERVAL 30 DAY\nORDER BY market_id, date;\n```\n\n## Data Insertion Patterns\n\n### Bulk Insert (Recommended)\n\n```typescript\nimport { ClickHouse } from 'clickhouse'\n\nconst clickhouse = new ClickHouse({\n  url: process.env.CLICKHOUSE_URL,\n  port: 8123,\n  basicAuth: {\n    username: process.env.CLICKHOUSE_USER,\n    password: process.env.CLICKHOUSE_PASSWORD\n  }\n})\n\n\u002F\u002F ✅ Batch insert (efficient)\nasync function bulkInsertTrades(trades: Trade[]) {\n  const values = trades.map(trade => `(\n    '${trade.id}',\n    '${trade.market_id}',\n    '${trade.user_id}',\n    ${trade.amount},\n    '${trade.timestamp.toISOString()}'\n  )`).join(',')\n\n  await clickhouse.query(`\n    INSERT INTO trades (id, market_id, user_id, amount, timestamp)\n    VALUES ${values}\n  `).toPromise()\n}\n\n\u002F\u002F ❌ Individual inserts (slow)\nasync function insertTrade(trade: Trade) {\n  \u002F\u002F Don't do this in a loop!\n  await clickhouse.query(`\n    INSERT INTO trades VALUES ('${trade.id}', ...)\n  `).toPromise()\n}\n```\n\n### Streaming Insert\n\n```typescript\n\u002F\u002F For continuous data ingestion\nimport { createWriteStream } from 'fs'\nimport { pipeline } from 'stream\u002Fpromises'\n\nasync function streamInserts() {\n  const stream = clickhouse.insert('trades').stream()\n\n  for await (const batch of dataSource) {\n    stream.write(batch)\n  }\n\n  await stream.end()\n}\n```\n\n## Materialized Views\n\n### Real-time Aggregations\n\n```sql\n-- Create materialized view for hourly stats\nCREATE MATERIALIZED VIEW market_stats_hourly_mv\nTO market_stats_hourly\nAS SELECT\n    toStartOfHour(timestamp) AS hour,\n    market_id,\n    sumState(amount) AS total_volume,\n    countState() AS total_trades,\n    uniqState(user_id) AS unique_users\nFROM trades\nGROUP BY hour, market_id;\n\n-- Query the materialized view\nSELECT\n    hour,\n    market_id,\n    sumMerge(total_volume) AS volume,\n    countMerge(total_trades) AS trades,\n    uniqMerge(unique_users) AS users\nFROM market_stats_hourly\nWHERE hour >= now() - INTERVAL 24 HOUR\nGROUP BY hour, market_id;\n```\n\n## Performance Monitoring\n\n### Query Performance\n\n```sql\n-- Check slow queries\nSELECT\n    query_id,\n    user,\n    query,\n    query_duration_ms,\n    read_rows,\n    read_bytes,\n    memory_usage\nFROM system.query_log\nWHERE type = 'QueryFinish'\n  AND query_duration_ms > 1000\n  AND event_time >= now() - INTERVAL 1 HOUR\nORDER BY query_duration_ms DESC\nLIMIT 10;\n```\n\n### Table Statistics\n\n```sql\n-- Check table sizes\nSELECT\n    database,\n    table,\n    formatReadableSize(sum(bytes)) AS size,\n    sum(rows) AS rows,\n    max(modification_time) AS latest_modification\nFROM system.parts\nWHERE active\nGROUP BY database, table\nORDER BY sum(bytes) DESC;\n```\n\n## Common Analytics Queries\n\n### Time Series Analysis\n\n```sql\n-- Daily active users\nSELECT\n    toDate(timestamp) AS date,\n    uniq(user_id) AS daily_active_users\nFROM events\nWHERE timestamp >= today() - INTERVAL 30 DAY\nGROUP BY date\nORDER BY date;\n\n-- Retention analysis\nSELECT\n    signup_date,\n    countIf(days_since_signup = 0) AS day_0,\n    countIf(days_since_signup = 1) AS day_1,\n    countIf(days_since_signup = 7) AS day_7,\n    countIf(days_since_signup = 30) AS day_30\nFROM (\n    SELECT\n        user_id,\n        min(toDate(timestamp)) AS signup_date,\n        toDate(timestamp) AS activity_date,\n        dateDiff('day', signup_date, activity_date) AS days_since_signup\n    FROM events\n    GROUP BY user_id, activity_date\n)\nGROUP BY signup_date\nORDER BY signup_date DESC;\n```\n\n### Funnel Analysis\n\n```sql\n-- Conversion funnel\nSELECT\n    countIf(step = 'viewed_market') AS viewed,\n    countIf(step = 'clicked_trade') AS clicked,\n    countIf(step = 'completed_trade') AS completed,\n    round(clicked \u002F viewed * 100, 2) AS view_to_click_rate,\n    round(completed \u002F clicked * 100, 2) AS click_to_completion_rate\nFROM (\n    SELECT\n        user_id,\n        session_id,\n        event_type AS step\n    FROM events\n    WHERE event_date = today()\n)\nGROUP BY session_id;\n```\n\n### Cohort Analysis\n\n```sql\n-- User cohorts by signup month\nSELECT\n    toStartOfMonth(signup_date) AS cohort,\n    toStartOfMonth(activity_date) AS month,\n    dateDiff('month', cohort, month) AS months_since_signup,\n    count(DISTINCT user_id) AS active_users\nFROM (\n    SELECT\n        user_id,\n        min(toDate(timestamp)) OVER (PARTITION BY user_id) AS signup_date,\n        toDate(timestamp) AS activity_date\n    FROM events\n)\nGROUP BY cohort, month, months_since_signup\nORDER BY cohort, months_since_signup;\n```\n\n## Data Pipeline Patterns\n\n### ETL Pattern\n\n```typescript\n\u002F\u002F Extract, Transform, Load\nasync function etlPipeline() {\n  \u002F\u002F 1. Extract from source\n  const rawData = await extractFromPostgres()\n\n  \u002F\u002F 2. Transform\n  const transformed = rawData.map(row => ({\n    date: new Date(row.created_at).toISOString().split('T')[0],\n    market_id: row.market_slug,\n    volume: parseFloat(row.total_volume),\n    trades: parseInt(row.trade_count)\n  }))\n\n  \u002F\u002F 3. Load to ClickHouse\n  await bulkInsertToClickHouse(transformed)\n}\n\n\u002F\u002F Run periodically\nsetInterval(etlPipeline, 60 * 60 * 1000)  \u002F\u002F Every hour\n```\n\n### Change Data Capture (CDC)\n\n```typescript\n\u002F\u002F Listen to PostgreSQL changes and sync to ClickHouse\nimport { Client } from 'pg'\n\nconst pgClient = new Client({ connectionString: process.env.DATABASE_URL })\n\npgClient.query('LISTEN market_updates')\n\npgClient.on('notification', async (msg) => {\n  const update = JSON.parse(msg.payload)\n\n  await clickhouse.insert('market_updates', [\n    {\n      market_id: update.id,\n      event_type: update.operation,  \u002F\u002F INSERT, UPDATE, DELETE\n      timestamp: new Date(),\n      data: JSON.stringify(update.new_data)\n    }\n  ])\n})\n```\n\n## Best Practices\n\n### 1. Partitioning Strategy\n- Partition by time (usually month or day)\n- Avoid too many partitions (performance impact)\n- Use DATE type for partition key\n\n### 2. Ordering Key\n- Put most frequently filtered columns first\n- Consider cardinality (high cardinality first)\n- Order impacts compression\n\n### 3. Data Types\n- Use smallest appropriate type (UInt32 vs UInt64)\n- Use LowCardinality for repeated strings\n- Use Enum for categorical data\n\n### 4. Avoid\n- SELECT * (specify columns)\n- FINAL (merge data before query instead)\n- Too many JOINs (denormalize for analytics)\n- Small frequent inserts (batch instead)\n\n### 5. Monitoring\n- Track query performance\n- Monitor disk usage\n- Check merge operations\n- Review slow query log\n\n**Remember**: ClickHouse excels at analytical workloads. Design tables for your query patterns, batch inserts, and leverage materialized views for real-time aggregations.",{"name":5,"metadata":57,"description":7},{"author":58,"version":59},"affaan-m","1.0","main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fcc-skill-clickhouse-io",3963,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates","https:\u002F\u002Faitmpl.com","https:\u002F\u002Fgithub.com\u002Fdavila7","https:\u002F\u002Fdanielavila.me","Building AI dev tools with LLMs","2026-11-15 01:32:48",{"source":8,"skillName":5,"commands":71,"prompt":81,"zipAvailable":82},[72,75,78],{"tool":73,"command":74},"npx","npx skills add davila7\u002Fclaude-code-templates --skill cc-skill-clickhouse-io",{"tool":76,"command":77},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill cc-skill-clickhouse-io",{"tool":79,"command":80},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill cc-skill-clickhouse-io","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fcc-skill-clickhouse-io\u002FSKILL.md",true,[],[85,112,139,166,201,225],{"skillNo":86,"name":87,"nameZh":6,"description":88,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":89,"categories":90,"keywords":92,"publishedAt":52},"01M70NHBTQ8BV0A2SCAZXEPHCX","wealthville-pool-scoring","Score DeFi liquidity pools before providing liquidity. Returns a 0-100 pool score and an Enter\u002FHold\u002FExit\u002FReduce\u002FAvoid verdict with calibrated confidence for ~68,800 Solana pools and 575 EVM pools, plus a public miss-inclusive track record. Use when asked whether a pool is worth LPing into, which pools look best right now, or how accurate the scores have actually been.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fweb-data\u002Fwealthville-pool-scoring\u002FSKILL.md",[91],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[93,94,95,96,97,37,98,99,100,101,102,103,104,105,106,107,108,109,110,111],"scoring","pool","wealthville pool scoring","liquidity","which","exit","now","use","when","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":113,"name":114,"nameZh":6,"description":115,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":116,"categories":117,"keywords":119,"publishedAt":52},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. Analyzes dead code, TODO\u002FFIXME\u002FHACK markers, dependency staleness, complexity hotspots, code duplication signals, test coverage gaps, unused exports, and outdated patterns. Use this skill when the user asks to audit their codebase for tech debt, wants a health check of their code quality, says things like \"where should I focus refactoring\", \"find code smells\", \"audit my codebase\", \"what technical debt do we have\", \"prioritize cleanup work\", or asks about code quality metrics. Also trigger when the user mentions \"code rot\", \"legacy cleanup\", \"refactoring priorities\", or wants to understand the overall health of a project before a major effort. Trigger even if the user doesn't say \"technical debt\" explicitly — any request to systematically assess code quality and identify improvement priorities belongs here.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Ftechnical-debt-auditor\u002FSKILL.md",[118],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[120,121,122,123,124,125,126,127,128,129,130,101,131,132,133,134,135,136,137,138],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":140,"name":141,"nameZh":6,"description":142,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":143,"categories":144,"keywords":150,"publishedAt":52},"01M70KPYH9QNHCNME5GJQGE22K","readonly-sql-guard","Make an agent's database access provably read-only. Use when an agent runs caller-supplied SQL against a production\u002FERP\u002Fanalytics database (a \"query\" MCP tool, text-to-SQL, an admin console), or when reviewing DB-access code for write\u002FDDL\u002Fside-effect\u002Ftransaction-escape risks. Checks what a statement CALLS, not just its shape.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdatabase\u002Freadonly-sql-guard\u002FSKILL.md",[145,148,149],{"categoryNo":146,"categoryName":147,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":27,"categoryName":28,"taxonomyType":29,"taxonomyTypeDesc":30},[151,152,153,154,155,156,157,37,158,159,160,161,162,163,164,100,165,49,101,42],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access",{"skillNo":167,"name":168,"nameZh":6,"description":169,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":170,"licenseFileUrl":171,"sourceHtmlUrl":172,"categories":173,"keywords":177,"publishedAt":52},"01M6AFY6F7CC2BBZP1QD6VTNVC","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[174],{"categoryNo":175,"categoryName":176,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,37,200],"artifacts builder","web","web artifacts builder","artifacts","builder","state","routing","state management","component","css","frontend","ui","Tools","file","components","shadcn","react","Tailwind","suite","jsx","html","single-file","ui components",{"skillNo":202,"name":203,"nameZh":6,"description":204,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":170,"licenseFileUrl":205,"sourceHtmlUrl":206,"categories":207,"keywords":212,"publishedAt":52},"01M6AFWF33SFV9Z3SF4PEEE4BF","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[208,211],{"categoryNo":209,"categoryName":210,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":175,"categoryName":176,"taxonomyType":21,"taxonomyTypeDesc":22},[213,214,215,216,217,179,218,188,189,219,220,221,222,37,223,103,224],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":226,"name":227,"nameZh":6,"description":228,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":170,"licenseFileUrl":229,"sourceHtmlUrl":230,"categories":231,"keywords":239,"publishedAt":52},"01M6AF67Q67JJJ5V51JR2P52KA","discernment-nudge","After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FSKILL.md",[232,235,236],{"categoryNo":233,"categoryName":234,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":237,"categoryName":238,"taxonomyType":29,"taxonomyTypeDesc":30},"01JBSEEDCAT000000000000036","内容创作者",[240,241,242,243,244,245,45,246,181,247,191,131,248,249,250,251,252,163,253,101,254,255,256,257,258,109,259],"discernment nudge","nudge","interpretation","help","run","reasoning","writing","lookup","content","review","conversation","short","up","invoke","you","analysis","claims","argument","assemble","recommendations"]