Cloudflare GraphQL Analytics for zone traffic, firewall events, Workers metrics, and schema exploration. Use when querying Cloudflare analytics data or exploring the GraphQL API.
日本語の概要は準備中です。原文の説明を表示しています。
Use when working with Snowflake — snowflake data warehouse analysis, query performance tuning, cost optimization, warehouse management, and schema inspection. Covers QUERY_HISTORY analysis, credit consumption, warehouse utilization, storage analysis, data sharing, and Time Travel. Read this skill before any Snowflake operations — it enforces two-phase execution, anti-hallucination rules, and read-only safety constraints.
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
Analyze and optimize Snowflake data warehouse — cost, performance, and schema.
Always discover databases, schemas, and tables before querying. Never assume object names.
#!/bin/bash
snow_cmd() {
snowsql -a "$SNOWFLAKE_ACCOUNT" \
-u "$SNOWFLAKE_USER" \
-p "$SNOWFLAKE_PASSWORD" \
--warehouse "$SNOWFLAKE_WAREHOUSE" \
-o output_format=tsv \
-o header=false \
-o timing=false \
-q "$1" 2>/dev/null
}
echo "=== Databases ==="
snow_cmd "SHOW DATABASES;" | awk '{print $2, $4, $5}' | head -20
echo ""
echo "=== Warehouses ==="
snow_cmd "SHOW WAREHOUSES;" | awk '{print $1, $2, $6}' | head -10
echo ""
echo "=== Current Role & Context ==="
snow_cmd "SELECT CURRENT_USER(), CURRENT_ROLE(), CURRENT_WAREHOUSE(), CURRENT_DATABASE(), CURRENT_SCHEMA();"
echo ""
echo "=== Schemas in Target Database ==="
snow_cmd "SHOW SCHEMAS IN DATABASE ${SNOWFLAKE_DATABASE};" | awk '{print $2, $3}' | head -20
After Phase 1: Only reference databases, schemas, and warehouses confirmed above.
#!/bin/bash
snow_query() {
snowsql -a "$SNOWFLAKE_ACCOUNT" \
-u "$SNOWFLAKE_USER" \
-p "$SNOWFLAKE_PASSWORD" \
--warehouse "${SNOWFLAKE_WAREHOUSE}" \
--database "${SNOWFLAKE_DATABASE:-}" \
--schema "${SNOWFLAKE_SCHEMA:-}" \
-o output_format=tsv \
-o header=false \
-o timing=false \
-q "$1" 2>/dev/null
}
SHOW TABLES IN SCHEMA <schema>DESCRIBE TABLE <table>SHOW WAREHOUSESLIMIT on all SELECT queries — never unbounded scans on production tablesSELECT * from large tables — always specify columns after inspecting schemaLIMIT — default cap 1000 rows for data queriesINFORMATION_SCHEMA for metadata queries over SHOW where possible (SQL-standard)#!/bin/bash
echo "=== Credit Consumption Last 30 Days ==="
snow_query "
SELECT
DATE_TRUNC('day', START_TIME) AS day,
WAREHOUSE_NAME,
ROUND(SUM(CREDITS_USED), 2) AS credits,
COUNT(*) AS query_count
FROM SNOWFLAKE.ACCOUNT_USAGE.WAREHOUSE_METERING_HISTORY
WHERE START_TIME >= DATEADD('day', -30, CURRENT_TIMESTAMP())
GROUP BY 1, 2
ORDER BY 1 DESC, 3 DESC
LIMIT 60;" | column -t
echo ""
echo "=== Top Warehouses by Cost (30 days) ==="
snow_query "
SELECT
WAREHOUSE_NAME,
ROUND(SUM(CREDITS_USED), 2) AS total_credits,
ROUND(SUM(CREDITS_USED_COMPUTE), 2) AS compute_credits,
ROUND(SUM(CREDITS_USED_CLOUD_SERVICES), 2) AS cloud_service_credits,
COUNT(*) AS sessions
FROM SNOWFLAKE.ACCOUNT_USAGE.WAREHOUSE_METERING_HISTORY
WHERE START_TIME >= DATEADD('day', -30, CURRENT_TIMESTAMP())
GROUP BY 1
ORDER BY 2 DESC
LIMIT 10;" | column -t
echo ""
echo "=== Storage Costs ==="
snow_query "
SELECT
DATE_TRUNC('month', USAGE_DATE) AS month,
ROUND(AVG(STORAGE_BYTES)/POWER(1024,3), 2) AS avg_storage_tb,
ROUND(AVG(STAGE_BYTES)/POWER(1024,3), 2) AS avg_stage_tb,
ROUND(AVG(FAILSAFE_BYTES)/POWER(1024,3), 2) AS avg_failsafe_tb
FROM SNOWFLAKE.ACCOUNT_USAGE.STORAGE_USAGE
WHERE USAGE_DATE >= DATEADD('month', -3, CURRENT_DATE())
GROUP BY 1
ORDER BY 1 DESC;" | column -t
#!/bin/bash
echo "=== Slowest Queries (last 24h) ==="
snow_query "
SELECT
QUERY_ID,
QUERY_TYPE,
WAREHOUSE_NAME,
USER_NAME,
ROUND(TOTAL_ELAPSED_TIME/1000, 1) AS elapsed_sec,
ROUND(BYTES_SCANNED/POWER(1024,3), 2) AS scanned_gb,
ROUND(CREDITS_USED_CLOUD_SERVICES, 4) AS credits,
LEFT(QUERY_TEXT, 80) AS query_preview
FROM SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY
WHERE START_TIME >= DATEADD('hour', -24, CURRENT_TIMESTAMP())
AND EXECUTION_STATUS = 'SUCCESS'
ORDER BY TOTAL_ELAPSED_TIME DESC
LIMIT 15;" | column -t
echo ""
echo "=== Queries with Full Table Scans ==="
snow_query "
SELECT
USER_NAME,
WAREHOUSE_NAME,
ROUND(PARTITIONS_SCANNED/NULLIF(PARTITIONS_TOTAL,0)*100, 1) AS pct_partitions_scanned,
ROUND(BYTES_SCANNED/POWER(1024,3), 2) AS scanned_gb,
ROUND(TOTAL_ELAPSED_TIME/1000, 1) AS elapsed_sec,
LEFT(QUERY_TEXT, 80) AS query_preview
FROM SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY
WHERE START_TIME >= DATEADD('hour', -24, CURRENT_TIMESTAMP())
AND PARTITIONS_TOTAL > 10
AND PARTITIONS_SCANNED/NULLIF(PARTITIONS_TOTAL,0) > 0.9
ORDER BY BYTES_SCANNED DESC
LIMIT 10;" | column -t
echo ""
echo "=== Failed Queries (last 24h) ==="
snow_query "
SELECT
ERROR_CODE,
ERROR_MESSAGE,
COUNT(*) AS count,
USER_NAME,
WAREHOUSE_NAME
FROM SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY
WHERE START_TIME >= DATEADD('hour', -24, CURRENT_TIMESTAMP())
AND EXECUTION_STATUS = 'FAIL'
GROUP BY 1, 2, 4, 5
ORDER BY 3 DESC
LIMIT 10;" | column -t
#!/bin/bash
echo "=== Warehouse Utilization ==="
snow_query "
SELECT
WAREHOUSE_NAME,
COUNT(*) AS total_queries,
ROUND(AVG(TOTAL_ELAPSED_TIME/1000), 1) AS avg_elapsed_sec,
ROUND(AVG(QUEUED_OVERLOAD_TIME/1000), 1) AS avg_queue_sec,
MAX(QUEUED_OVERLOAD_TIME/1000) AS max_queue_sec,
ROUND(SUM(BYTES_SCANNED)/POWER(1024,4), 2) AS total_tb_scanned
FROM SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY
WHERE START_TIME >= DATEADD('day', -7, CURRENT_TIMESTAMP())
GROUP BY 1
ORDER BY 2 DESC
LIMIT 10;" | column -t
echo ""
echo "=== Warehouse Auto-Suspend Opportunities ==="
snow_query "
SELECT
WAREHOUSE_NAME,
ROUND(SUM(CREDITS_USED), 2) AS credits_used,
COUNT(DISTINCT DATE_TRUNC('hour', START_TIME)) AS active_hours,
ROUND(SUM(CREDITS_USED)/NULLIF(COUNT(DISTINCT DATE_TRUNC('hour', START_TIME)), 0), 2) AS credits_per_hour
FROM SNOWFLAKE.ACCOUNT_USAGE.WAREHOUSE_METERING_HISTORY
WHERE START_TIME >= DATEADD('day', -30, CURRENT_TIMESTAMP())
GROUP BY 1
HAVING credits_per_hour < 0.1 -- Barely used warehouses
ORDER BY 2 DESC;" | column -t
#!/bin/bash
DB="${1:-$SNOWFLAKE_DATABASE}"
SCHEMA="${2:-PUBLIC}"
echo "=== Tables in $DB.$SCHEMA ==="
snow_query "
SELECT
TABLE_NAME,
ROW_COUNT,
ROUND(BYTES/POWER(1024,3), 3) AS data_gb,
CLUSTERING_KEY,
IS_TRANSIENT
FROM ${DB}.INFORMATION_SCHEMA.TABLES
WHERE TABLE_SCHEMA = '${SCHEMA}'
AND TABLE_TYPE = 'BASE TABLE'
ORDER BY BYTES DESC NULLS LAST
LIMIT 25;" | column -t
echo ""
echo "=== Tables Missing Clustering Keys (large tables) ==="
snow_query "
SELECT TABLE_NAME, ROW_COUNT, ROUND(BYTES/POWER(1024,3), 2) AS data_gb
FROM ${DB}.INFORMATION_SCHEMA.TABLES
WHERE TABLE_SCHEMA = '${SCHEMA}'
AND CLUSTERING_KEY IS NULL
AND BYTES > 10*POWER(1024,3) -- > 10GB
ORDER BY BYTES DESC
LIMIT 10;" | column -t
#!/bin/bash
DB="${1:-$SNOWFLAKE_DATABASE}"
TABLE="${2:?Table name required}"
SCHEMA="${3:-PUBLIC}"
echo "=== Table: $DB.$SCHEMA.$TABLE ==="
snow_query "DESCRIBE TABLE ${DB}.${SCHEMA}.${TABLE};" | head -20
echo ""
echo "=== Row Count ==="
snow_query "SELECT COUNT(*) FROM ${DB}.${SCHEMA}.${TABLE};"
echo ""
echo "=== Recent Changes (Time Travel — if enabled) ==="
snow_query "
SELECT
QUERY_ID,
QUERY_TYPE,
ROUND(ROWS_PRODUCED, 0) AS rows_affected,
START_TIME
FROM SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY
WHERE QUERY_TEXT ILIKE '%${TABLE}%'
AND QUERY_TYPE IN ('INSERT', 'UPDATE', 'DELETE', 'MERGE', 'COPY')
AND START_TIME >= DATEADD('day', -7, CURRENT_TIMESTAMP())
ORDER BY START_TIME DESC
LIMIT 10;" | column -t
#!/bin/bash
echo "=== Top Users by Query Count (30 days) ==="
snow_query "
SELECT
USER_NAME,
COUNT(*) AS query_count,
ROUND(SUM(TOTAL_ELAPSED_TIME/1000/3600), 1) AS total_hours,
ROUND(SUM(BYTES_SCANNED)/POWER(1024,4), 2) AS total_tb_scanned
FROM SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY
WHERE START_TIME >= DATEADD('day', -30, CURRENT_TIMESTAMP())
GROUP BY 1
ORDER BY 2 DESC
LIMIT 10;" | column -t
echo ""
echo "=== Login History (failed logins) ==="
snow_query "
SELECT
USER_NAME,
CLIENT_IP,
FIRST_AUTHENTICATION_FACTOR,
ERROR_MESSAGE,
EVENT_TIMESTAMP
FROM SNOWFLAKE.ACCOUNT_USAGE.LOGIN_HISTORY
WHERE IS_SUCCESS = 'NO'
AND EVENT_TIMESTAMP >= DATEADD('day', -7, CURRENT_TIMESTAMP())
ORDER BY EVENT_TIMESTAMP DESC
LIMIT 15;" | column -t
Present results as a structured report:
Analyzing Snowflake Report
══════════════════════════
Resources discovered: [count]
Resource Status Key Metric Issues
──────────────────────────────────────────────
[name] [ok/warn] [value] [findings]
Summary: [total] resources | [ok] healthy | [warn] warnings | [crit] critical
Action Items: [list of prioritized findings]
Target ≤50 lines of output. Use tables for multi-resource comparisons.
| Shortcut | Counter | Why |
|---|---|---|
| "I'll skip discovery and check known resources" | Always run Phase 1 discovery first | Resource names change, new resources appear — assumed names cause errors |
| "The user only asked for a quick check" | Follow the full discovery → analysis flow | Quick checks miss critical issues; structured analysis catches silent failures |
| "Default configuration is probably fine" | Audit configuration explicitly | Defaults often leave logging, security, and optimization features disabled |
| "Metrics aren't needed for this" | Always check relevant metrics when available | API/CLI responses show current state; metrics reveal trends and intermittent issues |
| "I don't have access to that" | Try the command and report the actual error | Assumed permission failures prevent useful investigation; actual errors are informative |
ACCOUNT_USAGE latency: Data in ACCOUNT_USAGE views has a 45-minute to 3-hour delay — for real-time use INFORMATION_SCHEMA (14-day retention, no delay)PARTITIONS_SCANNED null: Can be null for metadata-only queries — use NULLIF() in calculations"MyTable" vs MYTABLE are differentSHOW output format: SHOW commands output varies by client — prefer INFORMATION_SCHEMA for programmatic useSHOW WAREHOUSES status reflects recent queriesPARTITIONS_SCANNED/PARTITIONS_TOTAL ratio — key signal for missing clustering keysDATA_RETENTION_TIME_IN_DAYS before querying historical dataQUERY_HISTORY for high-frequency tiny queriesまだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Cloudflare GraphQL Analytics for zone traffic, firewall events, Workers metrics, and schema exploration. Use when querying Cloudflare analytics data or exploring the GraphQL API.
日本語の概要は準備中です。原文の説明を表示しています。
Use when working with Alloydb — google AlloyDB instance analysis, query insights, columnar engine optimization, maintenance windows, and cluster health.
日本語の概要は準備中です。原文の説明を表示しています。
Use when working with Aqua — aqua Security platform analysis. Covers container runtime protection, image assurance policies, compliance frameworks, vulnerability management, workload protection, and registry scanning. Use when analyzing container security posture, reviewing image compliance, investigating runtime alerts, or auditing security policies.
日本語の概要は準備中です。原文の説明を表示しています。
Use when working with Bigquery — google BigQuery job analysis, slot utilization, cost analysis, dataset management, and query optimization.
日本語の概要は準備中です。原文の説明を表示しています。
Use when working with Cassandra — apache Cassandra keyspace analysis, compaction strategies, repair status, nodetool operations, and cluster health monitoring.
日本語の概要は準備中です。原文の説明を表示しています。
Use when working with Checkov — checkov infrastructure-as-code security scanning. Covers Terraform, CloudFormation, Kubernetes, and Dockerfile scanning, policy management, custom checks, compliance frameworks, and suppression management. Use when scanning IaC for security misconfigurations, evaluating compliance, managing custom policies, or reviewing scan results.
日本語の概要は準備中です。原文の説明を表示しています。