Cloudflare GraphQL Analytics for zone traffic, firewall events, Workers metrics, and schema exploration. Use when querying Cloudflare analytics data or exploring the GraphQL API.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when working with Great Expectations — great Expectations data quality framework analysis. Covers expectation suite management, validation result review, data docs generation, checkpoint execution, datasource configuration, and batch analysis. Use when reviewing data quality results, managing expectation suites, investigating validation failures, or auditing data quality configurations.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Analyze and manage Great Expectations data quality suites, validations, and checkpoints.
Always discover project structure and available suites before querying specific validation results.
#!/bin/bash
GE_DIR="${GE_PROJECT_DIR:-.}"
echo "=== Great Expectations Project Structure ==="
ls -la "${GE_DIR}/great_expectations/" 2>/dev/null || echo "No GE directory found at ${GE_DIR}"
echo ""
echo "=== Expectation Suites ==="
ls "${GE_DIR}/great_expectations/expectations/" 2>/dev/null | head -20
echo ""
echo "=== Checkpoints ==="
ls "${GE_DIR}/great_expectations/checkpoints/" 2>/dev/null | head -20
echo ""
echo "=== Datasources (from config) ==="
python3 -c "
import yaml
with open('${GE_DIR}/great_expectations/great_expectations.yml') as f:
config = yaml.safe_load(f)
for name, ds in config.get('datasources', {}).items():
print(f'{name}\t{ds.get(\"class_name\", \"?\")}\t{ds.get(\"execution_engine\", {}).get(\"class_name\", \"?\")}')
" 2>/dev/null | column -t
echo ""
echo "=== Validation Results (recent) ==="
ls -lt "${GE_DIR}/great_expectations/uncommitted/validations/" 2>/dev/null | head -10
#!/bin/bash
GE_DIR="${GE_PROJECT_DIR:-.}"
GE_BASE="${GE_DIR}/great_expectations"
# Run GE CLI command
ge_cmd() {
cd "${GE_DIR}" && great_expectations "$@" 2>/dev/null
}
# Parse validation result JSON
ge_parse_validation() {
local result_file="$1"
jq '{
suite: .meta.expectation_suite_name,
success: .success,
evaluated: (.results | length),
passed: ([.results[] | select(.success == true)] | length),
failed: ([.results[] | select(.success == false)] | length),
run_time: .meta.run_id.run_time
}' "$result_file"
}
# List all expectation suites
ge_list_suites() {
find "${GE_BASE}/expectations" -name "*.json" -type f 2>/dev/null | \
sed "s|${GE_BASE}/expectations/||;s|\.json$||;s|/|.|g"
}
#!/bin/bash
echo "=== All Expectation Suites ==="
ge_list_suites | while read suite; do
SUITE_FILE="${GE_BASE}/expectations/$(echo $suite | tr '.' '/').json"
COUNT=$(jq '.expectations | length' "$SUITE_FILE" 2>/dev/null || echo 0)
echo -e "${suite}\t${COUNT} expectations"
done | column -t
echo ""
echo "=== Suite Detail ==="
SUITE="${1:-}"
if [ -n "$SUITE" ]; then
SUITE_FILE="${GE_BASE}/expectations/$(echo $SUITE | tr '.' '/').json"
jq -r '.expectations[] | "\(.expectation_type)\t\(.kwargs | to_entries | map("\(.key)=\(.value)") | join(", ") | .[0:60])"' \
"$SUITE_FILE" | column -t | head -20
fi
#!/bin/bash
echo "=== Recent Validation Results ==="
find "${GE_BASE}/uncommitted/validations" -name "*.json" -type f 2>/dev/null | \
sort -r | head -10 | while read vfile; do
jq -r '"Suite: \(.meta.expectation_suite_name)\tSuccess: \(.success)\tPassed: \([.results[] | select(.success)] | length)/\(.results | length)\tTime: \(.meta.run_id.run_time[0:16] // "?")"' "$vfile" 2>/dev/null
done | column -t
echo ""
echo "=== Failed Validations ==="
find "${GE_BASE}/uncommitted/validations" -name "*.json" -type f 2>/dev/null | \
sort -r | head -20 | while read vfile; do
FAILED=$(jq 'select(.success == false) | .meta.expectation_suite_name' "$vfile" 2>/dev/null)
if [ -n "$FAILED" ]; then
jq -r '"\(.meta.expectation_suite_name)\t\(.meta.run_id.run_time[0:16] // "?")\t\([.results[] | select(.success == false)] | length) failed"' "$vfile"
fi
done | column -t
#!/bin/bash
VALIDATION_FILE="${1:?Validation result file path required}"
echo "=== Validation Summary ==="
ge_parse_validation "$VALIDATION_FILE"
echo ""
echo "=== Failed Expectations ==="
jq -r '
.results[] | select(.success == false) |
"\(.expectation_config.expectation_type)\t\(.expectation_config.kwargs.column // "table-level")\tObserved: \(.result.observed_value // "N/A")"
' "$VALIDATION_FILE" | column -t | head -20
echo ""
echo "=== Unexpected Values (sample) ==="
jq -r '
.results[] | select(.success == false) |
select(.result.partial_unexpected_list != null) |
"\(.expectation_config.kwargs.column): \(.result.partial_unexpected_list[:5] | join(", "))"
' "$VALIDATION_FILE" | head -10
#!/bin/bash
CHECKPOINT="${1:?Checkpoint name required}"
DRY_RUN="${2:-true}"
if [ "$DRY_RUN" = "true" ]; then
echo "=== Checkpoint Config: $CHECKPOINT ==="
CKPT_FILE="${GE_BASE}/checkpoints/${CHECKPOINT}.yml"
python3 -c "
import yaml
with open('${CKPT_FILE}') as f:
config = yaml.safe_load(f)
print(f'Class: {config.get(\"class_name\", \"?\")}')
for vc in config.get('validations', []):
print(f' Suite: {vc.get(\"expectation_suite_name\", \"?\")}\tBatch: {vc.get(\"batch_request\", {}).get(\"datasource_name\", \"?\")}')
" 2>/dev/null
echo ""
echo "To execute, call with dry_run=false"
else
echo "=== Running Checkpoint: $CHECKPOINT ==="
cd "${GE_DIR}" && great_expectations checkpoint run "$CHECKPOINT" 2>&1 | tail -20
fi
#!/bin/bash
echo "=== Data Docs Sites ==="
python3 -c "
import yaml
with open('${GE_BASE}/great_expectations.yml') as f:
config = yaml.safe_load(f)
for name, site in config.get('data_docs_sites', {}).items():
store = site.get('store_backend', {})
print(f'{name}\t{store.get(\"class_name\", \"?\")}\t{store.get(\"base_directory\", store.get(\"bucket\", \"?\"))}')
" 2>/dev/null | column -t
echo ""
echo "=== Build Data Docs ==="
echo "Run: great_expectations docs build --no-view"
echo ""
echo "=== Expectation Coverage ==="
ge_list_suites | while read suite; do
SUITE_FILE="${GE_BASE}/expectations/$(echo $suite | tr '.' '/').json"
TYPES=$(jq -r '[.expectations[].expectation_type] | unique | join(", ")' "$SUITE_FILE" 2>/dev/null)
echo -e "${suite}\t${TYPES}"
done | column -t | head -15
Present results as a structured report:
Analyzing Great Expectations 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.
--help output.| 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 |
great_expectations/ directorymy_suite.orders maps to expectations/my_suite/orders.jsonuncommitted/validations/ are gitignored by default — use a configured validations store for persistenceconfig_version in great_expectations.yml아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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Cloudflare GraphQL Analytics for zone traffic, firewall events, Workers metrics, and schema exploration. Use when querying Cloudflare analytics data or exploring the GraphQL API.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when working with Alloydb — google AlloyDB instance analysis, query insights, columnar engine optimization, maintenance windows, and cluster health.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when working with Bigquery — google BigQuery job analysis, slot utilization, cost analysis, dataset management, and query optimization.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when working with Cassandra — apache Cassandra keyspace analysis, compaction strategies, repair status, nodetool operations, and cluster health monitoring.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.