Agent skill
check-prod
Query the production Vercel database via the public API to investigate data issues
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/check-prod
SKILL.md
Check Production Data
Query the production Sai Explorer API to investigate data in the Vercel Postgres database.
Base URL: https://sai-explorer.vercel.app
Use curl -s to fetch JSON, then pipe to python3 -c for parsing/filtering.
User Request
$ARGUMENTS
Available Endpoints
| Endpoint | Params | Description |
|---|---|---|
/api/trades |
network, limit, offset |
All trades (default limit 1000) |
/api/user-trades |
network, address |
Trades for a specific user |
/api/user-stats |
network, address |
Aggregate stats for a user (volume, PnL, trade count) |
/api/user-deposits |
network, address |
Deposits for a specific user |
/api/user-withdraws |
network, address |
Withdraws for a specific user |
/api/stats |
network |
Global platform stats |
/api/volume |
network |
Volume leaderboard |
/api/deposits |
network |
All deposits |
/api/withdraws |
network |
All withdraws |
/api/insights |
network |
PnL insights and analytics |
/api/chart-data |
network, type |
Chart time series data |
/api/markets |
network |
Market info |
/api/collateral |
network |
Collateral breakdown |
/api/tvl-breakdown |
network |
TVL by vault |
Default network=mainnet for all endpoints.
Key Data Mappings
- Micro-units:
collateralAmount,realizedPnlCollateral,openCollateralAmountare in micro-units (divide by 1,000,000) - USD conversion:
microAmount / 1e6 * collateralPrice - Collateral types: USDC (price ~$1.0) or stNIBI (price ~$0.005)
- Trade fields: Nested under
trade.perpBorrowing.collateralToken.symbolandtrade.perpBorrowing.baseToken.symbol - Address formats: Users have both Bech32 (
nibi1...) and EVM (0x...) addresses
Instructions
- Determine which endpoint(s) to query based on the user's request
- Fetch data with
curl -sand parse withpython3 -c - Present findings in a clear, formatted table or summary
- If investigating data quality, check for NULL values, unexpected ranges, or inconsistencies
- For large datasets, use
limitandoffsetto page through results
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