turbo-builder

Original：🇺🇸 English

Translated

Build and deploy new Goldsky Turbo pipelines from scratch. Triggers on: 'build a pipeline', 'index X on Y chain', 'set up a pipeline', 'track transfers to postgres', or any request describing data to move from a chain/contract to a destination (postgres, clickhouse, kafka, s3, webhook). Covers the full workflow: requirements → dataset selection → YAML generation → validation → deploy. Not for debugging (use /turbo-doctor) or syntax lookups (use /turbo-pipelines).

2installs

Sourcegoldsky-io/goldsky-agent

Added on2026-04-02

NPX Install

npx skill4agent add goldsky-io/goldsky-agent turbo-builder

SKILL.md Content

View Translation Comparison →

Pipeline Builder

Boundaries

Build NEW pipelines. Do not diagnose broken pipelines — that belongs to
```
/turbo-doctor
```
.
Do not serve as a YAML reference. If the user only needs to look up a field or syntax, use the
```
/turbo-pipelines
```
skill instead.
For dataset lookups, use
```
/datasets
```
.

Walk the user through building a complete pipeline from scratch, step by step. Generate a valid YAML configuration, validate it, and deploy it.

Builder Workflow

Step 1: Verify Authentication

Run

goldsky project list 2>&1

to check login status.

If logged in: Note the current project and continue.
If not logged in: Use the
```
/auth-setup
```
skill for guidance.

Step 2: Understand the Goal

Ask the user what they want to index. Good questions:

What blockchain/chain? (Ethereum, Base, Polygon, Solana, etc.)
What data? (transfers, swaps, events from a specific contract, all transactions, etc.)
Where should the data go? (PostgreSQL, ClickHouse, Kafka, S3, etc.)
Do they need transforms? (filtering, aggregation, enrichment)
One-time backfill or continuous streaming?

If the user already described their goal, extract answers from their description.

Step 3: Choose the Dataset

Use the

/datasets

skill to find the right dataset.

Key points:

Common datasets:

<chain>.decoded_logs

,

<chain>.raw_transactions

,

<chain>.erc20_transfers

,

<chain>.raw_traces

For decoded contract events, use
```
<chain>.decoded_logs
```
with a filter on
```
address
```
and
```
topic0
```

For Solana: use

solana.transactions

,

solana.token_transfers

, etc.

Present the dataset choice to the user for confirmation.

Step 4: Configure the Source

Build the source section of the YAML:

yaml

sources:
  my_source:
    type: dataset
    dataset_name: <chain>.<dataset>
    version: 1.0.0
    start_at: earliest  # or a specific block number

Ask about:

Start block:
```
earliest
```
(from genesis),
```
latest
```
(from now), or a specific block number
End block: Only for job-mode/backfill pipelines. Omit for streaming.
Source-level filter: Optional filter to reduce data at the source (e.g., specific contract address)

Step 5: Configure Transforms (if needed)

If the user needs transforms, use the

/turbo-transforms

skill to help:

SQL transforms — filter, aggregate, join, or reshape data using DataFusion SQL
TypeScript transforms — custom logic, external API calls, complex processing
Dynamic tables — join with a PostgreSQL table or in-memory allowlist

Build the transforms section:

yaml

transforms:
  my_transform:
    type: sql
    primary_key: id
    sql: |
      SELECT * FROM my_source
      WHERE <conditions>

Step 6: Configure the Sink

Ask where the data should go. Use the

/turbo-pipelines

skill for sink configuration:

Sink	Key config
PostgreSQL	`secret_name` , `schema` , `table` , `primary_key`
ClickHouse	`secret_name` , `table` , `order_by`
Kafka	`secret_name` , `topic`
S3	`bucket` , `region` , `prefix` , `format`
Webhook	`url` , `format`

For sinks requiring

secret_name

, check if the secret exists:

bash

goldsky secret list

If it doesn't exist, help create it using the

/secrets

skill.

Step 7: Choose Mode

Use the

/turbo-pipelines

skill for guidance:

Streaming (default) — continuous processing, no
```
end_block
```
, runs indefinitely
Job mode — one-time backfill, set
```
job: true
```
and
```
end_block
```

Step 8: Generate, Validate, and Present

Assemble the complete pipeline YAML. Use a descriptive name following the convention:

<chain>-<data>-<sink>

(e.g.,

base-erc20-transfers-postgres

).

Write the YAML file to disk (e.g.,
```
<pipeline-name>.yaml
```
).
Run validation BEFORE showing the YAML to the user:

bash

goldsky turbo validate -f <pipeline-name>.yaml

If validation fails, fix the issues and re-validate. Do NOT present the YAML until validation passes. Common fixes:
- Missing
```
version
```
  field on dataset source
- Invalid dataset name (check chain prefix)
- Missing
```
secret_name
```
  for database sinks
- SQL syntax errors in transforms
Once validation passes, present the full YAML to the user for review.

Step 9: Deploy

After user confirms the YAML looks good:

bash

goldsky turbo apply <pipeline-name>.yaml

Step 10: Verify

After deployment:

bash

goldsky turbo list

Suggest running inspect to verify data flow:

bash

goldsky turbo inspect <pipeline-name>

Present a summary:

## Pipeline Deployed

**Name:** [name]
**Chain:** [chain]
**Dataset:** [dataset]
**Sink:** [sink type]
**Mode:** [streaming/job]

**Next steps:**
- Monitor with `goldsky turbo inspect <name>`
- Check logs with `goldsky turbo logs <name>`
- Use /turbo-doctor if you run into issues

Important Rules

Always validate before presenting complete YAML to the user. Never show unvalidated complete pipeline YAML.
Always validate before deploying.
Always show the user the complete YAML before deploying.
For job-mode pipelines, remind the user they auto-cleanup ~1hr after completion.
Use
```
blackhole
```
sink for testing pipelines without writing to a real destination.
If the user wants to modify an existing pipeline, check if it's streaming (update in place) or job-mode (must delete first).
Default to
```
start_at: earliest
```
unless the user specifies otherwise.
Always include
```
version: 1.0.0
```
on dataset sources.

/turbo-pipelines
— YAML configuration and architecture reference
/turbo-doctor
— Diagnose and fix pipeline issues
/turbo-operations
— Lifecycle commands and monitoring reference
/turbo-transforms
— SQL and TypeScript transform reference
/datasets
— Dataset names and chain prefixes
/secrets
— Sink credential management

turbo-builder

NPX Install

Tags

SKILL.md Content

Pipeline Builder

Boundaries

Builder Workflow

Step 1: Verify Authentication

Step 2: Understand the Goal

Step 3: Choose the Dataset

Step 4: Configure the Source

Step 5: Configure Transforms (if needed)

Step 6: Configure the Sink

Step 7: Choose Mode

Step 8: Generate, Validate, and Present

Step 9: Deploy

Step 10: Verify

Important Rules

Related