wap-ingestion
Ingest data from S3 into bauplan using the Write-Audit-Publish pattern for safe data loading. Use when loading new data from S3, performing safe data ingestion, or when the user mentions WAP, data ingestion, importing parquet/csv/jsonl files, or needs to safely load data with quality checks.
Best use case
wap-ingestion is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. Ingest data from S3 into bauplan using the Write-Audit-Publish pattern for safe data loading. Use when loading new data from S3, performing safe data ingestion, or when the user mentions WAP, data ingestion, importing parquet/csv/jsonl files, or needs to safely load data with quality checks.
Ingest data from S3 into bauplan using the Write-Audit-Publish pattern for safe data loading. Use when loading new data from S3, performing safe data ingestion, or when the user mentions WAP, data ingestion, importing parquet/csv/jsonl files, or needs to safely load data with quality checks.
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "wap-ingestion" skill to help with this workflow task. Context: Ingest data from S3 into bauplan using the Write-Audit-Publish pattern for safe data loading. Use when loading new data from S3, performing safe data ingestion, or when the user mentions WAP, data ingestion, importing parquet/csv/jsonl files, or needs to safely load data with quality checks.
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/wap-ingestion/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How wap-ingestion Compares
| Feature / Agent | wap-ingestion | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Ingest data from S3 into bauplan using the Write-Audit-Publish pattern for safe data loading. Use when loading new data from S3, performing safe data ingestion, or when the user mentions WAP, data ingestion, importing parquet/csv/jsonl files, or needs to safely load data with quality checks.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Write-Audit-Publish (WAP) Pattern
Implement WAP by writing a Python script using the `bauplan` SDK. Do NOT use CLI commands.
**The three steps**: Write (ingest to temp branch) → Audit (quality checks) → Publish (merge to main)
**Branch safety**: All operations happen on a temporary branch, NEVER on `main`. By default, branches are kept open for inspection after success or failure.
**Atomic multi-table operations**: `merge_branch` is atomic. You can create or modify multiple tables on a branch, and when you merge, either all changes apply to main or none do. This enables safe multi-table ingestion workflows.
## Required User Input
Before writing the WAP script, you MUST ask the user for the following parameters:
1. **S3 path** (required): The S3 URI pattern for the source data (e.g., `s3://bucket/path/*.parquet`)
2. **Table name** (required): The name for the target table
3. **On success behavior** (optional):
- `inspect` (default): Keep the branch open for user inspection before merging
- `merge`: Automatically merge to main and delete the branch
4. **On failure behavior** (optional):
- `keep` (default): Leave the branch open for inspection/debugging
- `delete`: Delete the failed branch
## WAP Script Template
See [wap_template.py](wap_template.py) for the complete template. Minimal usage:
```python
from wap_template import wap_ingest
branch, success = wap_ingest(
table_name="orders",
s3_path="s3://my-bucket/data/*.parquet",
namespace="bauplan",
on_success="inspect", # or "merge"
on_failure="keep" # or "delete"
)
```
## Key SDK Methods
| Method | Description |
|--------|-------------|
| `bauplan.Client()` | Initialize the bauplan client |
| `client.info()` | Get client info; access username via `.user.username` |
| `client.create_branch(name, from_ref="main")` | Create a new branch from specified ref |
| `client.has_branch(name)` | Check if branch exists |
| `client.delete_branch(name)` | Delete a branch |
| `client.create_table(table, search_uri, ...)` | Create table with schema inferred from S3 |
| `client.import_data(table, search_uri, ...)` | Import data from S3 into table |
| `client.query(query, ref)` | Run SQL query, returns PyArrow Table |
| `client.merge_branch(source_ref, into_branch)` | Merge branch into target |
| `client.has_table(table, ref, namespace)` | Check if table exists on branch |
> **SDK Reference**: For detailed method signatures, check https://docs.bauplanlabs.com/reference/bauplan
## Workflow Checklist
Copy and track progress:
```
WAP Progress:
- [ ] Ask user for: S3 path, table name, on_success, on_failure
- [ ] Write script using wap_template.py
- [ ] Run script: python wap_script.py
- [ ] Verify output shows row count > 0
- [ ] If on_success="inspect": confirm branch ready for review
- [ ] If on_success="merge": confirm merge to main succeeded
```
## Example Output
**Successful run (on_success="inspect")**:
```
$ python wap_script.py
Imported 15234 rows
WAP completed successfully. Branch 'alice.wap_orders_1704067200' ready for inspection.
To merge manually: client.merge_branch(source_ref='alice.wap_orders_1704067200', into_branch='main')
```
**Successful run (on_success="merge")**:
```
$ python wap_script.py
Imported 15234 rows
Successfully published orders to main
Cleaned up branch: alice.wap_orders_1704067200
```
**Failed run (on_failure="keep")**:
```
$ python wap_script.py
WAP failed: No data was imported
Branch 'alice.wap_orders_1704067200' preserved for inspection/debugging.
```
## WAP on Existing Tables
To append data to an existing table, skip `create_table` and only call `import_data`:
```python
# Table already exists on main - just import new data
client.import_data(
table=table_name,
search_uri=s3_path,
namespace=namespace,
branch=branch_name
)
```
This appends rows to the existing table schema. The audit and publish phases remain the same: the new rows are automatically sandboxed on the branch until merged.
## CLI Merge After Inspection
When `on_success="inspect"` (default), the branch is left open for user review. If the user asks to merge after inspecting the data, use the CLI:
```bash
# 1. Checkout to main first (required before merging)
bauplan checkout main
# 2. Merge the WAP branch into main
bauplan branch merge <username>.wap_<table_name>_<timestamp>
# 3. Optionally delete the branch after successful merge
bauplan branch rm <username>.wap_<table_name>_<timestamp>
```
> **Note**: You must be on `main` to run `bauplan branch merge`. The branch name is printed by the WAP script upon completion.Related Skills
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