Notebook

You are helping the user create a Jupyter notebook that uses StackQL to query cloud and SaaS resources via the pystackql magic commands.

Input: $@

Follow these steps in order.

Step 1 - Understand the requirements

Parse the input to determine:

• Which provider(s) and resources are involved
• What the user wants to query or analyze
• Whether --server flag is present (use server mode instead of local binary)

If the input is vague, ask:

• Which cloud provider? (google, aws, azure, github, etc.)
• What resources or data do you want to explore?
• Any specific analysis or visualization goals?

Step 2 - Discover resource schemas

Check if StackQL is installed and the provider is pulled:

command -v stackql
stackql exec "SHOW PROVIDERS;" --output json

If needed, pull the provider:

stackql exec "REGISTRY PULL <provider>;"

Get the resource schema to inform the notebook queries:

stackql exec "SHOW SERVICES IN <provider>;" --output json
stackql exec "SHOW RESOURCES IN <provider>.<service>;" --output json
stackql exec "DESCRIBE <provider>.<service>.<resource>;" --output json
stackql exec "SHOW METHODS IN <provider>.<service>.<resource>;" --output json

Use this to understand what fields are available and what WHERE clause parameters are required.

Step 3 - Determine the notebook path

Ask the user where to save the notebook, or use a sensible default based on the topic:

<descriptive-name>.ipynb

Step 4 - Build the notebook

Create the notebook with cells following this structure. Use the NotebookEdit tool to create and populate the notebook.

Cell conventions

Follow these rules for all notebook cells:

• One heading per cell, placed at the top
• No horizontal rules (---, ***, ___) or <hr/> tags
• Any cell with <div>, <link>, <script> must use %md-sandbox type
• Inline styles only in %md-sandbox cells (no classes or <style> elements)
• Use spacing and headings to separate sections, not horizontal rules

Cell 1 - Title (markdown)

# <Descriptive Notebook Title>

<Brief description of what this notebook does.>

Cell 2 - Setup (code)

For local mode (default):

%load_ext pystackql.magic

For server mode (--server flag):

%load_ext pystackql.magics

Cell 3 - Pull providers (code)

%stackql registry pull <provider>

One line per provider if multiple are needed.

Cell 4 - Variables (code)

Define Python variables for parameterized queries:

project = "your-project-id"
region = "us-central1"

Include all required WHERE clause parameters discovered in Step 2. Use sensible placeholder values and add comments telling the user to update them.

Cell 5+ - Query sections

For each query/analysis, create a pair of cells:

Markdown cell with a section heading:

## <Section Title>

<Brief description of what this query does.>

Code cell with the StackQL query:

For single-line queries:

%stackql SELECT name, status FROM <provider>.<service>.<resource> WHERE <required_params>

For multi-line queries:

%%stackql
SELECT
    name,
    status,
    <other_fields>
FROM <provider>.<service>.<resource>
WHERE <required_param> = '$variable'
ORDER BY name

Visualization cells (optional)

Where results are suitable for visualization, add a code cell after the query:

Simple bar chart:

stackql_df.plot(kind='bar', x='name', y='count', title='<Chart Title>');

Custom matplotlib:

import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(10, 6))
ax.bar(stackql_df['<x_col>'], stackql_df['<y_col>'])
ax.set_xlabel('<X Label>')
ax.set_ylabel('<Y Label>')
ax.set_title('<Chart Title>')
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
plt.show()

Final cell - Summary/next steps (markdown)

## Next Steps

- Modify the queries above to explore different resources
- Use `/stackql-skills:notebook-cell` to add more queries
- Use `/stackql-skills:query` for ad-hoc queries outside the notebook

Step 5 - Key patterns to follow

Variable substitution

Use $variable in queries where the value comes from a Python variable:

WHERE project = '$project' AND zone = '$zone'

Dollar sign escaping

When the query needs a literal $ (e.g., JSON path expressions), use $$:

JSON_EXTRACT(Properties, '$$.BucketName')

Result access

• The last query result is always stored in stackql_df (pandas DataFrame)
• The _ variable also references the last cell output (standard IPython)
• Use %%stackql --no-display to run a query without displaying the result
• Use %%stackql --csv-download to add a CSV download button

Registry operations

Providers must be pulled before querying. Use line magic:

%stackql registry pull <provider>

Mutations

For INSERT, DELETE, EXEC, and REGISTRY operations, the magic command routes these automatically to the correct execution method.

Step 6 - Report

Tell the user:

• The notebook path
• What provider(s) and resources are covered
• Remind them to update the variable values (project IDs, regions, etc.)
• How to run the notebook: jupyter notebook <path> or open in VS Code
• Note that pystackql must be installed: pip install pystackql