spanner-data

Usage

All scripts can be executed using Node.js. Replace <param_name> and <param_value> with actual values.

Bash: node <skill_dir>/scripts/<script_name>.js '{"<param_name>": "<param_value>"}'

PowerShell: node <skill_dir>/scripts/<script_name>.js '{\"<param_name>\": \"<param_value>\"}'

Note: The scripts automatically load the environment variables from various .env files. Do not ask the user to set vars unless skill executions fails due to env var absence.

Scripts

execute_sql

Use this tool to execute DML SQL. Please use the googlesql interface for Spanner.

Parameters

Name	Type	Description	Required	Default
sql	string	The sql to execute.	Yes

---

execute_sql_dql

Use this tool to execute DQL SQL. Please use the googlesql interface for Spanner.

Parameters

Name	Type	Description	Required	Default
sql	string	The sql to execute.	Yes

---

list_graphs

Lists detailed graph schema information (node tables, edge tables, labels and property declarations) as JSON for user-created graphs. Filters by a comma-separated list of graph names. If names are omitted, lists all graphs. The output can be 'simple' (graph names only) or 'detailed' (full schema).

Parameters

Name	Type	Description	Required	Default
graph_names	string	Optional: A comma-separated list of graph names. If empty, details for all graphs in user-accessible schemas will be listed.	No	``
output_format	string	Optional: Use 'simple' to return graph names only or use 'detailed' to return the full information schema.	No	detailed

---

list_tables

Lists detailed schema information (object type, columns, constraints, indexes) as JSON for user-created tables (ordinary or partitioned). Filters by a comma-separated list of names. If names are omitted, lists all tables in user schemas. The output can be 'simple' (table names only) or 'detailed' (full schema).

Parameters

Name	Type	Description	Required	Default
table_names	string	Optional: A comma-separated list of table names. If empty, details for all tables in user-accessible schemas will be listed.	No	``
output_format	string	Optional: Use 'simple' to return table names only or use 'detailed' to return the full information schema.	No	detailed

---

Anti-Patterns

• NEVER execute raw DML/DDL operations on production database instances without verification.
• NEVER ignore connection limits and connection pool starvation indicators.

6) Capture Knowledge

Memory Sync

After completing key technical findings, architectural decisions, code refactorings, or risk assessments, you MUST trigger the local memory capture.

1. Save the final summary or artifact as a Markdown file in the project directory.
2. Invoke the capture script:

   python3 ~/memory_system/capture_knowledge.py <file_path>
   

3. This ensures that new learnings, policies, and technical snippets are automatically routed to the correct local storage (OKF or ChromaDB).