docx-templates-database-integration
Sub-skill of docx-templates: Database Integration.
Best use case
docx-templates-database-integration is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of docx-templates: Database Integration.
Teams using docx-templates-database-integration should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/database-integration/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How docx-templates-database-integration Compares
| Feature / Agent | docx-templates-database-integration | 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?
Sub-skill of docx-templates: Database Integration.
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
# Database Integration
## Database Integration
```python
"""
Generate documents from database queries.
"""
from docxtpl import DocxTemplate
from typing import List, Dict
import sqlite3
from sqlalchemy import create_engine, text
import pandas as pd
def generate_from_database(
template_path: str,
output_dir: str,
db_connection: str,
query: str,
filename_field: str = "id"
) -> List[str]:
"""
Generate documents from database query results.
Args:
template_path: Path to template
output_dir: Output directory
db_connection: Database connection string
query: SQL query to fetch data
filename_field: Field for output filename
Returns:
List of generated file paths
"""
# Connect and fetch data
engine = create_engine(db_connection)
with engine.connect() as conn:
result = conn.execute(text(query))
records = [dict(row._mapping) for row in result]
# Generate documents
return mail_merge_from_list(template_path, output_dir, records, filename_field)
def generate_customer_reports(
template_path: str,
output_dir: str,
db_path: str
) -> Dict:
"""Generate customer reports from SQLite database."""
conn = sqlite3.connect(db_path)
conn.row_factory = sqlite3.Row
# Fetch customers with their orders
query = """
SELECT
c.id,
c.name,
c.email,
c.address,
COUNT(o.id) as order_count,
SUM(o.total) as total_spent
FROM customers c
LEFT JOIN orders o ON c.id = o.customer_id
GROUP BY c.id
"""
cursor = conn.cursor()
cursor.execute(query)
records = []
for row in cursor.fetchall():
record = dict(row)
# Fetch order details for each customer
cursor.execute(
"SELECT * FROM orders WHERE customer_id = ?",
(record["id"],)
)
record["orders"] = [dict(r) for r in cursor.fetchall()]
records.append(record)
conn.close()
# Generate reports
generated = mail_merge_from_list(
template_path,
output_dir,
records,
filename_field="id"
)
return {
"total_customers": len(records),
"generated_reports": len(generated),
"files": generated
}
```Related Skills
library-evaluation-integration
Create evaluation scripts and integration tests for Python scientific libraries in the digitalmodel package. Follows the established pattern from fluids, ht, meshio, sectionproperties, and pygmt evaluations.
clean-worktree-integration-from-dirty-main
Land validated issue work from isolated worktrees when the main checkout is dirty by creating a fresh integration worktree, cherry-picking only implementation commits, re-running combined validation, and preparing push/closeout artifacts.
hermes-ecosystem-integration
Wire Hermes into workspace-hub ecosystem — multi-repo skills, config sync, session export to learning pipeline, memory cross-pollination, skill patch tracking, and cross-machine health checks.
api-integration
Integrate offshore engineering software APIs with mock testing for OrcaFlex, AQWA, and WAMIT
docx
Comprehensive Word document toolkit for reading, creating, and editing .docx files. Supports text extraction, document creation with python-docx, and tracked changes via redlining workflow. Use for legal, academic, or professional document manipulation.
llm-wiki-roadmap-integration
Integrate repo-ecosystem work into an existing llm-wiki / knowledge-roadmap issue without creating duplicate GitHub issues.
mkdocs-integration-with-python-package
Sub-skill of mkdocs: Integration with Python Package (+2).
improve-integration
Sub-skill of improve: Integration.
clean-code-pre-commit-integration
Sub-skill of clean-code: Pre-commit Integration.
agent-teams-work-queue-integration
Sub-skill of agent-teams: Work Queue Integration.
vscode-extensions-git-workflow-integration
Sub-skill of vscode-extensions: Git Workflow Integration (+1).
raycast-alfred-project-switcher-integration
Sub-skill of raycast-alfred: Project Switcher Integration.