semantic-search-setup-status-monitoring
Sub-skill of semantic-search-setup: Status Monitoring.
Best use case
semantic-search-setup-status-monitoring is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of semantic-search-setup: Status Monitoring.
Teams using semantic-search-setup-status-monitoring 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/status-monitoring/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How semantic-search-setup-status-monitoring Compares
| Feature / Agent | semantic-search-setup-status-monitoring | 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 semantic-search-setup: Status Monitoring.
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
# Status Monitoring
## Status Monitoring
```python
def get_embedding_status(db_path):
conn = sqlite3.connect(db_path, timeout=30)
cursor = conn.cursor()
cursor.execute('SELECT COUNT(*) FROM chunks')
total_chunks = cursor.fetchone()[0]
cursor.execute('SELECT COUNT(*) FROM embeddings')
embedded = cursor.fetchone()[0]
conn.close()
return {
'total': total_chunks,
'embedded': embedded,
'remaining': total_chunks - embedded,
'progress': f"{100*embedded/total_chunks:.1f}%"
}
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