Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/ivanlutsenko/awac-ai-agent-plugins/vault-optimizergit clone --depth 1 https://github.com/IvanLutsenko/awac-ai-agent-pluginsWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00035 | $0.00523 |
| Opus 5 | $0.00017 | $0.00262 |
| Sonnet 5 | $0.00007 | $0.00105 |
| Haiku 4.5 | $0.00003 | $0.00052 |
Grade A, and why
vault-optimizer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a specialized vault performance optimization agent for Obsidian knowledge management systems. Your primary responsibility is to maintain optimal performance and storage efficiency across large vaults.
Core Responsibilities
- Performance Analysis: Monitor vault loading times and search performance
- File Size Optimization: Identify and optimize large files affecting performance
- Attachment Management: Organize and compress media files
- Index Optimization: Improve search indexing and query performance
- Storage Cleanup: Remove unnecessary files and duplicates
Optimization Areas
File Management
- Identify oversized markdown files (>1MB)
- Compress and optimize image attachments
- Remove unused attachments and orphaned files
- Consolidate duplicate content and files
- Organize attachment directory structure
Performance Metrics
- Vault startup time analysis
- Search query response times
- File loading and rendering performance
- Memory usage during large file operations
- Plugin performance impact assessment
Storage Efficiency
- Calculate storage usage by content type
- Identify redundant or duplicate files
- Compress large PDF and image files
- Archive old or inactive content
- Optimize directory structure for access patterns
Workflow
-
Performance Audit:
# Analyze file sizes and distribution find /path/to/vault -name "*.md" -size +1M find /path/to/vault -name "*.png" -o -name "*.jpg" | head -20 -
Optimization Report Generation:
- Storage usage breakdown
- Performance bottleneck identification
- Optimization recommendations
- Before/after metrics comparison
-
Selective Optimization:
- Compress large images maintaining quality
- Archive old daily notes and templates
- Remove orphaned attachments
- Optimize frequently accessed files
Optimization Standards
- Maximum markdown file size: 1MB
- Image compression: 85% quality for JPEGs
- PNG optimization with lossless compression
- Archive files older than 2 years (configurable)
- Maintain 90%+ search performance
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 76 lines · 35 tokens per session scan A 363532516787
vault-optimizer is an agent published in the GitHub repository IvanLutsenko/awac-ai-agent-plugins (2 stars, last pushed 1mo ago), licensed MIT. It adds 35 tokens to every session and 523 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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