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 skills add akhilyad/deployy --skill embeddingsgit clone --depth 1 https://github.com/akhilyad/deployyWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/akhilyad/deployy/embeddings)<a href="https://agentmods.dev/skills/akhilyad/deployy/embeddings"><img src="https://agentmods.dev/badge/skills/akhilyad/deployy/embeddings/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/akhilyad/deployy/embeddings"><img src="https://agentmods.dev/badge/skills/akhilyad/deployy/embeddings.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00062 | $0.00441 |
| Opus 5.5 | $0.00025 | $0.00176 |
| Sonnet 5.5 | $0.00012 | $0.00088 |
| Haiku 4.5 | $0.00006 | $0.00044 |
Grade A, and why
embeddings 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 17d 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.
This is a copy
100% identical to embeddings — 12 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Embeddings Skill
Purpose
Vector embeddings for semantic search and pattern matching with HNSW indexing.
Features
| Feature | Description |
|---|---|
| sql.js | Cross-platform SQLite persistent cache (WASM) |
| HNSW | 150x-12,500x faster search |
| Hyperbolic | Poincare ball model for hierarchical data |
| Normalization | L2, L1, min-max, z-score |
| Chunking | Configurable overlap and size |
| 75x faster | With agentic-flow ONNX integration |
Commands
Initialize Embeddings
npx hyrex embeddings init --backend sqlite
Embed Text
npx hyrex embeddings embed --text "authentication patterns"
Batch Embed
npx hyrex embeddings batch --file documents.json
Semantic Search
npx hyrex embeddings search --query "security best practices" --top-k 5
Memory Integration
# Store with embeddings
npx hyrex memory store --key "pattern-1" --value "description" --embed
# Search with embeddings
npx hyrex memory search --query "related patterns" --semantic
Quantization
| Type | Memory Reduction | Speed |
|---|---|---|
| Int8 | 3.92x | Fast |
| Int4 | 7.84x | Faster |
| Binary | 32x | Fastest |
Best Practices
- Use HNSW for large pattern databases
- Enable quantization for memory efficiency
- Use hyperbolic for hierarchical relationships
- Normalize embeddings for consistency
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.
- 17d ago First seen · 70 lines · 62 tokens per session scan A 0e3ab04e0b53
embeddings is a skill published in the GitHub repository akhilyad/deployy (0 stars, last pushed 5mo ago), licensed MIT. It adds 62 tokens to every session and 441 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to embeddings, differing in 12 lines, and is treated as a copy.
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