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 oyi77/1ai-skills --skill zvecgit clone --depth 1 https://github.com/oyi77/1ai-skillsWrote 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/oyi77/1ai-skills/zvec)<a href="https://agentmods.dev/skills/oyi77/1ai-skills/zvec"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/zvec/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/oyi77/1ai-skills/zvec"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/zvec.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00028 | $0.01549 |
| Opus 5 | $0.00014 | $0.00775 |
| Sonnet 5 | $0.00006 | $0.00310 |
| Haiku 4.5 | $0.00003 | $0.00155 |
Grade A, and why
zvec 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 6d 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 — 223 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ZVec Skill
Alibaba's lightweight in-process vector database - "The SQLite of Vector Databases"
Overview
ZVec is an open-source, in-process vector database from Alibaba's Tongyi Lab. It's lightweight, blazing fast, and embeds directly into your application - no server needed. Built on Proxima (Alibaba's battle-tested vector search engine used in production across Taobao, Ele.me, and more).
Anti-Rationalization Table
| Rationalization | Reality |
|---|---|
| "I'll figure it out as I go" | A structured approach saves time and reduces errors. Follow the workflow in this skill rather than improvising. |
| "I already know this topic" | Familiarity breeds shortcuts. Use the checklist to verify you haven't missed critical steps. |
| "This doesn't apply to my situation" | The patterns here generalize across contexts. Adapt, don't skip — the underlying principles hold. |
| "One more tool will fix it" | Adding complexity rarely solves process gaps. Master the core workflow first. |
When to Use
Trigger phrases:
- "Mikolov et al."
- "Zero-copy vector operations for efficient similarity search and embedding storag"
Use this skill when you need:
- Lightweight vector storage with minimal setup
- Fast local RAG without external services
- Edge AI with on-device embeddings
- Simple API that "just works"
- Production-grade performance in a tiny package
Key Features
- Automated workflow execution with error recovery
- Configurable parameters for different use cases
- Integration with existing tooling and pipelines
- Detailed logging and status reporting
🚀 Blazing Fast
- Searches billions of vectors in milliseconds
- Built on Alibaba's Proxima engine
- Optimized for low latency
📦 Simple, Just Works
pip install zvecand start searching- No servers, no config, no daemon
- Runs wherever your code runs
🌍 Runs Anywhere
- macOS (ARM64)
- Linux (x86_64, ARM64)
- Python 3.10-3.12
- Node.js support
🔍 Rich Query Support
- Dense and sparse vectors
- Hybrid search with filters
- Multiple index types (Flat, HNSW, IVF)
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.
- 6d ago First seen · 223 lines · 28 tokens per session scan A 70c9af62f5ee
zvec is a skill published in the GitHub repository oyi77/1ai-skills (12 stars, last pushed today), licensed MIT. It adds 28 tokens to every session and 1,549 once invoked, about $0.0001 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 2026-09-04.
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