An in-process vector database: a small database embedded directly in an application for storing and searching numerical representations of content. It supports collections, indexing, embeddings, reranking, and saving data to disk.

Use it to store documents and their vectors, build similarity-search indexes, ingest and update data, rerank results, and preserve collections between runs.

It provides local vector search without requiring a separate database service, helping applications find content by meaning rather than exact words.

zvec — Skill by itechmeat

Install

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.

agentmods

npx agentmods add skills/itechmeat/llm-code/zvec

Any agent

npx skills add itechmeat/llm-code --skill zvec

Clone the repo

git clone --depth 1 https://github.com/itechmeat/llm-code

Made for: Claude Code, Codex.

Per session 54 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,865 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. Scan, not verified.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00054	$0.01865
Opus 5	$0.00027	$0.00932
Sonnet 5	$0.00011	$0.00373
Haiku 4.5	$0.00005	$0.00186

Measured 2d ago against content hash 86a606d6f124, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 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.

skills/zvec/SKILL.md · 101 lines

How it starts

The opening of the file, as written. The whole thing — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Zvec

Zvec is a lightweight, in-process vector database meant to be embedded into applications ("SQLite for vectors").

Overview: references/overview.md
Concepts: references/concepts.md
Quickstart (first operations): references/quickstart.md
Installation (only if needed): references/installation.md
Index types & quantization: references/indexing.md
Embedding pipelines: references/embedding.md
Reranking pipelines: references/reranker.md
Data modeling & collections: references/collections.md
CRUD / search operations: references/data-operations.md
Configuration & persistence: references/configuration.md

Operator recipes (high signal)

Minimal “embed Zvec” checklist
- (Optional) Configure globals once at startup via zvec.init(...) (logging, query_threads).
- Create a collection on disk with create_and_open(path=..., schema=..., option=...).
- Ingest documents as Doc(id=..., fields=..., vectors=...) via insert() or upsert().
- Query via collection.query(vectors=VectorQuery(...), topk=...).
- Call collection.optimize() periodically after heavy ingestion.
Bulk ingest + keep query latency stable
- Prefer batched insert() / upsert().
- Monitor collection.stats and run optimize() when flat buffers grow.
Hybrid retrieval patterns
- Filter-only: collection.query(filter=..., topk=...).
- Vector + filter: pass both vectors=... and filter=....
- Multi-vector fusion: pass multiple VectorQuery items and rerank using WeightedReRanker or RRF.
Memory-sensitive ANN on x86_64
- Prefer HNSW-RaBitQ when HNSW-quality recall matters but memory is the limiting factor.
- Start with the documented defaults (total_bits=7, num_clusters=16) and tune query-time ef before changing quantization bits.
Safe evolution of live collections
- Add/drop/alter scalar columns via add_column(), drop_column(), alter_column().
- Manage indexes via create_index() / drop_index() (scalar). Vector indexes cannot be dropped.

Read the full file on GitHub · 101 lines

Files

What ships with it

10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

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 · 101 lines · 54 tokens per session scan A 86a606d6f124

Subscribe to this mod's changes

zvec is a skill published in the GitHub repository itechmeat/llm-code (22 stars, last pushed 1mo ago), licensed MIT. It adds 54 tokens to every session and 1,865 once invoked, about $0.0003 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 2026-08-30.

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zvec