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 itechmeat/llm-code --skill zvecgit clone --depth 1 https://github.com/itechmeat/llm-codeWrote 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/itechmeat/llm-code/zvec)<a href="https://agentmods.dev/skills/itechmeat/llm-code/zvec"><img src="https://agentmods.dev/badge/skills/itechmeat/llm-code/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/itechmeat/llm-code/zvec"><img src="https://agentmods.dev/badge/skills/itechmeat/llm-code/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 | $0.00054 | $0.02239 |
| Opus 5.5 | $0.00022 | $0.00896 |
| Sonnet 5.5 | $0.00011 | $0.00448 |
| Haiku 4.5 | $0.00005 | $0.00224 |
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 1mo 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 — 112 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").
Quick navigation
- 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=...)viainsert()orupsert(). - Query via
collection.query(vectors=VectorQuery(...), topk=...). - Call
collection.optimize()periodically after heavy ingestion.
- (Optional) Configure globals once at startup via
-
Bulk ingest + keep query latency stable
- Prefer batched
insert()/upsert(). - Monitor
collection.statsand runoptimize()when flat buffers grow.
- Prefer batched
-
Hybrid retrieval patterns
- Filter-only:
collection.query(filter=..., topk=...). - Vector + filter: pass both
vectors=...andfilter=.... - Multi-vector fusion: pass multiple
VectorQueryitems and rerank usingWeightedReRankeror RRF.
- Filter-only:
-
Memory-sensitive ANN on x86_64
- Prefer
HNSW-RaBitQwhen HNSW-quality recall matters but memory is the limiting factor. - Start with the documented defaults (
total_bits=7,num_clusters=16) and tune query-timeefbefore changing quantization bits.
- Prefer
-
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.
- Add/drop/alter scalar columns via
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.
- references/collections.md 3.9 KB
- references/concepts.md 2.4 KB
- references/configuration.md 699 B
- references/data-operations.md 6.0 KB
- references/embedding.md 2.8 KB
- references/indexing.md 8.2 KB
- references/installation.md 1.3 KB
- references/overview.md 1.6 KB
- references/quickstart.md 1.4 KB
- references/reranker.md 1.6 KB
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.
- 1mo ago Changed · +11 lines 131e8c4d6b34
- 1mo ago First seen · 101 lines · 54 tokens per session scan A 86a606d6f124
zvec is a skill published in the GitHub repository itechmeat/llm-code (24 stars, last pushed 24d ago), licensed MIT. It adds 54 tokens to every session and 2,239 once invoked, about $0.0002 per session on Opus 5.5. 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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