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 skills/zilliztech/zilliz-plugin/vectornpx skills add zilliztech/zilliz-plugin --skill vectorgit clone --depth 1 https://github.com/zilliztech/zilliz-pluginWhat 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 | $0.00028 | $0.00963 |
| Opus 5 | $0.00014 | $0.00481 |
| Sonnet 5 | $0.00006 | $0.00193 |
| Haiku 4.5 | $0.00003 | $0.00096 |
Grade A, and why
vector 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prerequisites
- CLI installed, logged in, and cluster context set (see setup skill).
- Target collection must exist and be loaded (see collection skill).
Commands Reference
All vector commands accept an optional --database <db-name> flag to target a non-default database. If omitted, the database from the current context is used.
Insert Vectors
zilliz vector insert --collection <collection-name> --data '[{"id": 1, "vector": [0.1, 0.2, ...], "text": "hello"}]'
# Optional: --database <database-name>
# Or use raw JSON: --body '{...}'
Upsert Vectors
zilliz vector upsert --collection <collection-name> --data '[{"id": 1, "vector": [0.1, 0.2, ...], "text": "hello"}]'
# Optional: --partition <partition-name>, --database <database-name>
# Or use raw JSON: --body '{...}'
Vector Search
zilliz vector search --collection <collection-name> --data '[[0.1, 0.2, 0.3, ...]]'
# Optional:
# --anns-field <vector-field-to-search-on>
# --limit <max-results-to-return>
# --filter <scalar-filter-expression>
# --output-fields '["field1", "field2"]'
# --database <database-name>
Hybrid Search
zilliz vector hybrid-search \
--collection <collection-name> \
--search '[{"data": [[0.1, ...]], "annsField": "dense_vector", "limit": 10}, {"data": [["search text"]], "annsField": "sparse_vector", "limit": 10}]' \
--rerank '{"strategy": "rrf", "params": {"k": 60}}'
# Optional:
# --limit <max-results-to-return>
# --output-fields '["field1", "field2"]'
# --database <database-name>
# Or use raw JSON: --body '{...}'
Query by Filter
zilliz vector query --collection <collection-name> --filter <scalar-filter-expression>
# Optional:
# --limit <max-results-to-return>
# --output-fields '["field1", "field2"]'
# --database <database-name>
Get by ID
zilliz vector get --collection <collection-name> --id '[1, 2, 3]'
# Optional: --output-fields '["field1", "field2"]', --database <database-name>
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 · 105 lines · 28 tokens per session scan A f530379e62d1
vector is a skill published in the GitHub repository zilliztech/zilliz-plugin (3 stars, last pushed yesterday), licensed Apache-2.0. It adds 28 tokens to every session and 963 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 2026-08-30.
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