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.
git clone --depth 1 https://github.com/stratalab/strata-coreWrote 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/commands/stratalab/strata-core/vector.embedding.update)<a href="https://agentmods.dev/commands/stratalab/strata-core/vector.embedding.update"><img src="https://agentmods.dev/badge/commands/stratalab/strata-core/vector.embedding.update/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/commands/stratalab/strata-core/vector.embedding.update"><img src="https://agentmods.dev/badge/commands/stratalab/strata-core/vector.embedding.update.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 | $0.00000 | $0.00169 |
| Opus 5.5 | $0.00000 | $0.00068 |
| Sonnet 5.5 | $0.00000 | $0.00034 |
| Haiku 4.5 | $0.00000 | $0.00017 |
Grade A, and why
vector.embedding.update 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.
What it actually says
Replaces one visible vector's embedding and leaves its metadata as it stands — the mirror of vector update-metadata, which patches metadata and leaves the embedding. Use this to re-embed a document after a model change, a re-chunk or a backfill; vector upsert writes the whole record, so metadata not restated there is removed. Missing vectors return a no-op mutation acknowledgement and are never created. With text instead of a vector, the text is embedded through the collection's recorded model on the same inference path as vector upsert, so the inference.* failures listed below apply only to that form.
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 First seen · 7 lines · 0 tokens per session scan A 89990849649e
vector.embedding.update is a command published in the GitHub repository stratalab/strata-core (3 stars, last pushed 17d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 169 tokens. 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 2026-09-20.
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