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/azure/documentdb-agent-kit/vector-searchnpx skills add Azure/documentdb-agent-kit --skill vector-searchgit clone --depth 1 https://github.com/Azure/documentdb-agent-kitWhat 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 | $0.00106 | $0.00472 |
| Opus 5 | $0.00053 | $0.00236 |
| Sonnet 5 | $0.00021 | $0.00094 |
| Haiku 4.5 | $0.00011 | $0.00047 |
Grade A, and why
documentdb-vector-search 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.
What it actually says
Vector Search — Azure DocumentDB (cosmosSearch)
Azure DocumentDB's native vector index type is cosmosSearch. Pick the sub-type by scale:
| Index sub-type | Scale sweet spot | Tier |
|---|---|---|
vector-diskann (recommended) |
Up to 500k+ vectors | M30+ |
vector-hnsw |
Up to ~50k vectors | M30+ |
vector-ivf |
Under ~10k vectors | M10+ |
Similarity options: COS (cosine), L2 (Euclidean), IP (inner product).
Rules
- vector-choose-index-type — Prefer DiskANN for production; use HNSW up to 50k, IVF under 10k.
- vector-create-diskann-index — Create a
vector-diskannindex with correctdimensions,similarity,maxDegree, andlBuild. - vector-knn-query — Query with
$search+cosmosSearch; tunelSearchandk; combine with pre-filters. - vector-product-quantization — Shrink high-dimensional vectors (up to 16,000 dims) while preserving recall.
- vector-half-precision — Halve vector memory with fp16 indexing and minimal recall loss.
- vector-normalize-embeddings — Normalize embeddings when using cosine similarity; store model + dimensions alongside vectors.
What ships with it
6 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.
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 · 27 lines · 106 tokens per session scan A 641ac2c39af8
documentdb-vector-search is a skill published in the GitHub repository Azure/documentdb-agent-kit (5 stars, last pushed 1mo ago), licensed MIT. It adds 106 tokens to every session and 472 once invoked, about $0.0005 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 2026-08-31.
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