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 orneryd/NornicDB --skill managed-embeddingsgit clone --depth 1 https://github.com/orneryd/NornicDBWrote 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/orneryd/nornicdb/managed-embeddings)<a href="https://agentmods.dev/skills/orneryd/nornicdb/managed-embeddings"><img src="https://agentmods.dev/badge/skills/orneryd/nornicdb/managed-embeddings/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/orneryd/nornicdb/managed-embeddings"><img src="https://agentmods.dev/badge/skills/orneryd/nornicdb/managed-embeddings.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 | $0.00094 | $0.02485 |
| Opus 5.5 | $0.00038 | $0.00994 |
| Sonnet 5.5 | $0.00019 | $0.00497 |
| Haiku 4.5 | $0.00009 | $0.00248 |
Grade A, and why
nornicdb-managed-embeddings 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 — 219 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Managed Embeddings (Cypher API)
NornicDB can generate embeddings server-side and store them on nodes for you. From the consumer's point of view this is a Cypher feature: a WITH EMBEDDING clause on writes, and a db.index.vector.embed procedure for ad-hoc embedding.
Two storage slots, both readable
Every node has two embedding slots:
ChunkEmbeddings— managed by NornicDB.[][]float32— one entry per chunk;ChunkEmbeddings[0]is the primary embedding. Built by the embedding worker or byWITH EMBEDDING. Internal metadata (model,dimensions,chunk_count,embedded_at) is stored separately so it does not polluteProperties.NamedEmbeddings— client-managed.map[string][]float32, keyed by vector name (e.g."default","openai-small"). Set viadb.create.setNodeVectorPropertyor the Qdrant gRPC compatibility layer.
Vector index lookup order is NamedEmbeddings[indexProperty] → node.Properties[indexProperty] → ChunkEmbeddings. You usually want a vector index on a label whose nodes have managed embeddings, in which case nothing extra is needed.
Enabling managed embeddings
export NORNICDB_EMBEDDING_ENABLED=true
export NORNICDB_EMBEDDING_PROVIDER=ollama # ollama | openai | orca | voyage | local
export NORNICDB_EMBEDDING_MODEL=mxbai-embed-large
export NORNICDB_EMBEDDING_API_URL=http://localhost:11434
export NORNICDB_EMBEDDING_DIMENSIONS=1024
YAML form:
embedding:
enabled: true
provider: ollama # ollama | openai | orca | voyage | local
model: mxbai-embed-large
url: http://localhost:11434
dimensions: 1024
The OpenAI, Orca, and Voyage providers read embedding.api_key (or NORNICDB_EMBEDDING_API_KEY). The local provider resolves the model file inside NORNICDB_MODELS_DIR.
Defaults shipped with NornicDB: provider=local, model=bge-m3, dimensions=1024.
Per-database configuration
For a named database, use canonical keys in the YAML databases: map or
PUT /admin/databases/{name}/config: db.nornic.embedding.provider,
db.nornic.embedding.model, db.nornic.embedding.api.url,
db.nornic.embedding.api.key, db.nornic.embedding.mode, and
db.nornic.embedding.dimensions. Their
NORNICDB_* forms remain supported global environment alternatives and
alternate input names; canonical keys win collisions.
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 · +2 lines 09f2c0fae9e3
- 1mo ago Changed · +1 lines · +4 tokens per session 674958d6f80d
- 1mo ago First seen · 216 lines · 90 tokens per session scan A 917d103874ef
nornicdb-managed-embeddings is a skill published in the GitHub repository orneryd/NornicDB (919 stars, last pushed yesterday), licensed MIT. It adds 94 tokens to every session and 2,485 once invoked, about $0.0004 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 2026-09-05.
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