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/frankxai/claude-code-oracle-skillsWrote 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/frankxai/claude-code-oracle-skills/vector-search)<a href="https://agentmods.dev/commands/frankxai/claude-code-oracle-skills/vector-search"><img src="https://agentmods.dev/badge/commands/frankxai/claude-code-oracle-skills/vector-search/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/frankxai/claude-code-oracle-skills/vector-search"><img src="https://agentmods.dev/badge/commands/frankxai/claude-code-oracle-skills/vector-search.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 | $0.00009 | $0.00224 |
| Opus 5 | $0.00005 | $0.00112 |
| Sonnet 5 | $0.00002 | $0.00045 |
| Haiku 4.5 | $0.00001 | $0.00022 |
Grade A, and why
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 8d 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
Context
You are implementing Oracle Database 23ai AI Vector Search. This includes table creation, embedding storage, and similarity search queries.
Your Task
- Understand the use case (document search, product similarity, etc.)
- Read the oracle-ai-architect skill:
plugins/oracle-ai-architect/skills/SKILL.md - Generate:
- Table creation DDL with vector columns
- Python code for embedding generation (OCI GenAI)
- SQL queries for vector similarity search
- Hybrid search examples (vector + traditional filters)
Output Includes
- SQL DDL for vector-enabled tables
- Python code for OCI GenAI embeddings
- Example queries (cosine, euclidean, dot product)
- Best practices for indexing
Example Invocations
/vector-search "document Q&A system"
/vector-search "product recommendation engine"
/vector-search "semantic code search"
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.
- 8d ago First seen · 35 lines · 9 tokens per session scan A ea28638b888c
vector-search is a command published in the GitHub repository frankxai/claude-code-oracle-skills (2 stars, last pushed 12d ago), licensed Apache-2.0. It adds 9 tokens to every session and 224 once invoked, about $0.0000 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.
Other commands, from other repositories
ingest
Manually add knowledge to the Weaviate store.
laravel-vector-search
Add semantic/vector search with pgvector (Laravel 13+); use the laravel:vector-search skill exactly as written.
vector.batch_upsert
Upsert multiple vectors in one itemwise batch.
vector.batch_delete
Delete multiple vectors by key.
vector.collection.list
Lists vector collections visible in the selected branch and space, including collection dimension, metric, and count facts.
vector.collection.stats
Read facts for one vector collection.