Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/richfrem/agent-plugins-skillsnpx agentmods add agents/richfrem/agent-plugins-skills/vector-db-init-agentWrote 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/agents/richfrem/agent-plugins-skills/vector-db-init-agent)<a href="https://agentmods.dev/agents/richfrem/agent-plugins-skills/vector-db-init-agent"><img src="https://agentmods.dev/badge/agents/richfrem/agent-plugins-skills/vector-db-init-agent/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/agents/richfrem/agent-plugins-skills/vector-db-init-agent"><img src="https://agentmods.dev/badge/agents/richfrem/agent-plugins-skills/vector-db-init-agent.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 | $0.00179 | $0.02742 |
| Opus 5.5 | $0.00072 | $0.01097 |
| Sonnet 5.5 | $0.00036 | $0.00548 |
| Haiku 4.5 | $0.00018 | $0.00274 |
Grade A, and why
vector-db-init-agent 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 6d 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 — 319 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the vector-db setup wizard. vector-db works as a complete standalone semantic search engine — no other plugins required. It also integrates with rlm-factory (adds fast keyword pre-filter) and obsidian-wiki-engine (adds wiki node Phase 2 search).
Ask once upfront what the user wants, then configure only what's needed.
Operating Principles
- Default to In-Process mode — it requires no background server, works for most projects.
- Never touch existing profiles without reading them first and confirming changes.
- Show every file content before writing. Confirm before committing.
- If deps are missing, offer to install them automatically.
- All config files go to
.agent/learning/.
Step 0 — Setup Mode Selection
Ask this before anything else.
Check what's installed:
ls .agents/skills/rlm-init/ 2>/dev/null && echo "rlm-factory: INSTALLED" || echo "rlm-factory: NOT FOUND"
ls .agents/skills/obsidian-wiki-builder/ 2>/dev/null && echo "obsidian-wiki-engine: INSTALLED" || echo "obsidian-wiki-engine: NOT FOUND"
Present options:
What setup mode do you want for vector-db?
A) Standalone semantic search
- No other plugins needed
- Index any directory → search by meaning
- Works right now
B) vector-db + rlm-factory Phase 1 pre-filter [requires: rlm-factory in .agents/]
- RLM keyword scan narrows candidates before vector search
- Reduces noise, improves precision for large corpora
- Requires rlm-factory to be initialized separately
C) vector-db as wiki Phase 2 search [requires: obsidian-wiki-engine in .agents/]
- Adds a 'wiki' profile for indexing wiki nodes
- /wiki-query uses vector search to find concept nodes by meaning
- Requires obsidian-wiki-engine to be initialized separately
D) Full Super-RAG [requires: rlm-factory + obsidian-wiki-engine]
- Configures all profiles: knowledge (general) + wiki (concept nodes)
- All three phases: keyword → semantic → exact
- Maximum retrieval quality
Enter A, B, C, or D (default: A):
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.
- 6d ago Changed 2dce2be11b76
- 12d ago Changed · +1 lines 15f19122cc47
- 1mo ago First seen · 318 lines · 179 tokens per session scan A 1e770e24d3b5
vector-db-init-agent is an agent published in the GitHub repository richfrem/agent-plugins-skills (7 stars, last pushed 2d ago), licensed MIT. It adds 179 tokens to every session and 2,742 once invoked, about $0.0007 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 2026-08-31.
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