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/aeriondyseti/vector-memory-mcp/vector-memory-usagenpx skills add aeriondyseti/vector-memory-mcp --skill vector-memory-usagegit clone --depth 1 https://github.com/aeriondyseti/vector-memory-mcpWrote 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/aeriondyseti/vector-memory-mcp/vector-memory-usage)<a href="https://agentmods.dev/skills/aeriondyseti/vector-memory-mcp/vector-memory-usage"><img src="https://agentmods.dev/badge/skills/aeriondyseti/vector-memory-mcp/vector-memory-usage.svg" alt="Measured on agentmods" 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 | $0.00102 | $0.01871 |
| Opus 5 | $0.00051 | $0.00936 |
| Sonnet 5 | $0.00020 | $0.00374 |
| Haiku 4.5 | $0.00010 | $0.00187 |
Grade A, and why
vector-memory-usage 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 4d 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 — 178 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Vector Memory Usage
The vector memory system provides semantic, project-scoped memory storage. Memories persist across sessions and are retrieved via semantic search — meaning queries find relevant memories by meaning, not just keyword matching.
Database Storage and Version Control
The vector-memory MCP server stores its database as a single SQLite file (.vector-memory/memories.db) inside the project directory. This database should be committed to version control by default. Committing the database ensures:
- Portability — cloning the repo includes all accumulated project context, so new sessions (or new machines) start with full memory intact
- Collaboration — teammates benefit from shared architectural decisions, known blockers, and implementation insights
- Durability — the database is backed up alongside the code it describes, preventing accidental loss
The database is a single SQLite file with sqlite-vec for vector search and FTS5 for full-text search. If a project has sensitive memories that should not be committed, add .vector-memory/ to .gitignore on a per-project basis — but the default expectation is to commit it.
When to Proactively Search Memories
Search memories BEFORE making decisions or assumptions. The cost of an unnecessary search is low; the cost of missing relevant context is high.
Mandatory Search Triggers
- Architectural decisions — before choosing a library, pattern, or approach, search for prior decisions on the same topic
- Debugging unfamiliar code — search for implementation notes, known issues, or past resolutions
- Starting a new task — search for relevant context, prior attempts, or related decisions
- Referential ambiguity — when the user says "the project", "that bug", "last time", "as we discussed", search to resolve the reference
- Repeated patterns — when implementing something similar to past work, search for the established pattern
Recommended Search Triggers
- Before suggesting solutions — check if the problem was solved before
- When encountering unfamiliar conventions — search for project-specific patterns or standards
- Code review context — search for why code was written a certain way before suggesting changes
- Configuration questions — search for prior setup decisions and rationale
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.
- 4d ago First seen · 178 lines · 102 tokens per session scan A e06e1fdcdc32
vector-memory-usage is a skill published in the GitHub repository aeriondyseti/vector-memory-mcp (5 stars, last pushed 1mo ago), licensed MIT. It adds 102 tokens to every session and 1,871 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 2026-08-30.
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