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 matrixorigin/memoria --skill local-embeddinggit clone --depth 1 https://github.com/matrixorigin/memoriaWrote 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/matrixorigin/memoria/local-embedding)<a href="https://agentmods.dev/skills/matrixorigin/memoria/local-embedding"><img src="https://agentmods.dev/badge/skills/matrixorigin/memoria/local-embedding/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/matrixorigin/memoria/local-embedding"><img src="https://agentmods.dev/badge/skills/matrixorigin/memoria/local-embedding.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Privilege Escalation · line 13 Commands invoke sudo or root privileges. Verify this elevated access is necessary and justified.Fix: Avoid sudo/root unless strictly required. Prefer least-privilege patterns. If elevation is needed, document the justification and scope.
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 | $0.00034 | $0.00508 |
| Opus 5.5 | $0.00014 | $0.00203 |
| Sonnet 5.5 | $0.00007 | $0.00102 |
| Haiku 4.5 | $0.00003 | $0.00051 |
Grade B, and why
local-embedding scanned grade B with 1 finding 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.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
sudo cp memoria/target/release/memoria /usr/local/bin/ What it actually says
Build from Source
Pre-built binaries do NOT include local embedding.
cd Memoria
make build-local
sudo cp memoria/target/release/memoria /usr/local/bin/
Binary is ~50-80MB (bundles ONNX Runtime). Expected.
Configure
memoria init --tool kiro # No --embedding-* flags needed
Leave EMBEDDING_* env vars empty in mcp.json → local embedding is the default.
How It Works
- First query → model downloads to
~/.cache/fastembed/(~30MB default) - Model loads via ONNX Runtime (~3-5s)
- Subsequent queries are fast (in-process)
Models
| Model | Dim | Size | Notes |
|---|---|---|---|
all-MiniLM-L6-v2 |
384 | ~30MB | Default. Fast, English |
BAAI/bge-m3 |
1024 | ~1.2GB | Best quality, multilingual |
Change model in mcp.json env block:
{ "EMBEDDING_MODEL": "BAAI/bge-m3", "EMBEDDING_DIM": "1024" }
⚠️ Choose BEFORE first startup. Dimension is locked into schema.
When to Use
| Local | Remote (OpenAI/SiliconFlow) | |
|---|---|---|
| Privacy | ✅ Offline | ⚠️ Text sent to API |
| Cost | Free | API key |
| First query | ~3-5s | Fast |
| Build | From source | Pre-built works |
| Offline | ✅ | ❌ |
Recommendation: Use remote unless you need offline/strict privacy.
Troubleshooting
| Problem | Fix |
|---|---|
| "compiled without local-embedding" | Build from source: make build-local |
| Model download fails | Set HF_ENDPOINT for mirror, or manually download to ~/.cache/fastembed/ |
| High memory | Default ~100MB. bge-m3 ~1-2GB. Choose based on available RAM |
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 First seen · 65 lines · 34 tokens per session scan B f963c2bfa1e3
local-embedding is a skill published in the GitHub repository matrixorigin/memoria (614 stars, last pushed yesterday), licensed Apache-2.0. It adds 34 tokens to every session and 508 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it B with 1 finding (asks for root). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
browserwing-admin
Manage and operate BrowserWing — an intelligent browser automation platform. Install dependencies, configure LLM, create/manage/execute automation scripts, use AI-driven exploration to generate scripts, browse the script marketplace, and troubleshoot issues.
dify
Use when building LLM applications with visual workflow — RAG knowledge bases, AI agents, chatbots with drag-and-drop orchestration. Dify: open-source LLM app platform supporting 30+ models (OpenAI, Claude, DeepSeek, Ollama, Qwen, GLM) with Docker deployment.
ai-skills
Use when building LLM applications, RAG knowledge bases, AI agents, terminal coding agents, multi-model orchestration, plugin-based agent harnesses, or file translation. Index of 14 skills: Dify, LangChain, LlamaIndex, Ollama, Transformers, Hermes Agent, OpenClaw, OpenCode, Pi, DocuTranslate, DeepSeek Harness and more.
vector-db-ops
Use when vector database operations — Pinecone, Weaviate, Qdrant, ChromaDB. Indexing, querying, filtering, and managing vector embeddings for RAG and similarity search. Use when working with vector db ops.
Vector Databases
Guides retrieval-store design, indexing, and query behavior for embedding-backed systems without confusing storage with application truth.
vector-databases
Vector database operations — embed, store, search, and build RAG pipelines.