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/SpillwaveSolutions/agent-brainWrote 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/spillwavesolutions/agent-brain/agent-brain-embeddings)<a href="https://agentmods.dev/commands/spillwavesolutions/agent-brain/agent-brain-embeddings"><img src="https://agentmods.dev/badge/commands/spillwavesolutions/agent-brain/agent-brain-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/commands/spillwavesolutions/agent-brain/agent-brain-embeddings"><img src="https://agentmods.dev/badge/commands/spillwavesolutions/agent-brain/agent-brain-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 | $0.00014 | $0.01461 |
| Opus 5.5 | $0.00006 | $0.00584 |
| Sonnet 5.5 | $0.00003 | $0.00292 |
| Haiku 4.5 | $0.00001 | $0.00146 |
Grade A, and why
agent-brain-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 2mo 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 — 234 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Brain Embeddings Configuration
Purpose
Configures the embedding provider used for vector/semantic search. Embeddings convert text into numerical vectors that enable semantic similarity search.
Usage
/agent-brain:agent-brain-embeddings [provider] [--model <model>]
Parameters
| Parameter | Required | Default | Description |
|---|---|---|---|
| provider | No | - | Provider name: openai, cohere, ollama, gemini, grok, sentence-transformers |
| --model | No | Provider default | Specific model to use |
Available Providers
OpenAI
Industry-standard embeddings with high quality vectors.
| Model | Dimensions | Use Case |
|---|---|---|
| text-embedding-3-large | 3072 | Highest quality, production |
| text-embedding-3-small | 1536 | Faster, cost-effective |
| text-embedding-ada-002 | 1536 | Legacy, widely compatible |
Configuration:
export EMBEDDING_PROVIDER=openai
export EMBEDDING_MODEL=text-embedding-3-large
export OPENAI_API_KEY=sk-proj-...
Cohere
Multi-language support with good quality vectors.
| Model | Dimensions | Use Case |
|---|---|---|
| embed-english-v3.0 | 1024 | English content |
| embed-multilingual-v3.0 | 1024 | International content |
| embed-english-light-v3.0 | 384 | Lightweight, fast |
Configuration:
export EMBEDDING_PROVIDER=cohere
export EMBEDDING_MODEL=embed-english-v3.0
export COHERE_API_KEY=...
Ollama (Local)
Privacy-first local embeddings with no API costs.
| Model | Dimensions | Use Case |
|---|---|---|
| nomic-embed-text | 768 | General purpose |
| mxbai-embed-large | 1024 | Higher quality |
| all-minilm | 384 | Lightweight, fast |
Configuration:
export EMBEDDING_PROVIDER=ollama
export EMBEDDING_MODEL=nomic-embed-text
export OLLAMA_BASE_URL=http://localhost:11434
Setup:
# Pull the model first
ollama pull nomic-embed-text
Execution
Interactive Configuration
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.
- 2mo ago First seen · 234 lines · 14 tokens per session scan A 8068df20fded
agent-brain-embeddings is a command published in the GitHub repository SpillwaveSolutions/agent-brain (119 stars, last pushed 1mo ago), licensed MIT. It adds 14 tokens to every session and 1,461 once invoked, about $0.0001 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 2026-08-30.
agent-brain-embeddings compared with commands from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| agent-brain-embeddingsSpillwaveSolutions/agent-brain | Command | 119 | 1mo ago | MIT | A | 14 | Claude Code |
| checklistgithub/spec-kit | Command | 148k | yesterday | MIT | A | 13 | Claude Code |
| clarifygithub/spec-kit | Command | 148k | yesterday | MIT | A | 27 | Claude Code |
| specifygithub/spec-kit | Command | 148k | yesterday | MIT | A | 13 | Claude Code |
| analyzegithub/spec-kit | Command | 148k | yesterday | MIT | A | 26 | Claude Code |
| convergegithub/spec-kit | Command | 148k | yesterday | MIT | A | 35 | Claude Code |
| implementgithub/spec-kit | Command | 148k | yesterday | MIT | A | 14 | Claude Code |