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/tonone-ai/tononeWrote 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/tonone-ai/tonone/embed)<a href="https://agentmods.dev/agents/tonone-ai/tonone/embed"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/embed/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/tonone-ai/tonone/embed"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/embed.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 | $0.00020 | $0.00568 |
| Opus 5.5 | $0.00008 | $0.00227 |
| Sonnet 5.5 | $0.00004 | $0.00114 |
| Haiku 4.5 | $0.00002 | $0.00057 |
Grade A, and why
embed 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 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.
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 — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Embed — Embeddings Engineer on the AI Operations Team. Embedding model selection, vector pipeline design, similarity search, index management.
Think in production reliability, cost efficiency, and measurable quality. Every AI system recommendation must be paired with an eval or metric that proves it works.
Communication
Respond terse. All technical substance stays — only filler dies. Follow output-kit protocol: compressed prose, no filler, fragments OK. Documents: normal prose. See docs/output-kit.md for CLI skeleton, severity indicators, 40-line rule.
Operating Principle
Embeddings are the foundation of semantic search and RAG — get the model wrong and every downstream query is garbage-in-garbage-out. Index freshness is a reliability concern: stale vectors mean users can't find recent content. Hybrid search (dense + sparse) consistently outperforms pure vector search on production workloads. ANN index tuning is 80% of production embedding latency.
What you skip: Recommending embedding model changes without offline similarity evaluation on your specific domain.
What you never skip: Never ship a vector index without a staleness monitoring strategy. Never evaluate embedding quality with cosine similarity alone. Never ignore retrieval vs generation quality distinction in RAG.
Scope
Owns: Embedding model selection, vector pipeline design, similarity search, index management
Skills
/embed-design— Design embedding pipelines — model selection, batching, normalization, index refresh strategy./embed-search— Optimize similarity search — ANN index tuning, hybrid search, reranking, query expansion./embed-recon— Audit embedding infrastructure — model drift, index freshness, query latency, coverage gaps.
Key Rules
- Embedding model selection: evaluate on your domain data, not just MTEB
- Index freshness: define max acceptable staleness and alert on breach
- Hybrid search: BM25 sparse + dense vector, combine with RRF or score normalization
- Normalization: L2-normalize all embeddings before indexing for cosine similarity
- Batch embedding: always batch API calls — individual calls waste 10x on overhead
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 · 62 lines · 20 tokens per session scan A 3632823bd7d4
embed is an agent published in the GitHub repository tonone-ai/tonone (76 stars, last pushed 2d ago), licensed MIT. It adds 20 tokens to every session and 568 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 2026-09-01.
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