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 ericrisco/rsc-harness --skill embeddings-searchgit clone --depth 1 https://github.com/ericrisco/rsc-harnessWrote 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/ericrisco/rsc-harness/embeddings-search)<a href="https://agentmods.dev/skills/ericrisco/rsc-harness/embeddings-search"><img src="https://agentmods.dev/badge/skills/ericrisco/rsc-harness/embeddings-search/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/ericrisco/rsc-harness/embeddings-search"><img src="https://agentmods.dev/badge/skills/ericrisco/rsc-harness/embeddings-search.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00088 | $0.02825 |
| Opus 5 | $0.00044 | $0.01412 |
| Sonnet 5 | $0.00018 | $0.00565 |
| Haiku 4.5 | $0.00009 | $0.00282 |
Grade A, and why
embeddings-search 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 5d 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 — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
embeddings-search — make and judge the vectors
You own the embedding technique layer: turn a corpus into searchable vectors, turn a question into a good retrieval, and measure whether that retrieval is any good. You stop the moment the right chunks come back, measured by a number. You do not assemble a prompt or generate an answer.
Route the adjacent surfaces away:
- Operating the store — collection schema, HNSW/IVFFlat tuning, metadata-filter path,
quantization,
ef_searchrecall knobs →../vector-db/SKILL.md. You decide what vectors go in and how to query; vector-db decides how the store holds and serves them. - The full retrieve → rerank → prompt → generate → answer loop and its groundedness /
faithfulness eval →
../rag/SKILL.md. - Pulling typed fields out of documents (invoice number, date, total) →
../structured-extraction/SKILL.md. - Writing the prompt the model reasons with →
../prompt-engineering/SKILL.md.
1. Pick the embedding model
Decide on three axes: language coverage, quality tier (read MTEB but don't worship it), and cost — where cost is set by dimensions, because dims set storage and memory.
| Model | Best when | Dims (Matryoshka) | Max input | ~Price /1M tok | Query/doc asymmetry |
|---|---|---|---|---|---|
OpenAI text-embedding-3-small |
Cheap English/multi baseline | 1536 (truncatable) | 8191 tok | ~$0.02 | none required |
OpenAI text-embedding-3-large |
Higher quality, still API-simple | 3072 (truncatable) | 8191 tok | ~$0.13 | none required |
Cohere embed-v4 |
Strong multilingual, API | up to 1536 | long | API-priced | search_query vs search_document |
Voyage voyage-3-large |
Retrieval-specialised, top tasks | model-set | long | API-priced | yes (input_type) |
| Gemini Embedding | Tops MTEB English retrieval (~68.3) | truncatable | long | API-priced | yes (task type) |
BGE-M3 / e5 (open) |
Self-host, no per-token bill | 1024 (BGE-M3) | long | self-host | yes (query: / passage:) |
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago First seen · 185 lines · 88 tokens per session scan A ba1819b30a20
embeddings-search is a skill published in the GitHub repository ericrisco/rsc-harness (74 stars, last pushed 2d ago), licensed MIT. It adds 88 tokens to every session and 2,825 once invoked, about $0.0004 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 2026-09-03.
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