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 vikasudasi/skill-vault --skill semantic-search-embeddingsgit clone --depth 1 https://github.com/vikasudasi/skill-vaultWrote 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/vikasudasi/skill-vault/semantic-search-embeddings)<a href="https://agentmods.dev/skills/vikasudasi/skill-vault/semantic-search-embeddings"><img src="https://agentmods.dev/badge/skills/vikasudasi/skill-vault/semantic-search-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/skills/vikasudasi/skill-vault/semantic-search-embeddings"><img src="https://agentmods.dev/badge/skills/vikasudasi/skill-vault/semantic-search-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 | $0.00028 | $0.00443 |
| Opus 5 | $0.00014 | $0.00221 |
| Sonnet 5 | $0.00006 | $0.00089 |
| Haiku 4.5 | $0.00003 | $0.00044 |
Grade A, and why
semantic-search-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 9d 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.
What it actually says
Semantic Search with Embeddings
Use when keyword search is too brittle and you want to retrieve by meaning.
Pipeline
- Embed documents → vectors.
- Store in a vector index (SQLite sqlite-vec, pgvector, FAISS, Qdrant…).
- Query → embed the query, compute cosine similarity, take top-k.
- Filter → apply scope/trust/visibility after similarity.
Embed the right text
Embed discovery metadata (title, description, tags, trigger phrases), not the full body. This mirrors Skill Vault's approach: it keeps indexes small and retrieval focused on what it's for, not every word. Weight: name + description
- a few keywords is usually enough for good recall.
Pick a model
- Local, cheap, deterministic: all-MiniLM-L6-v2 (384-dim) — good for self-hosted, private searches (Skill Vault uses this, same as agent-knowledge-graph).
- Higher quality, bigger: OpenAI/Mistral embedding APIs (1536+ dims) — needs a key + network.
Ranking
Cosine similarity is standard; normalize vectors so dot product == cosine. Round scores for stable display (Skill Vault rounds to 4 dp).
Pitfalls
- Embedding garbage in → garbage out: dedupe/normalize text first.
- Keep the vector dimension fixed to the model; switching models orphans old vectors.
- Store only metadata embeddings; return full content by id on demand (progressive disclosure) to keep the index small.
What ships with it
2 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.
- 9d ago First seen · 48 lines · 28 tokens per session scan A 7325736b406b
semantic-search-embeddings is a skill published in the GitHub repository vikasudasi/skill-vault (0 stars, last pushed 24d ago), licensed Apache-2.0. It adds 28 tokens to every session and 443 once invoked, about $0.0001 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 2026-08-31.
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