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 yoanbernabeu/grepai-skills --skill grepai-embeddings-openaigit clone --depth 1 https://github.com/yoanbernabeu/grepai-skillsWrote 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/yoanbernabeu/grepai-skills/grepai-embeddings-openai)<a href="https://agentmods.dev/skills/yoanbernabeu/grepai-skills/grepai-embeddings-openai"><img src="https://agentmods.dev/badge/skills/yoanbernabeu/grepai-skills/grepai-embeddings-openai/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/yoanbernabeu/grepai-skills/grepai-embeddings-openai"><img src="https://agentmods.dev/badge/skills/yoanbernabeu/grepai-skills/grepai-embeddings-openai.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk 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 | $0.00028 | $0.01711 |
| Opus 5 | $0.00014 | $0.00856 |
| Sonnet 5 | $0.00006 | $0.00342 |
| Haiku 4.5 | $0.00003 | $0.00171 |
Grade A, and why
grepai-embeddings-openai 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 12d 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 — 299 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GrepAI Embeddings with OpenAI
This skill covers using OpenAI's embedding API with GrepAI for high-quality, cloud-based embeddings.
When to Use This Skill
- Need highest quality embeddings
- Team environment with shared infrastructure
- Don't want to manage local embedding server
- Willing to trade privacy for quality/convenience
Considerations
| Aspect | Details |
|---|---|
| ✅ Quality | State-of-the-art embeddings |
| ✅ Speed | Fast, no local compute needed |
| ✅ Scalability | Handles any codebase size |
| ⚠️ Privacy | Code sent to OpenAI servers |
| ⚠️ Cost | Pay per token |
| ⚠️ Internet | Requires connection |
Prerequisites
- OpenAI API key
- Billing enabled on OpenAI account
Get your API key at: https://platform.openai.com/api-keys
Configuration
Basic Configuration
# .grepai/config.yaml
embedder:
provider: openai
model: text-embedding-3-small
api_key: ${OPENAI_API_KEY}
Set the environment variable:
export OPENAI_API_KEY="sk-..."
With Parallel Processing
embedder:
provider: openai
model: text-embedding-3-small
api_key: ${OPENAI_API_KEY}
parallelism: 8 # Concurrent requests for speed
Direct API Key (Not Recommended)
embedder:
provider: openai
model: text-embedding-3-small
api_key: sk-your-api-key-here # Avoid committing secrets!
Warning: Never commit API keys to version control.
Available Models
text-embedding-3-small (Recommended)
| Property | Value |
|---|---|
| Dimensions | 1536 |
| Price | $0.00002 / 1K tokens |
| Quality | Very high |
| Speed | Fast |
Best for: Most use cases, good balance of cost/quality.
embedder:
provider: openai
model: text-embedding-3-small
text-embedding-3-large
| Property | Value |
|---|---|
| Dimensions | 3072 |
| Price | $0.00013 / 1K tokens |
| Quality | Highest |
| Speed | Fast |
Best for: Maximum accuracy, cost not a concern.
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.
- 12d ago First seen · 299 lines · 28 tokens per session scan A 4967e1054851
grepai-embeddings-openai is a skill published in the GitHub repository yoanbernabeu/grepai-skills (20 stars, last pushed 7mo ago), licensed MIT. It adds 28 tokens to every session and 1,711 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 2026-08-30.
Other skills, from other repositories
qdrant-clients-sdk
Qdrant provides client SDKs for various programming languages, allowing easy integration with Qdrant deployments.
gno
Search local documents, files, notes, and knowledge bases. Index directories, search with BM25/vector/hybrid, get AI answers with citations. Use when user wants to search files, find documents, query notes, look up information in local folders, index a directory, set up document search, build a knowledge base, needs…
qdrant-relevance-feedback
Expanding the candidate pool via relevance feedback, as an alternative to reranking when a dense retriever is too weak. Use when someone asks about 'Qdrant's Relevance Feedback API', 'improving dense search relevance/recall', 'how to discover/get more relevant results from vector search', 'cheaper/better alternative…
qdrant-hybrid-search-prefetches
Constructing prefetch queries for hybrid retrieval, including sparse/dense and multi-field setups, and choosing a sparse embedding model. Use when someone asks 'dense and sparse in one search?', 'how to combine multiple fields for retrieval?', 'payloads or sparse vectors for lexical?', 'which sparse embedding model to…
qdrant-model-migration
Guides embedding model migration in Qdrant without downtime. Use when someone asks 'how to switch embedding models', 'how to migrate vectors', 'how to update to a new model', 'zero-downtime model change', 'how to re-embed my data', or 'can I use two models at once'. Also use when upgrading model dimensions, switching…
qdrant-multitenancy
Guides tenant isolation architecture in Qdrant for multi-tenant or multi-user applications. Use when someone asks 'how to isolate customer data', 'how to build multi-tenant search/RAG', 'how many collections should I create', 'how to partition tenants by payload', 'a customer's data legally has to stay in a certain…