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 benjaminasterA/antigravity-awesome-skills --skill azure-search-documents-tsgit clone --depth 1 https://github.com/benjaminasterA/antigravity-awesome-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/benjaminastera/antigravity-awesome-skills/azure-search-documents-ts)<a href="https://agentmods.dev/skills/benjaminastera/antigravity-awesome-skills/azure-search-documents-ts"><img src="https://agentmods.dev/badge/skills/benjaminastera/antigravity-awesome-skills/azure-search-documents-ts/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/benjaminastera/antigravity-awesome-skills/azure-search-documents-ts"><img src="https://agentmods.dev/badge/skills/benjaminastera/antigravity-awesome-skills/azure-search-documents-ts.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 | $0.00043 | $0.01626 |
| Opus 5 | $0.00022 | $0.00813 |
| Sonnet 5 | $0.00009 | $0.00325 |
| Haiku 4.5 | $0.00004 | $0.00163 |
Grade A, and why
azure-search-documents-ts 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 8d 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 — 272 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Azure AI Search SDK for TypeScript
Build search applications with vector, hybrid, and semantic search capabilities.
Installation
npm install @azure/search-documents @azure/identity
Environment Variables
AZURE_SEARCH_ENDPOINT=https://<service-name>.search.windows.net
AZURE_SEARCH_INDEX_NAME=my-index
AZURE_SEARCH_ADMIN_KEY=<admin-key> # Optional if using Entra ID
Authentication
import { SearchClient, SearchIndexClient } from "@azure/search-documents";
import { DefaultAzureCredential } from "@azure/identity";
const endpoint = process.env.AZURE_SEARCH_ENDPOINT!;
const indexName = process.env.AZURE_SEARCH_INDEX_NAME!;
const credential = new DefaultAzureCredential();
// For searching
const searchClient = new SearchClient(endpoint, indexName, credential);
// For index management
const indexClient = new SearchIndexClient(endpoint, credential);
Core Workflow
Create Index with Vector Field
import { SearchIndex, SearchField, VectorSearch } from "@azure/search-documents";
const index: SearchIndex = {
name: "products",
fields: [
{ name: "id", type: "Edm.String", key: true },
{ name: "title", type: "Edm.String", searchable: true },
{ name: "description", type: "Edm.String", searchable: true },
{ name: "category", type: "Edm.String", filterable: true, facetable: true },
{
name: "embedding",
type: "Collection(Edm.Single)",
searchable: true,
vectorSearchDimensions: 1536,
vectorSearchProfileName: "vector-profile",
},
],
vectorSearch: {
algorithms: [
{ name: "hnsw-algorithm", kind: "hnsw" },
],
profiles: [
{ name: "vector-profile", algorithmConfigurationName: "hnsw-algorithm" },
],
},
};
await indexClient.createOrUpdateIndex(index);
Index Documents
const documents = [
{ id: "1", title: "Widget", description: "A useful widget", category: "Tools", embedding: [...] },
{ id: "2", title: "Gadget", description: "A cool gadget", category: "Electronics", embedding: [...] },
];
const result = await searchClient.uploadDocuments(documents);
console.log(`Indexed ${result.results.length} documents`);
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.
- 8d ago First seen · 272 lines · 43 tokens per session scan A cc2bf498f117
azure-search-documents-ts is a skill published in the GitHub repository benjaminasterA/antigravity-awesome-skills (271 stars, last pushed today), licensed MIT. It adds 43 tokens to every session and 1,626 once invoked, about $0.0002 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.
Other skills, from other repositories
ai-orchestration-llamaindex
LlamaIndex.TS data framework for RAG, indexing, retrieval, query engines, chat engines, and agentic workflows in TypeScript.
ai-provider-cohere-sdk
Official Cohere TypeScript SDK patterns -- CohereClientV2, chat, embeddings, rerank, RAG with citations, tool use, streaming, and model selection.
transformers-js
Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. Supports NLP (text classification, translation, summarization), computer vision (image classification, object detection), audio (speech recognition, audio classification), and multimodal tasks. Works in browsers and…
ax-signature
This skill helps an LLM generate correct DSPy signature code using @ax-llm/ax. Use when the user asks about signatures, s(), f(), field types, string syntax, fluent builder API, validation constraints, or type-safe inputs/outputs.
ax-cpp-ai
Use when writing C++ code with axllm for named deployment profiles, generic provider clients, model selection, OpenAI-compatible calls, Responses, Gemini, Anthropic, routers, and balancers.
ax-cpp-gen
Use when writing C++ code with axllm for AxGen programs, forward calls, indexed multi-sampling, result pickers, streaming, tools, assertions, traces, usage, and output parsing.