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 JinLee794/agent-framework-skills --skill ai-searchgit clone --depth 1 https://github.com/JinLee794/agent-framework-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/jinlee794/agent-framework-skills/ai-search)<a href="https://agentmods.dev/skills/jinlee794/agent-framework-skills/ai-search"><img src="https://agentmods.dev/badge/skills/jinlee794/agent-framework-skills/ai-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/jinlee794/agent-framework-skills/ai-search"><img src="https://agentmods.dev/badge/skills/jinlee794/agent-framework-skills/ai-search.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 | $0.00083 | $0.01902 |
| Opus 5 | $0.00042 | $0.00951 |
| Sonnet 5 | $0.00017 | $0.00380 |
| Haiku 4.5 | $0.00008 | $0.00190 |
Grade A, and why
ai-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 10d 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 — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Azure AI Search Indexing for Foundry Agents
Build a repeatable push pipeline that reads content in Python, chunks it in Python, creates embeddings with the existing Foundry resource, and writes vectors plus readable text directly to the existing Azure AI Search index.
The resource boundary is deliberate:
local or application-readable files
-> Python loader and token-aware chunker
-> Foundry embedding deployment
-> Azure AI Search push API
Do not add Blob Storage, ADLS, Cosmos DB, indexers, skillsets, Content Understanding, project connections, managed identities, or a second model resource. The local MAF agent consumes the finished index directly; this workflow ends at a live, verified search index.
Runtime retrieval belongs to maf-foundry-agent. Agent and voice behaviour belongs in YAML and is owned by maf-agent-config.
Load only the depth you need
| Task | Reference |
|---|---|
Establish packages, .env, credentials, and first connection |
references/setup.md |
| Implement chunking, stable keys, embeddings, schema, and uploads | references/indexing-pipeline.md |
| Inspect and query the live index without exposing secrets | references/live-checks.md |
Environment contract
Read deployment coordinates from the repository-root .env. Never print key values.
| Variable | Purpose |
|---|---|
FOUNDRY_PROJECT_ENDPOINT |
Existing project endpoint, ending in /api/projects/<project> |
FOUNDRY_MODEL |
Chat deployment used by the reasoning and VoiceLive paths |
FOUNDRY_EMBEDDING_MODEL |
Embedding deployment name, not the model family guessed from it |
AZURE_SEARCH_ENDPOINT |
Existing Azure AI Search service endpoint |
AZURE_SEARCH_API_KEY |
Admin key in the indexing process; query key in read-only runtime processes |
AZURE_SEARCH_INDEX_NAME |
The exact index to inspect, create, populate, and query |
AZURE_SEARCH_API_VERSION |
Pinned Search API version used by the installed SDK |
What ships with it
4 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.
- 10d ago First seen · 174 lines · 83 tokens per session scan A e4f7dd90f4c9
ai-search is a skill published in the GitHub repository JinLee794/agent-framework-skills (2 stars, last pushed 1mo ago), licensed MIT. It adds 83 tokens to every session and 1,902 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 2026-08-31.
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