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 maf-foundry-agentgit 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/maf-foundry-agent)<a href="https://agentmods.dev/skills/jinlee794/agent-framework-skills/maf-foundry-agent"><img src="https://agentmods.dev/badge/skills/jinlee794/agent-framework-skills/maf-foundry-agent/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/maf-foundry-agent"><img src="https://agentmods.dev/badge/skills/jinlee794/agent-framework-skills/maf-foundry-agent.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 | $0.00077 | $0.02015 |
| Opus 5 | $0.00039 | $0.01007 |
| Sonnet 5 | $0.00015 | $0.00403 |
| Haiku 4.5 | $0.00008 | $0.00201 |
Grade A, and why
maf-foundry-agent 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.
How it starts
The opening of the file, as written. The whole thing — 214 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MAF on Foundry — Agent Patterns
Depth lives in references. Load one only when the task needs it.
| Task | Reference |
|---|---|
Write a @tool, author an Agent Skill, set approval modes |
references/tools-and-skills.md |
Session history and custom ContextProvider |
references/memory-and-context.md |
| Ground the agent in Azure AI Search | references/retrieval.md |
Creating, populating, and validating the Azure AI Search index is owned by the separate
ai-search skill.
Install
python -m pip install agent-framework agent-framework-foundry agent-framework-declarative azure-identity
Choose the chat client by credential
Pick the client from the credential you actually hold. Getting this wrong surfaces as a 403
on the first model call, long after startup looked healthy.
| You have | Client | Endpoint |
|---|---|---|
Entra token and Foundry User on the resource |
FoundryChatClient |
project endpoint |
| Resource API key only | OpenAIChatClient |
<resource>/openai/v1/ |
FoundryChatClient takes credential: TokenCredential | AsyncTokenCredential — it has no
api_key parameter, so a key cannot be substituted. It also calls agents/write on the
project, which Owner does not grant because that is a data action, not a control action.
# Entra: project endpoint
from agent_framework.foundry import FoundryChatClient
client = FoundryChatClient(
project_endpoint=settings.foundry_project_endpoint,
model=config.model.deployment,
credential=credential,
)
# API key only: resource endpoint
from agent_framework.openai import OpenAIChatClient
client = OpenAIChatClient(
config.model.deployment,
api_key=settings.foundry_api_key,
base_url=settings.foundry_openai_base_url, # <resource>/openai/v1/
)
The key path must use a base_url ending in /openai/v1/. Agent Framework calls the Responses
API, and pairing azure_endpoint= with a dated api_version fails with
400 API version not supported. Derive the resource endpoint by stripping /api/projects/...
from the project endpoint. Credentials accepted by the VoiceLive websocket are owned by
voicelive-realtime.
What ships with it
3 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 · 214 lines · 77 tokens per session scan A 2247f4e5c544
maf-foundry-agent is a skill published in the GitHub repository JinLee794/agent-framework-skills (2 stars, last pushed 1mo ago), licensed MIT. It adds 77 tokens to every session and 2,015 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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