Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/JinLee794/agent-framework-skillsnpx agentmods add skills/jinlee794/agent-framework-skills/maf-agent-configWrote 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-agent-config)<a href="https://agentmods.dev/skills/jinlee794/agent-framework-skills/maf-agent-config"><img src="https://agentmods.dev/badge/skills/jinlee794/agent-framework-skills/maf-agent-config/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-agent-config"><img src="https://agentmods.dev/badge/skills/jinlee794/agent-framework-skills/maf-agent-config.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 | $0.00057 | $0.04318 |
| Opus 5.5 | $0.00023 | $0.01727 |
| Sonnet 5.5 | $0.00011 | $0.00864 |
| Haiku 4.5 | $0.00006 | $0.00432 |
Grade A, and why
maf-agent-config 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 1mo 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 — 370 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent & Voice Configuration — YAML Contract
Behaviour lives in YAML. Python is wiring.
If changing a value would change what the agent says or does — instructions, model, temperature, which tools are attached, the voice, VAD thresholds, interim phrases — it is a YAML value. Python may only load, validate, and construct.
Use the shipped schema, do not invent one
Agent behaviour is written in MAF's own declarative agent schema (kind: Prompt) and
loaded by the shipped AgentFactory from agent-framework-declarative. Do not hand-roll a
private YAML dialect with bespoke pydantic models — that is a reimplementation of
AgentFactory that no other tool, sample, or language runtime can read.
from agent_framework.declarative import AgentFactory
What this buys, and what inventing a dialect costs:
- The same document loads in Python and .NET (
ChatClientPromptAgentFactory). create_agent_from_yaml_path/..._asyncare maintained upstream, including the provider and connection resolution you would otherwise write by hand.- A
kind: Workflowdocument can reference the agent file directly — see maf-multi-agent-workflows.
Declarative agents are experimental upstream (ExperimentalFeature.DECLARATIVE_AGENTS) and
emit an ExperimentalWarning on first use. Filter that warning at the entry point; do not fork
the schema to avoid it.
What goes where
Three homes, no overlap. Most config bugs are a value in the wrong one.
| Value | Home |
|---|---|
| Instructions, model deployment, temperature, tool list, voice, VAD, interim responses | config/**.yaml |
| Endpoints, resource names, credentials, connection strings | env → settings.py |
| Tool implementations, event loop, audio I/O, orchestration | src/ |
settings.py is the only reader of os.environ in src/. Inside a kind: Prompt
document, environment values are referenced with PowerFx =Env.NAME, which AgentFactory
resolves — see Placeholders.
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.
- 1mo ago First seen · 370 lines · 57 tokens per session scan A f94569f96a8c
maf-agent-config is a skill published in the GitHub repository JinLee794/agent-framework-skills (2 stars, last pushed 2mo ago), licensed MIT. It adds 57 tokens to every session and 4,318 once invoked, about $0.0002 per session on Opus 5.5. 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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