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 NicolaiLassen/orxhestra --skill yaml-composergit clone --depth 1 https://github.com/NicolaiLassen/orxhestraWrote 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/nicolailassen/orxhestra/yaml-composer)<a href="https://agentmods.dev/skills/nicolailassen/orxhestra/yaml-composer"><img src="https://agentmods.dev/badge/skills/nicolailassen/orxhestra/yaml-composer/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/nicolailassen/orxhestra/yaml-composer"><img src="https://agentmods.dev/badge/skills/nicolailassen/orxhestra/yaml-composer.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 | $0.00034 | $0.00709 |
| Opus 5 | $0.00017 | $0.00354 |
| Sonnet 5 | $0.00007 | $0.00142 |
| Haiku 4.5 | $0.00003 | $0.00071 |
Grade A, and why
yaml-composer 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 11d 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 — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.
YAML Composer
Build an entire multi-agent setup from a single YAML file.
pip install orxhestra[composer]
Minimal example
defaults:
model:
provider: openai
name: gpt-5.4
agents:
assistant:
type: llm
instructions: "You are a helpful assistant."
main_agent: assistant
from orxhestra.composer import Composer
agent = Composer.from_yaml("orx.yaml")
async for event in agent.astream("Hello"):
print(event.text)
Named models
models:
smart:
provider: anthropic
name: claude-opus-4-7
max_tokens: 8192
fast:
provider: openai
name: gpt-5.4-mini
agents:
researcher:
type: llm
model: smart
writer:
type: llm
model: fast
Extra keys on a model config are forwarded directly to the LangChain model constructor.
Tools
tools:
search:
function: "myapp.tools.search_web"
weather:
mcp:
url: "http://localhost:8001/mcp"
exit:
builtin: "exit_loop"
agents:
agent:
type: llm
tools:
- search
- weather
- function: "myapp.tools.inline_tool" # inline tool def
Agent types
llm— LlmAgentreact— ReActAgentsequential— SequentialAgent (runs agents in order)parallel— ParallelAgent (runs agents concurrently)loop— LoopAgent (repeats until exit_loop or max_iterations)a2a— A2AAgent (remote agent via A2A protocol)
Multi-agent with transfer
agents:
triage:
type: llm
instructions: "Route to the right specialist."
tools:
- transfer:
targets: [sales, support]
sales:
type: llm
instructions: "Handle sales inquiries."
support:
type: llm
instructions: "Handle support tickets."
main_agent: triage
Sequential pipeline
agents:
researcher:
type: llm
tools: [search]
writer:
type: llm
pipeline:
type: sequential
agents: [researcher, writer]
main_agent: pipeline
Runner and Server
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.
- 11d ago First seen · 148 lines · 34 tokens per session scan A cbbc44fba8d5
yaml-composer is a skill published in the GitHub repository NicolaiLassen/orxhestra (21 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 34 tokens to every session and 709 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 2026-08-30.
Other skills, from other repositories
ampersend
Give an agent a way to pay for things on the internet. Use when the user wants the agent to be able to pay for things online, when an HTTP call returns 402 Payment Required, when calling an endpoint that charges per request, when the user names a capability they want without a specific URL in mind, or when the user is…
unit-converter
Converts values between metric and imperial units, using the project's agreed factors.
agent-framework-py-release
Use when cutting a Python release for the microsoft/agent-framework monorepo. Triggers on "bump py versions", "cut a python release", "prepare release PR for python", "release py packages", "bump python to X.Y.Z", or similar requests to bump Python package versions and prepare a release PR. Handles all four lifecycle…
foundry-hosted-agent-validation
Step-by-step process for validating a Python Foundry hosted agent sample (under python/samples/04-hosting/foundry-hosted-agents/) end to end — running it locally (native runtime and azd ai agent run) and after deploying it to an Azure AI Foundry project with azd. Use this when asked to validate a hosted agent sample.
build-and-test
How to build and test .NET projects in the Agent Framework repository. Use this when verifying or testing changes.
python-feature-lifecycle
Guidance for package and feature lifecycle in the Agent Framework Python codebase, including stage meanings, feature-stage decorators, feature enums, and how to move APIs from one stage to the next.