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 agent-callbacksgit 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/agent-callbacks)<a href="https://agentmods.dev/skills/nicolailassen/orxhestra/agent-callbacks"><img src="https://agentmods.dev/badge/skills/nicolailassen/orxhestra/agent-callbacks/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/agent-callbacks"><img src="https://agentmods.dev/badge/skills/nicolailassen/orxhestra/agent-callbacks.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 | $0.00028 | $0.00627 |
| Opus 5 | $0.00014 | $0.00313 |
| Sonnet 5 | $0.00006 | $0.00125 |
| Haiku 4.5 | $0.00003 | $0.00063 |
Grade A, and why
agent-callbacks 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Callbacks
LlmAgent supports before/after hooks at the model and tool level.
Model callbacks
from orxhestra import LlmAgent, Context
from orxhestra.models.llm_request import LlmRequest
from orxhestra.models.llm_response import LlmResponse
async def log_before_model(ctx: Context, request: LlmRequest) -> None:
print(f"Calling LLM with {len(request.messages)} messages")
print(f"Tools available: {[t.name for t in request.tools]}")
async def log_after_model(ctx: Context, response: LlmResponse) -> None:
print(f"LLM responded: {response.text[:100]}")
if response.has_tool_calls:
print(f"Tool calls: {[tc['name'] for tc in response.tool_calls]}")
async def handle_error(
ctx: Context, request: LlmRequest, error: Exception
) -> LlmResponse | None:
print(f"LLM error: {error}")
return None # push error event; or return LlmResponse to recover
agent = LlmAgent(
name="monitored",
model=model,
before_model_callback=log_before_model,
after_model_callback=log_after_model,
on_model_error_callback=handle_error,
)
Tool callbacks
from typing import Any
async def log_tool_start(ctx: Context, tool_name: str, tool_args: dict) -> None:
print(f"Calling tool: {tool_name}({tool_args})")
async def log_tool_end(ctx: Context, tool_name: str, result: Any) -> None:
print(f"Tool {tool_name} returned: {str(result)[:100]}")
agent = LlmAgent(
name="tracked",
model=model,
tools=[search],
before_tool_callback=log_tool_start,
after_tool_callback=log_tool_end,
)
AgentTool callbacks
Intercept events from child agents when using AgentTool.
from orxhestra.tools import AgentTool
def before_agent(ctx, agent):
print(f"Delegating to sub-agent: {agent.name}")
def after_agent(ctx, agent, events):
print(f"Sub-agent {agent.name} produced {len(events)} events")
tool = AgentTool(
agent=researcher,
before_agent_callback=before_agent,
after_agent_callback=after_agent,
)
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 · 96 lines · 28 tokens per session scan A 2359a2858aa4
agent-callbacks is a skill published in the GitHub repository NicolaiLassen/orxhestra (21 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 28 tokens to every session and 627 once invoked, about $0.0001 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.
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