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 agentmods add skills/kennyzir/7deer_skills/python-agent-enginenpx skills add kennyzir/7deer_skills --skill python-agent-enginegit clone --depth 1 https://github.com/kennyzir/7deer_skillsWhat 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 | $0.00028 | $0.00407 |
| Opus 5 | $0.00014 | $0.00204 |
| Sonnet 5 | $0.00006 | $0.00081 |
| Haiku 4.5 | $0.00003 | $0.00041 |
Grade A, and why
python-agent-engine 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 2d 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.
What it actually says
Python Agent Engine
A plug-and-play AI Agent core for Python applications. It handles the complexity of LLM interaction, tool calling loops, and context management.
Features
- ReAct Loop: Automatically handles "Reasoning -> Tool Call -> Result -> Answer" process.
- Thinking Process: Returns structured "Thinking Steps" for UI visualization.
- Model Agnostic: Works with OpenAI, DeepSeek, or any OpenAI-compatible API.
Installation
- Copy
resources/agent_engine.pyto your project (e.g.,src/core/agent_engine.py). - Install dependencies:
pip install langchain-core langchain-openai python-dotenv - Set Environment Variables in your
.envfile:OPENAI_API_KEY=sk-... # Optional: OPENAI_BASE_URL=https://api.openai.com/v1
Usage Example
import asyncio
from langchain_core.tools import tool
from core.agent_engine import AgentEngine
# 1. Define Tools
@tool
def calculator(expression: str) -> str:
"""Calculates a math expression."""
return str(eval(expression))
# 2. Initialize Agent
agent = AgentEngine(
tools=[calculator],
system_prompt="You are a helpful math assistant.",
model_name="gpt-4o"
)
# 3. Chat
async def main():
response = await agent.chat("What is 123 * 456?")
print(f"Answer: {response.content}")
print("\nThinking Steps:")
for step in response.thinking_steps:
print(f"[{step.type}] {step.content}")
if __name__ == "__main__":
asyncio.run(main())
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.
- 2d ago First seen · 61 lines · 28 tokens per session scan A f196fcca75e0
python-agent-engine is a skill published in the GitHub repository kennyzir/7deer_skills (295 stars, last pushed 4d ago), licensed MIT. It adds 28 tokens to every session and 407 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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