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 kennyzir/7deer_skills --skill python-agent-enginegit clone --depth 1 https://github.com/kennyzir/7deer_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/kennyzir/7deer_skills/python-agent-engine)<a href="https://agentmods.dev/skills/kennyzir/7deer_skills/python-agent-engine"><img src="https://agentmods.dev/badge/skills/kennyzir/7deer_skills/python-agent-engine/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/kennyzir/7deer_skills/python-agent-engine"><img src="https://agentmods.dev/badge/skills/kennyzir/7deer_skills/python-agent-engine.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 26 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00407 |
| Opus 5.5 | $0.00011 | $0.00163 |
| Sonnet 5.5 | $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 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.
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.
- 1mo 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 (325 stars, last pushed 19d ago), licensed MIT. It adds 28 tokens to every session and 407 once invoked, about $0.0001 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 2026-08-30.
Other skills, from other repositories
port-sacred-terminal-ui-to-python
Take a React Window.tsx (or any sacred component) and produce a terminal CLI screen written in Python that uses Simulacrum's Python mirror in scripts/python/sacredcli/. Simulacrum is the sacred CLI framework — the JavaScript half lives in scripts/cli/lib/, the Python half is a one-to-one snakecase mirror.
python
The Vovk.ts Python client — generating a typed Python package from a Vovk API with vovk-python, the py / pySrc templates, TypedDict shapes, JSON + JSON Lines consumption, client-side validation, and PyPI publishing. Use whenever the user wants a typed Python SDK, ongoing Python integration, or PyPI publishing …
openai-agents-sdk
OpenAI Agents SDK (Python) development. Use when building AI agents, multi-agent handoffs, function tools, guardrails, sessions, streaming, or tracing with the openai-agents / agents Python package — including Azure OpenAI via LiteLLM. Triggers on imports from agents, uses of Runner.runsync/Runner.runstreamed…
pennylane
Builds and differentiates PennyLane quantum circuits, hybrid PyTorch or JAX models, molecular VQE and QAOA workflows. Use for variational quantum algorithms, quantum machine learning, simulator validation, and moving validated circuits to provider plugins. For hardware-specific compilation use qiskit or cirq; for…
rocm-kernels
Provides guidance for writing and benchmarking optimized Triton kernels for AMD GPUs (MI355X, R9700) on ROCm, targeting HuggingFace diffusers (LTX-Video, SD3, FLUX) and transformers. Core kernels: RMSNorm, RoPE 3D, GEGLU, AdaLN. Includes XCD swizzle, autotune, diffusers integration patterns, and LTX-Video pipeline…
holoscan-install-wheel
Install Holoscan SDK Python wheel via pip into a venv. Use for Python installs; not for native C++/apt or Conda installs.