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/ccivlcid/vibeharness/python-stacknpx skills add ccivlcid/VibeHarness --skill python-stackgit clone --depth 1 https://github.com/ccivlcid/VibeHarnessWhat 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 | $0.00064 | $0.00607 |
| Opus 5 | $0.00032 | $0.00303 |
| Sonnet 5 | $0.00013 | $0.00121 |
| Haiku 4.5 | $0.00006 | $0.00061 |
Grade A, and why
python-stack 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 yesterday.
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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python — AI, Data, Automation
Do not apply to Node.js web-only projects. Verify stack matches
PROJECT_RULES.md. Record architecture decisions indocs/ai-dev/.
Stack by Project Type
| Type | Stack | Install |
|---|---|---|
| AI/RAG/Chatbot | FastAPI + LangChain + ChromaDB | pip install fastapi uvicorn langchain openai chromadb |
| Data analysis | Streamlit + Pandas + Plotly | pip install streamlit pandas plotly |
| API server | FastAPI + SQLAlchemy | pip install fastapi uvicorn sqlalchemy |
| Automation | BeautifulSoup/Playwright | pip install beautifulsoup4 requests playwright |
Initial Setup
python -m venv venv
# Mac/Linux:
source venv/bin/activate
# Windows:
venv\Scripts\activate
pip install -r requirements.txt
Always provide the correct activation command for the user's OS.
FastAPI Pattern
from fastapi import FastAPI
from fastapi.staticfiles import StaticFiles
app = FastAPI()
app.mount("/static", StaticFiles(directory="static"), name="static")
@app.get("/api/hello")
async def hello():
return {"message": "Hello!"}
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000)
Streamlit Pattern
import streamlit as st
import pandas as pd
st.title("Dashboard")
uploaded = st.file_uploader("Upload CSV", type="csv")
if uploaded:
df = pd.read_csv(uploaded)
st.dataframe(df)
st.bar_chart(df)
SQLite + SQLAlchemy
from sqlalchemy import create_engine, Column, String, DateTime
from sqlalchemy.orm import declarative_base, sessionmaker
engine = create_engine("sqlite:///dev.db")
Base = declarative_base()
SessionLocal = sessionmaker(bind=engine)
Environment Variables
from dotenv import load_dotenv
import os
load_dotenv()
api_key = os.getenv("OPENAI_API_KEY")
Code Conventions
- snake_case (functions, variables), PascalCase (classes)
- Type hints required:
def get_user(user_id: str) -> User: - f-strings:
f"Hello {name}" - Pin versions in requirements.txt
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.
- yesterday First seen · 97 lines · 64 tokens per session scan A 86e291fc51bf
python-stack is a skill published in the GitHub repository ccivlcid/VibeHarness (5 stars, last pushed 4mo ago), licensed MIT. It adds 64 tokens to every session and 607 once invoked, about $0.0003 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 2026-08-31.
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