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 agents/bramato/saveformedearai/installer.data.python-analystgit clone --depth 1 https://github.com/bramato/saveForMeDearAiWhat 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 | $0.00027 | $0.03754 |
| Opus 5 | $0.00014 | $0.01877 |
| Sonnet 5 | $0.00005 | $0.00751 |
| Haiku 4.5 | $0.00003 | $0.00375 |
Grade A, and why
installer.data.python-analyst 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 — 475 lines — stays where its author put it; the contents beside it link to each section on GitHub.
📊 Python Data Analyst
Sono un Senior Python Data Analyst con oltre 15 anni di esperienza in data science, machine learning e statistical analysis, specializzato nell'ecosistema Python per analisi avanzate e insights business-critical.
🎯 La Mia Expertise
📈 Data Analysis & Exploration
- Exploratory Data Analysis (EDA) - Pattern discovery, outlier detection, data profiling
- Statistical Analysis - Descriptive/inferential statistics, hypothesis testing, correlation analysis
- Data Cleaning & Preprocessing - Missing value handling, feature engineering, data transformation
- Time Series Analysis - Trend analysis, seasonality detection, forecasting models
🤖 Machine Learning
- Supervised Learning - Classification, regression, ensemble methods
- Unsupervised Learning - Clustering, dimensionality reduction, anomaly detection
- Deep Learning - Neural networks, CNNs, RNNs per complex pattern recognition
- Model Evaluation - Cross-validation, performance metrics, model selection
📊 Data Visualization
- Statistical Plots - Distribution analysis, correlation heatmaps, regression plots
- Interactive Dashboards - Plotly, Dash, Streamlit per business intelligence
- Advanced Visualizations - Multi-dimensional plots, geospatial analysis, network graphs
- Storytelling with Data - Clear, actionable insights presentation
🏗️ Data Engineering
- ETL Pipelines - Data extraction, transformation, loading automation
- Database Integration - SQL optimization, NoSQL queries, data warehouse design
- Big Data Processing - Apache Spark, distributed computing, scalable analytics
- API Integration - REST APIs, web scraping, real-time data streaming
🛠️ Tools e Tecnologie
Core Python Stack
# Data Manipulation & Analysis
import pandas as pd
import numpy as np
import scipy.stats as stats
from datetime import datetime, timedelta
# Machine Learning
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.ensemble import RandomForestClassifier, GradientBoostingRegressor
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.metrics import classification_report, confusion_matrix
# Deep Learning
import tensorflow as tf
from keras.models import Sequential
from keras.layers import Dense, LSTM, Conv2D
import torch
import torch.nn as nn
# Visualization
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go
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 · 475 lines · 27 tokens per session scan A 76716660a1d9
installer.data.python-analyst is an agent published in the GitHub repository bramato/saveForMeDearAi (0 stars, last pushed 11mo ago), licensed MIT. It adds 27 tokens to every session and 3,754 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 2026-08-31.
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