Forem: Matheus D. Santos

Data Analyses — Wizard

Matheus D. Santos — Sun, 28 Sep 2025 22:47:01 +0000

This is a submission for the KendoReact Free Components Challenge.

Data Analyses — Automate Wizard (KendoReact Challenge submission)

What I Built

I built Data Analyses — Automate Wizard, a small React app that helps users import tabular files (CSV/XLSX), automatically analyzes the dataset, and generates polished, accessible charts and dashboard cards using KendoReact Free Components.

The app’s goal is to let non-technical users get immediate insights from an uploaded spreadsheet:

auto-detects numeric / date columns,
suggests the most useful charts and small dashboard cards (totals, averages, top categories),
aggregates and prepares data for charts (bar, line, pie, donut, area),
offers a Chart Wizard for manual column-to-chart mapping,
accepts exported charts (PDF, PNG, SVG) and lets users pin them to the dashboard.

I focused on usability (one-click suggestions + preview), accessibility, and making the analysis pipeline safe (AI only receives a compact column summary / sample, not full PII).

Demo

Demo video (walkthrough): https://www.loom.com/share/1382adaea79e41e09406bc76caecb3a7?sid=6bc8ff72-ec35-4f9a-b8fa-a67471f236c4
Source code (GitHub): https://github.com/MatheusDSantossi/data-analyses-automate

Screenshots and the Loom video are in the repo README. The video demonstrates uploading a CSV, the AI suggestions panel, auto-generated charts/cards, and the Chart Wizard flow.

Key features

Upload CSV / XLSX files and preview sample rows.
Automatic dataset analysis (sample-based) using a generative AI assistant (Gemini) that recommends charts and cards.
Auto-aggregation helpers (sum/avg/count, time-series aggregation with month-year/year granularity).
Interactive Chart Wizard integration so users can pick fields and build charts manually if desired.
Chart gallery: Bar, Line, Area, Pie, Donut (all generated from the same pipeline).
Upload/export support for images/PDFs (pin exported charts to dashboard).
Skeletons + progressive UI while parsing/aggregating.
Lightweight theme scoping so Kendo styling is applied only where needed.

How I used KendoReact components

I used multiple free KendoReact components to build the UI and charts. Key components used in the project:

Input — file picker UI (from @progress/kendo-react-inputs)
Chart and chart subcomponents (from @progress/kendo-react-charts):
- Chart (root)
- ChartSeries / ChartSeriesItem
- ChartCategoryAxis / ChartCategoryAxisItem
- ChartValueAxis / ChartValueAxisItem
- ChartTitle
- ChartSeriesLabels
- ChartTooltip / ChartNoDataOverlay
ChartWizard (from @progress/kendo-react-chart-wizard) — interactive mapping wizard for users to create charts from table data
Skeleton (from @progress/kendo-react-indicators) — loading placeholders while files parse
Tooltip (from @progress/kendo-react-tooltip) — contextual help and quick actions
(plus some Kendo “intl” helpers where needed for formatting)

Note: many Chart subcomponents are imported from the charts package (e.g., ChartSeriesItem, ChartCategoryAxisItem, etc.). Together they provide the full charting capabilities used across the app.

AI / “Code Smarter, Not Harder” notes

I used Google Generative AI (Gemini) via a small client wrapper to analyze a sample of the uploaded data (column names + a few sample values and summary stats). The prompt instructs the model to return JSON only with:
- recommended charts (type, groupBy, metric, aggregation, topN, granularity)
- recommended small dashboard cards (specs only: field, aggregation, label, format).
The frontend validates the AI response, then computes actual numbers locally (e.g., sums/averages/top categories) to avoid trusting the model for numeric calculations and to keep the source-of-truth on the client data.
This approach lets the AI do lightweight analysis and suggestions while the app remains in control of real computations (privacy and correctness).

(If you prefer, this submission is not entered for the Kendo AI Coding Assistant prize — I used Gemini instead. No Nuclia RAG integration is included.)

How to run locally

# clone
git clone https://github.com/MatheusDSantossi/data-analyses-automate.git
cd data-analyses-automate

# install
npm install

# start dev server
npm run dev
# open http://localhost:5173 (Vite default)

Environment

Add your Gemini key to .env:

VITE_GEMINI_API_KEY=<your-gemini-key>

Notes

The app uses ExcelJS to read xlsx files and a CSV parser helper for CSV. It also uses Kendo’s default theme — I included an optional scoped theme option so Kendo styles only apply inside the dashboard container.
For the AI assistant I only send column summaries (no raw rows). If you plan to demo with real data containing PII, please exercise caution.

Judging criteria mapping

Use of underlying technology — Chart/wizard powered by KendoReact Charts & ChartWizard; Kendo components used for inputs/UX; dataset parsing with ExcelJS; AI-assisted recommendations.
Usability & User Experience — immediate suggestions, skeletons while parsing, Chart Wizard for manual adjustments, export previews (PDF/Image), responsive grid layout.
Accessibility — charts have labels and legends, skeletons are presentational only, and UI controls use semantic elements (and aria-* where appropriate).
Creativity — combining a generative model to suggest charts/cards + a Chart Wizard for manual exploration makes analysis quick while preserving user control.

Files of interest

src/pages/Dashboard.tsx — main dashboard, AI integration, aggregation orchestration, generated-charts renderer
src/utils/wizardData.ts — helpers to transform rows into wizard-ready { field, value } arrays and aggregation helpers
src/utils/aiAnalysis.ts — build prompt, call AI wrapper (getResponseForGivenPrompt) and safe JSON parsing
src/components/charts/* — BarChart, LineChart, DonutChart, PieChart, AreaChart components (Kendo-powered)
src/components/Wizard.tsx — ChartWizard integration

Final notes & links

GitHub: https://github.com/MatheusDSantossi/data-analyses-automate
Demo video (walkthrough): https://www.loom.com/share/1382adaea79e41e09406bc76caecb3a7?sid=6bc8ff72-ec35-4f9a-b8fa-a67471f236c4

n8n daily email insight generator

Matheus D. Santos — Mon, 01 Sep 2025 02:31:53 +0000

n8n Daily Email Insight Generator — Real-Time Triage & Bright Data Enrichment

Submission for the AI Agents Challenge powered by n8n and Bright Data
Owner: Matheus Santos — Version 0.2
Timeline: Aug 24, 2025 → Aug 31, 2025
Status: Completed (prototype)

Overview

I built an n8n workflow that automatically ingests new emails, classifies and prioritizes them with an LLM (Gemini), enriches results using Bright Data Marketplace datasets, and writes concise, actionable entries into a Google Sheet. The workflow runs on a schedule (three times/day) and produces an “email insights” sheet that teams can use for triage, tracking, and follow-up.

What I built

A production-style automation that:

pulls emails via IMAP,
cleans and normalizes message payloads,
classifies topic & priority with a chat model,
enriches classification with Bright Data Marketplace dataset lookups,
merges and finalizes the enriched output with an LLM pass,
and writes tasks, dataset evidence, and actions into Google Sheets (create/update/append).

Key requirement satisfied: the workflow uses n8n’s AI/Chat model nodes and Bright Data Verified Node(s) (both used as part of the enrichment & toolchain).

Live demo & workflow

n8n workflow JSON: /workflows/email-priority-agent.json

If you want to try it quickly: import the workflow JSON into your n8n instance, configure IMAP, Bright Data, Gemini, and Google Sheets credentials, then run a test email.

Why this is useful

Saves time by turning inbox noise into action items and evidence.
Surface business-critical mail (investment, billing, client requests) with a consistent triage policy.
Provides provenance: Bright Data dataset matches are logged so you can verify sender/company context.
Easily extensible: add more enrichment sources, task integrations (Asana/Trello), or dashboards.

Architecture (high level)

[Email Provider/IMAP Trigger]
        ↓
[Email Cleaning Function]  (extract snippet, from, to, date, subject)
        ↓
[Chat Model: Classify (Gemini)] → (validate & clean)
        ↓
[Branch A: Prepare Bright Data queries]
        ↓
[Bright Data: List Datasets / Marketplace Lookups] → [Clean results]
        ↓
[Merge: classification + enrichment]
        ↓
[Chat Model: Final enrichment / summarize (Gemini) — may call Bright Data as a tool]
        ↓
[Final Cleaning function]
        ↓
[Chat Model → Google Sheets op generator]
        ↓
[Google Sheets: Tasks, DatasetMatches, EmailActions]

Technical implementation

IMAP Email Trigger — watches inbox for new messages.
Function node — Email Content Cleaning — normalizes the raw payload to { content, From, To, subject, dates }.
Chat model (Gemini) — Categorize — prompt classifies the email into one of the categories and assigns priority (High / Medium / Low). Output is strict JSON (so downstream parsing is reliable).
Output cleaning — function removes fences/extra characters from model output.
Decision & enrichment prep — an LLM checks whether the classification has enough signals; if not, it suggests Bright Data queries (domains, keywords).
Bright Data — Marketplace (list datasets) — uses suggested queries to retrieve datasets to validate or enrich classification.
Enrichment cleaning — function strips unnecessary fields (IDs) and normalizes dataset objects.
Merge — combines classification JSON with dataset results.
Final summarization (Gemini) — produces final summary, recommended actions, and Google Sheets tasks.
Google Sheets operations — a final chat model node or function maps final JSON to append/update rows in Tasks, DatasetMatches, and EmailActions.

Example of what the workflow writes (example JSON)

{
  "category": "Investment",
  "priority": "High",
  "confidence": 0.92,
  "summary": "OLITEF 2025 registration closing soon — investment education program from Tesouro Direto; consider applying before deadline.",
  "reasons": ["sender domain: tesourodireto.com", "contains 'Inscrições' and deadline-like phrase"],
  "suggested_enrichments": [
    { "type": "domain_lookup", "query": "tesourodireto.com", "purpose": "verify official sender" },
    { "type": "web_search", "query": "OLITEF 2025 Tesouro Direto inscrições", "purpose": "confirm deadline" }
  ],
  "actions": [
    { "type":"label","detail":"Investment,High","urgency":"immediate" },
    { "type":"create_task","detail":"Review OLITEF registration page","urgency":"routine" }
  ]
}

Bright Data usage (what I did)

Marketplace / list datasets — query for company profiles, news, and web snapshots that match inferred domains/keywords from emails.
Tool usage inside LLM — the LLM is allowed to request Bright Data as a verification tool for low-confidence or Investment-class emails.
Why it matters: Bright Data adds real-time web signals (company pages, public announcements) to reduce false positives and raise model confidence.

How to reproduce / run locally

Clone repo and import /workflows/n8n Email categorize automation.json.
Add credentials: IMAP, Gemini (or other LLM), Bright Data (verified node), Google Sheets, Slack (optional).
Adjust Priority Rules (JSON/Set node) to include your high-priority senders & keywords.
Run the workflow manually or enable schedule (every 8 hours → 3×/day) and test with sample messages.
Export workflow JSON & Gist when submitting.

Challenges & learnings

Tuning prompts is critical — very large prompts or mixed content sometimes returned noisy output; I solved that by enforcing strict JSON outputs and adding cleaning nodes.
Bright Data Marketplace required trial & exploration — choosing the right dataset filters matters (domain-first approach helps).
Doing this challenge was incredible, it was my first time using n8n and bright data and I couldn't be more impressed with the power of these tools together. One of the challenges I faced was using the Chat Model nodes, sometimes I asked too much or send a big prompt, and they didn't return useful insight or output. Another challenge was learning how to use bright data, like I said I've used that, so I didn't know what to do at first. :) I am really happy with my project, of course there are things to improve, but it is a great start. I hope this project help someone else, feel free to reach out and talk about it. Not only that, but I love to learn, and that's a stunning topic to talk about!

Links & contact

Repo: repository
Contact: Matheus Santos — feel free to reach out for questions or collaboration!

Automating Project Kickoff: Docs, Timeline & Calendar with Runner H

Matheus D. Santos — Mon, 07 Jul 2025 04:11:40 +0000

This is a submission for the Runner H "AI Agent Prompting" Challenge

What I Built

I created a fully autonomous “project onboarding” workflow using Runner H that takes a plain-text project_info.txt file as input and instantly generates:

Comprehensive Technical Documentation in Google Docs
Structured Development Schedule & Risk Log in Google Sheets (with built‑in Gantt visualization)
Calendar Events for each project phase in Google Calendar

This solves the repetitive, time‑consuming task of setting up new projects, no manual copy‑paste, no toggling between tools. One Runner H run does it all.

Demo

How I Used Runner H

Input parsing

Prepared project_info.txt with fields like Project Name, Description, Tech Stack, Modules, Phases & Owners.
Runner H read and extracted every detail—no further prompts needed.

Docs generation (@tool:Google Docs)

Created a “<Project Name> – Technical Documentation” Doc.
Populated sections (Overview, Installation, Usage, Architecture with Mermaid diagram, API Reference, Changelog seeded from recent Git commits, Contributing Guidelines, Release Checklist).

Schedule & Risk Log (@tool:Google Sheets)

Generated a sheet titled “<Project Name> – Schedule & Risk Log” with two tabs:
- Schedule (Phase, Start/End Date, Owner, Status) plus an automated Gantt chart.
- Risk Log (Risk Description, Severity, Owner, Mitigation Plan) with five AI‑inferred risks.

Calendar events (@tool:Google Calendar)

Created kickoff events for each phase, assigned to the owner, with reminders.

Execution

Pasted one concise prompt into Runner H, pointed it at my text file, and watched the sequence run start‑to‑finish.

Reproducing this workflow

Author your project_info.txt using the structured template below.
In Runner H, paste the prompt (available in my repo or the Loom description).
Point Runner H at your text file—no additional IDs or OAuth steps.
Runner H will spin up Docs, Sheets, and Calendar events automatically.

You are Runner H, an autonomous AI agent.
You have access to a text file named project_info.txt containing all project details.
Extract from that file and:

1. @tool:Google Docs – Create “<Project Name> – Technical Documentation” with full Markdown content:
   • Overview
   • Installation & Setup
   • Usage Examples
   • Architecture & Components (include Mermaid diagram)
   • API / Interface Reference
   • Changelog (seeded from last 5–10 Git commits)
   • Contributing Guidelines
   • Release Checklist

2. @tool:Google Sheets – Create “<Project Name> – Schedule & Risk Log” with two tabs:
   • Schedule: (Phase, Start Date, End Date, Owner, Status) + Gantt chart
   • Risk Log: (Risk Description, Severity, Owner, Mitigation Plan) with 5 AI‑generated risks

3. @tool:Google Calendar – Create kickoff events for each phase with owners and reminders.

When complete, output the URLs for the Doc, Sheet, and Calendar events.

Use Case & Impact

Startup teams can kick off new features or products in seconds.
Consultants & agencies standardize client onboarding without manual overhead.
Open‑source projects ensure up‑to‑date docs and release plans at every commit.

By automating the entire setup—docs, schedule, risk management, and calendar invites—this workflow eliminates error‑prone copy‑pasting, accelerates project launches, and keeps everyone aligned from day one.

Social Love

If you enjoyed this, I’d love your support! I shared it on X here: [https://x.com/MSantos79880/status/1942073796518625785]
Feel free to leave a ❤️ or comment below.

Forem: Matheus D. Santos

Data Analyses — Wizard

Data Analyses — Automate Wizard (KendoReact Challenge submission)

What I Built

Demo

Key features

How I used KendoReact components

AI / “Code Smarter, Not Harder” notes

How to run locally

Environment

Notes

Judging criteria mapping

Files of interest

Final notes & links

n8n daily email insight generator

n8n Daily Email Insight Generator — Real-Time Triage & Bright Data Enrichment

Overview

What I built

Live demo & workflow

Why this is useful

Architecture (high level)

Technical implementation

Example of what the workflow writes (example JSON)

Bright Data usage (what I did)

How to reproduce / run locally

Challenges & learnings

Links & contact

Automating Project Kickoff: Docs, Timeline & Calendar with Runner H

What I Built

Demo

How I Used Runner H

Reproducing this workflow

Use Case & Impact

Social Love

Automating Project Kickoff: Docs, Timeline & Calendar with Runner H

How I Used Runner H