# 30-in-15 · #12 — CSV → Insights Analyst (plan)

**Rung focus:** code execution + a data agent (vs. #11's long-running service).
**One-liner:** Upload a CSV → get auto-generated charts + plain-English findings you can trust.

## Core principle (honest by construction)
The LLM **never invents numbers.** A deterministic profiling pass (pandas) computes every stat and
chart; the model only *narrates* the already-computed summary. Numbers in the writeup are grounded in
the computed profile, not hallucinated. (Mirrors the #10 RAG "honest, cited" discipline.)

## v1 scope (lean)
1. **Upload** a CSV (size-capped, typed sniff).
2. **Profile** (pandas): shape, dtypes, missing %, numeric describe, top categoricals, correlations,
   simple outlier + trend flags.
3. **Auto-charts** (matplotlib, GritAI design-system palette): distributions, top categories,
   correlation heatmap, and a time trend if a date column is detected.
4. **Narrative**: LLM turns the computed profile into a plain-English "what's interesting here" —
   grounded in the numbers, flags data-quality issues, suggests next questions.
5. **Ask-a-question** (stretch): natural-language question → constrained pandas op executed on the
   dataframe → answer + the exact numbers (the "data agent / code execution" angle).

## Stack
- Flask + the canonical GritAI design system (`docs/gritai_design_system.css`), same as the Flask rungs.
- pandas + matplotlib (stdlib-adjacent, no heavy deps).
- Backend LLM: local Ollama (free) by default; Claude optional — same env-var pattern as the bot.
- Deploy: HF docker Space per [[hf-spaces-deploy-playbook]] → `insights.gritai.solutions` on the hub.
- Two-tier: free demo (funnel) / paid "GritAI Studio" full stack.

## Validate on REAL data (Rob's rule)
Build + tune against a real CSV Rob provides so the demo isn't synthetic. Fallback: a solid public
dataset, swap Rob's in later.

## Safety
Same secrets hygiene (env-only keys, log exception TYPE only). Uploaded data stays local/ephemeral;
never logged, never sent anywhere except the chosen LLM backend (local by default).