explore-data

name

explore-data

description

USE THIS SKILL when the user wants to explore, browse, preview, or understand their data before asking a specific question. Triggers on 'explore', 'browse data', 'what's in this dataset', 'show me the schema', 'what tables do I have', '/explore', '/data', or any request to look at the data structure, preview rows, check distributions, or understand what's available. Also use when a user just connected a new dataset and wants to see what's there. Do NOT skip this skill for data exploration — it includes quality checks and SWD chart standards that produce professional outputs.

Explore Data — Interactive Data Discovery

You are helping the user explore their dataset. Keep it fast, visual, and interactive — this is discovery mode, not full analysis.

Step 0: Load Context

import os, yaml

workspace = os.environ.get('AI_ANALYST_WORKSPACE', '')
if not workspace:
    for d in ['.', './data', '../data']:
        if os.path.isdir(d) and any(f.endswith(('.csv', '.parquet')) for f in os.listdir(d)):
            workspace = os.path.abspath(d)
            break

Load schema from .knowledge/datasets/{active}/schema.md if available. Load quirks from .knowledge/datasets/{active}/quirks.md if available.

If no active dataset, prompt: "No dataset connected. Use /connect-data or point me to your CSV files."

Step 1: Choose Exploration Mode

Mode A: Dataset Overview (no table specified)

• List all tables with row counts and date ranges
• Highlight 3-5 most analytically useful tables
• Show key entities and how they connect
• Suggest 3 starting questions

Mode B: Table Exploration (table specified)

• Column list with types and null rates
• Sample 5 random rows
• Numeric columns: min, max, mean, median
• Categorical columns: top 5 values with counts
• Date columns: range and coverage
• Flag quality issues (>5% nulls, low cardinality)

Mode C: Column Deep-Dive (table + column specified)

• Full distribution chart (histogram or bar chart)
• Null analysis
• Outlier detection (IQR method)
• Suggest related columns for cross-analysis

Step 2: Chart Standards (If Generating Any Visuals)

Even in exploration mode, apply SWD methodology:

import sys
sys.path.insert(0, '<plugin-path>/helpers')
from chart_helpers import swd_style, highlight_bar, action_title, save_chart

• Call swd_style() before every chart
• Background: #F7F6F2
• Use gray for context, amber #D97706 only for notable findings
• Takeaway titles (not generic labels)
• Clean formatting: no rotated text, remove top/right spines

Step 3: Quality Flags

Always highlight data issues:

• 20% nulls → BLOCKER (red flag)

• 5-20% nulls → WARNING
• Very low cardinality (< 3 unique values in expected-high column) → NOTE
• Empty table → BLOCKER
• All-null column → BLOCKER

Step 4: Interactive Follow-Up

After presenting results, offer 2-3 specific next actions:

• "Want to see how {column} varies by {dimension}?"
• "This looks like a good candidate for analysis. Try asking: '{specific question}'"
• "There are quality issues in {column}. Want deeper profiling?"

Step 5: Save Notes

Write brief exploration notes to {workspace}/working/explore_notes_{DATE}.md. These are available for subsequent analysis agents.

Rules

1. Keep it fast — max 3-4 queries per step
2. Never modify data during exploration
3. Always cite table and column names
4. Never dump all questions at once — offer 1-3 focused suggestions