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Ultra-Sharp Agent Skills — a research-first skill-authoring system + 72 production-ready skills for AI agents.

Python 100%

# Report Format — CSV Data-Quality Profile

Render the profiler's JSON into exactly this Markdown structure. Do not add, remove, or reorder sections.

markdown
# Data Profile: <file name>

**Verdict:** <one of the three verdict lines below>

## Overview
| Metric | Value |
|---|---|
| Rows profiled | <rows> (+ " (truncated at 100,000)" if truncated) |
| Columns | <number of columns> |
| Duplicate rows | <duplicate_rows> |
| Ragged rows | <ragged_rows> |

## Columns
| Column | Type | Nulls | Unique | Stats |
|---|---|---|---|---|
| <name> | <type> | <nulls> | <unique> | <stats cell — see rule below> |

## Issues
- <one bullet per detected issue; write "None detected." if empty>

# Cell and verdict rules

Stats cell:

  • numeric columns → min=<min>, max=<max>, mean=<mean>, median=<median> plus , outliers=<n> when the key is present and > 0
  • string columns → top: <value> (<count>), <value> (<count>), ... from top_values
  • boolean/date/empty columns →

Issue bullets (include each only when its condition is true):

  • nulls > 0 in a column → "<column> has missing values (%)"
  • duplicate_rows > 0 → " duplicate rows"
  • ragged_rows > 0 → " rows have an inconsistent number of fields"
  • outliers present and > 0 → "<column> has outliers (>3σ from mean)"
  • encoding_fallback is true → "file is not valid UTF-8; profiled using latin-1 fallback"
  • truncated is true → "profile limited to the first 100,000 rows"
  • rows == 0 → "file contains no data rows"

Verdict line (pick exactly one, in this priority order):

  1. rows == 0 OR ragged_rows > 0⚠️ Needs attention — <short reason>
  2. any other issue bullet present → 🟡 Usable with caveats — <n> issue(s) found
  3. no issue bullets → ✅ Clean — no data-quality issues detected