# Data Analysis

> Compute and verify descriptive statistics, trends, and qualified anomalies.

- Skill: `qredence/data-analysis` (Agent Skill)
- Install (CLI): `npx skillmds@latest add qredence/data-analysis`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qredence/data-analysis/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: Qredence (https://skillmd.com/u/qredence)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/qredence/data-analysis

---


# Data analysis

Use Python for deterministic calculations over data supplied in the request or
through an authorized Attachment. Do not use host paths, `open()`, shell
commands, or invented Attachment IDs.

## Prepare the data

1. Inspect the supplied schema, column types, row count, missing values,
   duplicates, and parse failures before computing results.
2. For an Attachment, call `read_attachment(attachment_id=...)` only with an
   ID present in the Turn's Attachment metadata. Require `ok: true`; use
   `content` when `encoding` is `utf-8`, and decode `content_base64` in memory
   when `encoding` is `base64`.
3. Keep the source and derived values in the existing Python interpreter. Do
   not treat a Python-local file as a durable Workspace file or Artifact.

## Analyze and verify

Compute only the requested metrics. State the statistical convention for every
ambiguous result, such as sample versus population dispersion, denominator,
time window, rounding, or missing-value treatment.

For anomaly claims:

1. State the rule used.
2. Report the relevant comparison value.
3. Do not call a point anomalous when it does not cross the rule.
4. For very small samples, qualify the conclusion and prefer descriptive
   language.

Before submitting, recompute and compare all reported counts, extrema, sums,
means, medians, dispersion values, and anomaly comparisons against the original
data. If the data is insufficient, say what is missing instead of guessing;
use empty `metrics` and `anomalies` lists when appropriate.

## Submit

Use the exact fields accepted by the current `SUBMIT` binding and do not add
unrecognized fields. When the explicitly selected `data-analysis` schema is
active, submit exactly:

```python
SUBMIT(
    answer=answer,
    findings=findings,
    metrics=metrics,
    anomalies=anomalies,
)
```

Keep `answer` as a non-empty string, `findings` and `anomalies` as string lists,
and `metrics` as a list of objects with a stable `name` and `value` (plus a
unit or convention when useful). When the default Fleet Signature is active,
submit only its required `answer` field.