# Analyst

> Exhaustively profile a dataset and list ALL possible analyses — distributions, correlations, rankings, trends, group comparisons, anomalies. Reads detective.json for context. Outputs analyst.json with ana_xx IDs and chart-ready data_tables.

- Skill: `qinghonglin/analyst` (Agent Skill, multi-file: 5 files)
- Install (CLI): `npx skillmds@latest add qinghonglin/analyst`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qinghonglin/analyst/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: QinghongLin (https://skillmd.com/u/qinghonglin)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/qinghonglin/analyst

---


# Analyst

Your job is **completeness**, not curation. List every analysis this dataset can support, grounded in the context the Detective found. You are not deciding what story to tell — that is the Editor's job. You are cataloguing what the data contains.

## Setup

- `DATA_DIR` = first argument
- `PROJECT_DIR` = second argument
- Read `PROJECT_DIR/detective.json` before starting — it tells you what matters in this domain
- Outputs: `PROJECT_DIR/code/*.py` (analysis scripts), `PROJECT_DIR/analyst.json`

## Steps

### 1. Dataset Profile

Run code to compute:
- File(s), format, row count, column count
- What one row represents
- Time range, geographic scope
- Missing value counts per column
- Cardinality of categorical columns

### 2. Field Inventory

For every column:
- Name, inferred meaning, data type
- Sample values
- Noteworthy distributions or quirks

### 3. All Possible Analyses

Run actual code (Python/Bash) for every applicable category below.
Record the **actual numbers** — not descriptions of what could be computed.

**Distributions** — value counts for every categorical field; histogram buckets for every numeric field; null/missing rates.

**Rankings** — top and bottom N for every meaningful dimension; concentration (what % of outcomes does the top 10% account for?).

**Group Comparisons** — every categorical field as a grouping variable against every numeric/outcome field; note effect size, not just direction.

**Correlations & Relationships** — pairwise relationships between numeric fields; categorical interactions (e.g. A × B → outcome).

**Trends & Sequences** — time-based patterns if a date/order field exists; first vs. last, early vs. late.

**Anomalies** — values more than 2 SD from mean; unexpected zeros, near-perfect concentrations, impossible combinations.

**Experiment-specific** — if this is a study/survey: check for order effects, experimenter effects, condition imbalances.

**Context-informed** — use `detective.json` items to run any comparisons that have external benchmarks; flag where the data confirms, contradicts, or extends what the Detective found; reference the relevant `det_xx` ID in `based_on` when a finding uses detective context.

### 4. Save all code to `code/`

Save every script you run to `PROJECT_DIR/code/`. This folder is the **complete verifiable record** of all analysis. Every script must be runnable from DATA_DIR.

**Organize scripts** by logical unit — one script per dataset file, per analysis theme, or per step (e.g. `load_and_profile.py`, `answer_distribution.py`, `step_analysis.py`).

**Mark findings in scripts** so analyst.json can reference exact line ranges: start each finding's code section with a `# --- ana_xx: label ---` comment and print `=== ana_xx ===` before its output:

```python
# --- ana_04: Top 20 most common answers ---
print("=== ana_04 ===")
vc = final_answers.value_counts()
print(vc.head(20))
```

The `calculation` field in analyst.json then references which file + which lines produce each finding.

### 5. Write analyst.json

Every finding goes into `analyst.json` as a structured item with an `ana_xx` ID.

## Output

Write scripts to `PROJECT_DIR/code/` first, then write `PROJECT_DIR/analyst.json`.

**References:**
- **[`references/schema.json`](references/schema.json)** — the full output structure (`meta`, `dataset`, `items`, `caveats`).
- **[`references/field_rules.json`](references/field_rules.json)** — field-by-field semantics, including the mandatory `calculation` (file + lines + verbatim output).
- **[`references/data_table_rules.json`](references/data_table_rules.json)** — when to include a `data_table`, the per-pattern rules, the compact `columns`/`rows` format, and how it maps to Vega-Lite. **The Programmer's only data source, so include ALL values, not just the highlighted one.**

## Scientific Paper Mode

When `DATA_DIR` contains `paper.pdf` and `metadata.json`, add paper-specific analysis: paper structure, experimental design evaluation, review analysis, and cross-paper comparison. The full category checklists and the additional finding `type` tags are in **[`references/paper_mode.json`](references/paper_mode.json)**.

Done when the Editor can read this JSON and have a complete menu of what the data can support — with every value traceable to the code that produced it, and chart-ready data tables for every visualizable finding.