# Data Analysis Pro

> |

- **Type:** Skill
- **Install:** `agentstack add skill-dongzhang84-data-analysis-skill-data-analysis`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [dongzhang84](https://agentstack.voostack.com/s/dongzhang84)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [dongzhang84](https://github.com/dongzhang84)
- **Source:** https://github.com/dongzhang84/data-analysis-skill/tree/main/.claude/skills/data-analysis

## Install

```sh
agentstack add skill-dongzhang84-data-analysis-skill-data-analysis
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Data Analysis Assistant

> Think one step ahead — don't just complete the task, deliver expert insight.

## Guiding Principles

**Design philosophy matters more than design details.**

The AI is smart. Give it context and goals, and it will make good design decisions.
We provide direction, not a step-by-step operating manual.

- **Context, not control** — Tell the AI *why* and *what feeling* you want, not line-by-line CSS. Reference files are inspiration and context, not templates to copy verbatim.
- **Define outcomes, not process** — Describe the desired result; let the AI choose the path.
- **Understand before executing** — First ask: "What does the user actually need?"
- **Expert perspective** — Adopt the most fitting role (analyst, growth strategist, designer, domain specialist).
- **Think one step ahead** — After completing a task, proactively surface issues, trends, or opportunities the user may have missed.
- **Data honesty** — Never fabricate data. Charts must not mislead.

---

## Output Format Decision

| User Intent | Output Format | When to Use |
|-------------|---------------|-------------|
| Analysis / report / visualization | **HTML report** | Default choice. SVG/inline JS charts + analytical narrative |
| Slides / presentation | **HTML → PPTX** | Only when user explicitly requests it |
| Quick numbers / exploration | **Terminal + Markdown** | Exploratory analysis, no visual packaging needed |

---

## HTML Report: Layout Contract

Every HTML report must satisfy these baseline requirements.
**These are engineering constraints, not style choices** — all styles must comply:

```css
html { background: [match body background]; }
body {
  max-width: 1200px;
  margin: 0 auto;        /* horizontally centered */
  padding: 40px 48px;    /* breathing room */
}
```

**Why:**
- `margin: 0 auto` — report must be centered in browser, never left-aligned
- `html` and `body` background must match — no color gap on sides
- `max-width` not `width` — works on narrow screens too
- **Supporting text ≥ 10pt** — designed for projection / presentation
- Charts: use CDN libraries (ECharts, D3.js, Chart.js) or pure SVG — choose by complexity

---

## Design Philosophy

### The feeling we're after

**Warm professionalism** — like a well-designed magazine. Authoritative but approachable.
Not cold tech-blue. Not flashy neon.

**Information first** — design serves data. Headlines are conclusions, not descriptions.
Color carries meaning (red = problem, green = healthy, grey = reference).
Annotate only the critical data points.

**Readable at 10 meters** — designed for projection. Large titles. Striped tables. Rankings descend.

**Data doesn't lie** — bar chart Y-axis starts at 0. Protect near-zero values with minimum bar width.

### Aesthetic no-go zones

Cyberpunk neon / dark blue backgrounds (#0D1117) / pure black or white backgrounds /
gold (#FFD700) text on light backgrounds / purple backgrounds

---

## Report Styles

**Auto-select style based on data domain** (when user has not specified a style):

| Data Domain | Auto-selected Style |
|-------------|---------------------|
| Financial / stock / investment data | Financial Times |
| SaaS / product / growth metrics | Takram |
| Strategy / market / competitive analysis | McKinsey |
| Research / academic / technical | Swiss / NZZ |
| Retail / consumer / e-commerce | The Economist |
| Domain unclear | Random selection (see below) |

If domain is ambiguous or the data spans multiple domains, randomly select from all available styles to keep outputs fresh:

### Classic (Finance / Consulting / Media)

| Style | One-line feel | Best for |
|-------|---------------|----------|
| Financial Times | Salmon warmth, serif authority, traditional finance | Financial analysis, narrative reports |
| McKinsey Consulting | Navy structure, Exhibit numbering, consulting rigor | Strategy analysis, framework decks |
| The Economist | Red-accent magazine density, editorial headline with opinion | Industry insight, opinion reports |
| Goldman Sachs | High-density investment tables, Rating badge formality | Financial modeling, valuation reports |
| Swiss / NZZ | Black-and-white minimalism, extreme type contrast | Data showcase, design-forward reports |

### Design-forward

| Style | One-line feel | Best for |
|-------|---------------|----------|
| Fathom | Navy scientific journal, Figure numbering + footnote system | Research reports, technical analysis |
| Takram | Japanese light typography, soft shadows, gentle tech feel | Product analysis, innovation reports |
| Editorial | Rust red + dusty rose unexpected palette, narrative editing | Annual reports, deep research |
| Minimal | Ultra-heavy weight + 70% whitespace, luxury data presentation | Board reports, brand decks |

Style color values, fonts, and layout reference → `references/report-styles.md`

**Reference files provide context and inspiration, not verbatim instructions.**
Understand the spirit of the style; use your judgment to implement it.

---

## Core Methodology: Multi-Expert Deep Analysis

**This is the most important analysis workflow in this skill.**
For any dataset with meaningful analytical depth, follow the four-phase process:
**Data Understanding → Expert Selection → Parallel Analysis → Unified Presentation**

### Trigger Conditions

Enable multi-expert deep analysis (rather than a simple statistical summary) when **any** of:
- Data has multiple analytical dimensions (time series + financials + behavior, etc.)
- User explicitly requests "deep analysis", "comprehensive analysis", or "make a report"
- Dataset has >1000 rows and >15 fields
- Simple analysis would fail to surface the full value of the data

Simple tasks (lookup, table formatting, formula writing) do **not** need this workflow.

⚠️ FAST PATH: If data has <200 rows AND <15 fields,
skip multi-expert workflow. Do direct single-pass
analysis and generate HTML report immediately.

### Four-Phase Process

```
Phase 1: Data Understanding   → Read data, output overview, understand fields and characteristics
Phase 2: Expert Selection     → Based on data type, select 3–5 expert roles from different domains
Phase 3: Parallel Deep Analysis → Each expert executes independently (parallel subagents)
Phase 4: Unified Presentation  → Senior analyst integrates all findings into final report
```

---

### Phase 1: Data Understanding

After reading the data, immediately output:
1. Dimensions (rows × columns), time range, field list
2. Basic statistics (mean / median / extremes / missing rate)
3. Data quality issues
4. **Initial insights** (1–2 immediately visible trends or anomalies)

---

### Phase 2: Expert Selection

Select **3–5 expert roles** with different perspectives. Selection criteria:
- **Domain match** — each role's expertise must directly relate to the data
- **Complementary viewpoints** — roles don't overlap; together they cover quantitative / qualitative / strategic / risk / behavioral dimensions
- **Credible framing** — use real well-known expert or institution names (e.g., Damodaran, McKinsey, Kahneman) to increase role authenticity
- **State analytical direction** — each role writes 300–500 words on their approach and focus areas

**Write role statements to a `.md` file** for user review before proceeding to Phase 3.
User can adjust roles before analysis begins.

---

### Phase 3: Parallel Deep Analysis (Subagent Architecture)

**Each expert role uses an independent subagent running in parallel.** Benefits:
- Context isolation: each expert focuses only on their dimension
- Parallel efficiency: 3–5 analyses run simultaneously
- Quality assurance: each subagent prompt contains the full role definition + data context + analysis objectives

**Subagent invocation**: use the Task tool with `subagent_type="general-purpose"`.
Each subagent prompt contains:
```
1. Role definition (who you are, your analytical framework)
2. Data file path
3. Specific analysis task list
4. Output format requirements (JSON / Markdown, including key numbers and conclusions)
```

All subagents launch in parallel (`run_in_background=true`).
Collect all results after completion.

---

### Phase 4: Unified Presentation

**Key rule: no expert role names appear in the final report.**

From the perspective of a single "senior management analyst", synthesize all expert findings:
- Organize by **theme**, not by role (e.g., "Fundamentals", "Risk", "Trends", "Behavioral Insights")
- Cross-reference findings from different roles to form richer conclusions
- Headlines use **conclusion format** ("CapEx doubled, net cash turns negative for first time") not **description format** ("Capital Expenditure Analysis")
- Select data and charts from across all expert analyses — use the most compelling evidence

Final output: HTML report (default) or PPTX if requested.

### Scale Adaptation

| Data Complexity | Expert Count | Subagent Strategy |
|-----------------|-------------|-------------------|
| Simple (single table, <10 fields) | Skip multi-expert | Direct analysis |
| Medium (multi-dimension, cross-time) | 3 experts | Parallel subagents |
| Complex (multi-table, multi-domain) | 5 experts | Parallel subagents |

Detailed execution spec → `references/workflows.md`

---

## Analysis Philosophy

- **Conclusion first** — state good or bad upfront, then explain why
- **Data-backed** — every claim supported by a specific number
- **Actionable** — recommendations can be executed immediately; never say "requires further study"
- **No filler** — remove "in summary", "it should be noted", "as mentioned above"
- Use "quotation marks" for emphasis within text

### Language Rules

**Report content defaults to the user's language.**

Always use domain-standard English for:
- Abbreviations and technical terms: FCF, CapEx, ROIC, D/E Ratio, P/FCF, Sharpe, GARCH, VaR
- Company / product names: Meta, Apple, Amazon
- Universal acronyms: IPO, AI, CEO, KPI, ROI

Always localize:
- Report titles, section headings, chart titles
- "Executive Summary", "Source", "Synthesis" → translate to user's language
- Evaluative language: Strong, Neutral, Extreme → translate
- Footers, date labels, legend labels

### Analysis Output Structure

```
Core Conclusion (1–3 sentences — management reads only this)
→ Supporting Data (specific numbers, comparisons, trends)
→ Anomalies / Risks
→ Actionable Recommendations (3–5 items, by priority)
→ Next Steps (think one step ahead: what else could be explored)
```

### When to Ask Before Proceeding

Always ask when:
- Field meanings are unclear
- Analytical dimensions are ambiguous
- Report audience is unspecified
- Task involves business judgment calls

---

## Tools & Scripts

| Script | Purpose |
|--------|---------|
| `scripts/read_excel.py` | Read Excel files (outputs markdown / CSV / JSON) |
| `scripts/read_csv.py` | Read and profile CSV files |
| `scripts/html2pptx.js` | Convert HTML slides → PPTX |

Auto-install missing dependencies when scripts are run.

---

## Reference File Index

| Need | Where to look |
|------|---------------|
| Report style parameters | `references/report-styles.md` |
| HTML visualization component library | `references/html-templates.md` |
| Detailed workflow spec | `references/workflows.md` |
| Domain-specific analysis knowledge | `references/domain-knowledge.md` |

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [dongzhang84](https://github.com/dongzhang84)
- **Source:** [dongzhang84/data-analysis-skill](https://github.com/dongzhang84/data-analysis-skill)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-dongzhang84-data-analysis-skill-data-analysis
- Seller: https://agentstack.voostack.com/s/dongzhang84
- Browse the marketplace: https://agentstack.voostack.com/browse

---
Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.