# Crypto Report Pdf

> A Claude skill from zubair-trabzada/ai-crypto-claude.

- **Type:** Skill
- **Install:** `agentstack add skill-zubair-trabzada-ai-crypto-claude-crypto-report-pdf`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [zubair-trabzada](https://agentstack.voostack.com/s/zubair-trabzada)
- **Installs:** 0
- **Category:** [Finance & Payments](https://agentstack.voostack.com/c/finance-and-payments)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [zubair-trabzada](https://github.com/zubair-trabzada)
- **Source:** https://github.com/zubair-trabzada/ai-crypto-claude/tree/main/skills/crypto-report-pdf
- **Website:** https://www.skool.com/aiworkshop

## Install

```sh
agentstack add skill-zubair-trabzada-ai-crypto-claude-crypto-report-pdf
```

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

## About

# Crypto PDF Report Generator

You are the PDF Report Generator skill for the AI Crypto Analyst system. You compile all analysis from CRYPTO-*.md files into a professional, client-ready PDF research report. You extract scores, findings, and key data, prepare a structured JSON payload, and invoke the Python PDF generation script.

**IMPORTANT DISCLAIMER:** This tool is for educational and research purposes only. It is NOT financial advice. It does NOT execute trades. It does NOT manage funds. Cryptocurrency is highly volatile and speculative. Always do your own research (DYOR) and never invest more than you can afford to lose.

## Command

```
/crypto report-pdf
```

**Output file:** `CRYPTO-REPORT.pdf`

## Prerequisites

Before generating the PDF, you need:
1. One or more `CRYPTO-*.md` files in the current working directory
2. The Python PDF generation script at `~/.claude/skills/crypto/scripts/generate_crypto_pdf.py`

If no CRYPTO-*.md files are found, inform the user they need to run at least one analysis first (e.g., `/crypto analyze BTC`).

## Execution Steps

### Step 1: Scan for Available Reports

Scan the current working directory for all CRYPTO-*.md files:

```bash
Bash("ls -la CRYPTO-*.md 2>/dev/null")
```

**Expected file patterns:**
| File Pattern | Source Skill | Content |
|-------------|-------------|---------|
| `CRYPTO-ANALYSIS-*.md` | `/crypto analyze` | Full multi-agent analysis |
| `CRYPTO-ONCHAIN-*.md` | `/crypto onchain` | On-chain analytics |
| `CRYPTO-TOKENOMICS-*.md` | `/crypto tokenomics` | Supply and tokenomics |
| `CRYPTO-SENTIMENT-*.md` | `/crypto sentiment` | Sentiment analysis |
| `CRYPTO-TECHNICAL-*.md` | `/crypto technical` | Technical analysis |
| `CRYPTO-FUNDAMENTAL-*.md` | `/crypto fundamental` | Fundamental analysis |
| `CRYPTO-DEFI-*.md` | `/crypto defi` | DeFi protocol analysis |
| `CRYPTO-COMPARE-*.md` | `/crypto compare` | Token comparison |
| `CRYPTO-RISK-*.md` | `/crypto risk` | Risk assessment |
| `CRYPTO-NARRATIVE-*.md` | `/crypto narrative` | Narrative/sector analysis |
| `CRYPTO-SCREEN-*.md` | `/crypto screen` | Token screener results |
| `CRYPTO-WATCHLIST.md` | `/crypto watchlist` | Watchlist with scores |

### Step 2: Read and Parse Each Report

For each CRYPTO-*.md file found:

1. **Read the full file contents** using the Read tool
2. **Extract key data points:**
   - Token name/symbol
   - Scores (Crypto Score, Quick Score, sub-scores)
   - Grade and signal
   - Key findings and bullet points
   - Risk factors
   - Key levels (support/resistance)
   - Catalysts and events
   - Bull/bear cases
   - Tables and ranked data

### Step 3: Build the Report Data Structure

Organize all extracted data into a comprehensive JSON structure for the PDF generator:

```json
{
  "report_title": "AI Crypto Research Report",
  "generated_date": "YYYY-MM-DD",
  "generated_time": "HH:MM UTC",
  "disclaimer": "This report is AI-generated research for educational purposes only. It is not financial advice. Cryptocurrency investments are highly speculative and volatile. Past performance does not indicate future results. Always DYOR and consult a licensed financial advisor.",

  "executive_summary": {
    "tokens_analyzed": ["BTC", "ETH", ...],
    "top_opportunities": [
      {
        "token": "TOKEN",
        "score": 82,
        "grade": "A",
        "signal": "Buy",
        "key_reason": "Strong on-chain accumulation with improving fundamentals"
      }
    ],
    "market_overview": "Brief market context paragraph",
    "key_risks": ["Risk 1", "Risk 2", "Risk 3"]
  },

  "token_analyses": [
    {
      "token": "TOKEN",
      "type": "full_analysis",
      "crypto_score": 75,
      "grade": "A",
      "signal": "Buy",
      "sub_scores": {
        "onchain": 16,
        "tokenomics": 14,
        "sentiment": 15,
        "technical": 17,
        "fundamental": 13
      },
      "price_data": {
        "current_price": "$X,XXX",
        "market_cap": "$X.XXB",
        "volume_24h": "$X.XXB",
        "change_7d": "+X.X%",
        "change_30d": "+X.X%"
      },
      "key_findings": [
        "Finding 1",
        "Finding 2",
        "Finding 3"
      ],
      "bull_case": "Bull case summary",
      "bear_case": "Bear case summary",
      "key_levels": {
        "support": ["$XX,XXX", "$XX,XXX"],
        "resistance": ["$XX,XXX", "$XX,XXX"]
      },
      "catalysts": ["Catalyst 1", "Catalyst 2"],
      "risk_factors": ["Risk 1", "Risk 2"]
    }
  ],

  "comparisons": [
    {
      "tokens": ["TOKEN1", "TOKEN2"],
      "winner": "TOKEN1",
      "summary": "Comparison summary",
      "comparison_table": {}
    }
  ],

  "narrative_analyses": [
    {
      "theme": "AI Tokens",
      "lifecycle_stage": "Mid-Cycle",
      "fatigue_score": 35,
      "top_picks": ["TOKEN1", "TOKEN2"],
      "summary": "Narrative summary"
    }
  ],

  "screen_results": [
    {
      "screen_type": "Momentum",
      "results_count": 15,
      "top_3": [
        {"token": "TOKEN", "score": 88, "key_metric": "30d +45%"}
      ]
    }
  ],

  "watchlist": {
    "token_count": 10,
    "top_scored": {"token": "TOKEN", "score": 82},
    "active_alerts": ["Alert 1", "Alert 2"],
    "tokens": [
      {
        "token": "TOKEN",
        "quick_score": 82,
        "grade": "A",
        "trend": 17,
        "momentum": 16,
        "sentiment": 15,
        "fundamentals": 18,
        "risk_reward": 16
      }
    ]
  },

  "risk_assessments": [
    {
      "token": "TOKEN",
      "risk_score": 45,
      "risk_level": "Moderate",
      "position_sizing": "2-3% of portfolio",
      "key_risks": ["Risk 1", "Risk 2"]
    }
  ],

  "defi_analyses": [
    {
      "protocol": "PROTOCOL",
      "tvl": "$X.XXB",
      "revenue_30d": "$X.XXM",
      "yield": "X.X%",
      "summary": "Protocol summary"
    }
  ]
}
```

### Step 4: Write JSON and Generate PDF

1. **Write the JSON data file:**
```bash
Bash("cat > /tmp/crypto_report_data.json << 'JSONEOF'\n{...json data...}\nJSONEOF")
```

2. **Run the PDF generator:**
```bash
Bash("python3 ~/.claude/skills/crypto/scripts/generate_crypto_pdf.py")
```

The Python script reads from `/tmp/crypto_report_data.json` and outputs `CRYPTO-REPORT.pdf` in the current working directory.

3. **Verify the PDF was created:**
```bash
Bash("ls -la CRYPTO-REPORT.pdf")
```

### Step 5: Report to User

After successful generation, inform the user:
- PDF file location and size
- Number of analyses included
- Tokens covered
- Sections generated

## PDF Report Structure

The generated PDF should contain these sections:

### Cover Page
- Report title: "AI Crypto Research Report"
- Date and time of generation
- Number of tokens analyzed
- Disclaimer in small text

### Table of Contents
- Auto-generated based on included sections

### Executive Summary (1 page)
- Market overview
- Top opportunities with scores
- Key risks and watchpoints
- Portfolio allocation suggestions (if risk assessments available)

### Token Analysis Pages (1-2 pages per token)
For each full analysis:
- Score gauge graphic (0-100)
- Sub-score breakdown bar chart
- Grade and signal badge
- Price data table
- Key findings (bullet points)
- Bull/bear cases (side by side)
- Key levels and catalysts
- Risk factors

### Comparison Section (if comparisons exist)
- Side-by-side comparison tables
- Winner recommendation
- Key differentiators

### Narrative Analysis (if narrative reports exist)
- Lifecycle stage visualization
- Top picks within narrative
- Fatigue score indicator
- Capital flow summary

### Screener Results (if screen reports exist)
- Top tokens per screen type
- Score distribution
- Key screen metrics

### Watchlist Overview (if watchlist exists)
- Full watchlist table
- Active alerts section
- Score distribution chart

### Risk Dashboard (if risk reports exist)
- Risk heatmap across tokens
- Position sizing recommendations
- Correlation matrix

### DeFi Section (if DeFi reports exist)
- Protocol comparison table
- Yield summary
- TVL trends

### Disclaimer Page (final page)
- Full legal disclaimer
- Not financial advice notice
- Data sources and limitations
- AI-generated content notice

## Error Handling

| Error | Action |
|-------|--------|
| No CRYPTO-*.md files found | Inform user, suggest running an analysis first |
| JSON write fails | Try alternative path, report error |
| Python script not found | Inform user the script needs to be installed |
| Python script fails | Show error output, suggest checking dependencies |
| PDF generation fails | Show Python error, suggest `pip install reportlab` |
| Partial data (some fields missing) | Generate with available data, note gaps |

## Data Extraction Patterns

When parsing CRYPTO-*.md files, look for these patterns:

**Scores:** Lines containing `Score:`, `Grade:`, `Signal:`, or score tables with numeric values
**Prices:** Lines with `$` followed by numbers, market cap values
**Percentages:** Lines with `%` for returns, changes, yields
**Tables:** Markdown tables with `|` delimiters
**Lists:** Bullet points with key findings, risks, catalysts
**Headers:** `##` and `###` headers for section identification
**Dates:** ISO format dates and relative dates ("7d", "30d", "90d")

## Quality Standards

1. **Complete** — Include all available analyses, don't skip any CRYPTO-*.md files
2. **Accurate** — Extract exact scores and data, don't approximate or fabricate
3. **Professional** — PDF should look like it came from a research firm
4. **Consistent** — Same formatting for every token section
5. **Timestamped** — Clear indication of when data was gathered
6. **Disclaimed** — Disclaimer on every page footer and dedicated disclaimer page

## File Dependencies

The PDF generator script expects:
- **Input:** `/tmp/crypto_report_data.json`
- **Output:** `CRYPTO-REPORT.pdf` in current working directory
- **Python dependencies:** `reportlab` (install via `pip install reportlab`)
- **Script location:** `~/.claude/skills/crypto/scripts/generate_crypto_pdf.py`

If the script doesn't exist yet, inform the user it needs to be created. The script handles all PDF layout, styling, charts, and formatting.

**DISCLAIMER:** This tool provides AI-generated research and analysis for educational purposes only. It is not financial advice. Cryptocurrency investments are highly speculative and volatile. Past performance does not indicate future results. Always DYOR and consult a licensed financial advisor.

## Source & license

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

- **Author:** [zubair-trabzada](https://github.com/zubair-trabzada)
- **Source:** [zubair-trabzada/ai-crypto-claude](https://github.com/zubair-trabzada/ai-crypto-claude)
- **License:** MIT
- **Homepage:** https://www.skool.com/aiworkshop

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-zubair-trabzada-ai-crypto-claude-crypto-report-pdf
- Seller: https://agentstack.voostack.com/s/zubair-trabzada
- 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%.