Stdin Processing

#!/usr/bin/env -S ai --haiku
Analyze the data provided on stdin. Summarize the key points, highlight
anything unusual, and suggest next steps.

cat data.json | ./analyze-stdin.md

[Data from stdin]
{"users": 1500, "revenue": 45000, ...}

Analyze the data provided on stdin. Summarize the key points...

cat data.json | ./analyze-stdin.md

# Analyze git history
git log --oneline -20 | ./analyze-stdin.md

# Analyze package dependencies
npm list --depth=0 | ./analyze-stdin.md

# Analyze recent logs
tail -100 /var/log/app.log | ./analyze-stdin.md

# Fetch and analyze
curl -s https://api.example.com/metrics | ./analyze-stdin.md

# GitHub API
curl -s https://api.github.com/repos/user/repo/issues | ./analyze-stdin.md

cat data.json | ./analyze-stdin.md > analysis.txt

cat metrics.json | ./analyze-stdin.md

{
  "date": "2026-03-03",
  "users": 1500,
  "revenue": 45000,
  "signups": 120,
  "churn": 8,
  "response_time_ms": [120, 150, 890, 130, 125]
}

Key metrics summary:
- Strong user base: 1,500 active users
- Revenue: $45,000
- Growth: 120 new signups, 8 churn (93% retention)

Unusual findings:
- Response time spike: 890ms outlier (others 120-150ms)
- Investigate slow requests

Next steps:
- Monitor response times for performance regression
- Analyze churn reasons
- Maintain current signup momentum

cat sales.csv | ./analyze-stdin.md

date,product,quantity,revenue
2026-03-01,Widget A,50,5000
2026-03-01,Widget B,30,4500
2026-03-02,Widget A,45,4500
2026-03-02,Widget B,35,5250
2026-03-03,Widget A,0,0

Sales analysis:
- Widget B trending up (30 → 35 units, $4,500 → $5,250)
- Widget A trending down (50 → 45 → 0 units)

Unusual finding:
- Widget A had ZERO sales on 2026-03-03 (out of stock?)

Next steps:
- Check Widget A inventory status immediately
- Increase Widget B stock to meet demand

tail -500 /var/log/nginx/access.log | ./analyze-stdin.md

#!/usr/bin/env -S ai --haiku
Analyze the nginx access logs provided on stdin:
- Request volume and patterns
- Most accessed endpoints
- Error rates
- Unusual activity or potential attacks

git log --oneline -50 | ./analyze-stdin.md

#!/usr/bin/env -S ai --haiku
Analyze the git commit history on stdin:
- What areas of the codebase are most active?
- Are commit messages clear and descriptive?
- Any patterns or concerns?

# Extract → Analyze → Format pipeline
./extract-data.md | ./analyze-stdin.md | ./format-report.md > final.txt

#!/usr/bin/env -S ai --haiku --skip
Read metrics.json and output only the fields: users, revenue, signups.
Format as CSV.

#!/usr/bin/env -S ai --haiku
Analyze the CSV data on stdin. Identify trends and anomalies.

#!/usr/bin/env -S ai --haiku
Format the analysis on stdin as a professional email to executives.

for file in logs/*.log; do
    echo "\n=== $file ==="
    cat "$file" | ./analyze-stdin.md
done > analysis-report.txt

# Prepend (default)
cat data.json | ./script.md

# Append
cat data.json | ./script.md --stdin-position append

# Replace entire prompt
cat data.json | ./script.md --stdin-position replace

#!/usr/bin/env -S ai --haiku
Analyze the following data and compare it to yesterday's metrics:

cat today.json | ./compare.md --stdin-position append

Analyze the following data and compare it to yesterday's metrics:
{"users": 1500, "revenue": 45000}

#!/usr/bin/env -S ai --haiku
Convert the JSON data on stdin to CSV format.
Include all fields as columns.

cat data.json | ./json-to-csv.md > data.csv

#!/usr/bin/env -S ai --haiku
The stdin contains JSON with an array of transactions.
Filter for transactions > $1000 and sum the total.
Output only the total amount.

cat transactions.json | ./filter-sum.md
# Output: $45,230

#!/usr/bin/env -S ai --haiku
Analyze the time-series data on stdin (CSV format).
Detect anomalies using the 3-sigma rule.
Output only the anomalous rows.

cat timeseries.csv | ./detect-anomalies.md > anomalies.csv

#!/usr/bin/env -S ai --haiku
The stdin contains a list of GitHub usernames (one per line).
For each user, output: username, estimated location, primary language.
Base this on common patterns in usernames and your knowledge.

cat users.txt | ./enrich-users.md > enriched-users.csv

#!/usr/bin/env -S ai --haiku --skip
Read metrics.json and analyze the data.
Compare to historical data in metrics-history.json.

ai script.md  # Script reads files directly

cat metrics.json | ./script.md  # Piped data

cat large-dataset.csv | ai --live --haiku << 'EOF'
Analyze the CSV data on stdin.
Print a summary after every 1000 rows.
Finally, output overall statistics.
EOF

# Script uses --haiku, override to --sonnet for better analysis
cat complex-data.json | ai --sonnet analyze-stdin.md

#!/usr/bin/env -S ai --haiku
---
vars:
  format: "summary"
---
Analyze the data on stdin.
Output format: {{format}}

cat data.json | ./analyze.md --format "detailed report"

name: Analyze Metrics
on:
  schedule:
    - cron: '0 0 * * *'  # Daily

jobs:
  analyze:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      
      - name: Setup AIRun
        run: |
          curl -fsSL https://claude.ai/install.sh | bash
          git clone https://github.com/andisearch/airun.git
          cd airun && ./setup.sh
      
      - name: Fetch and analyze metrics
        run: |
          curl -s https://api.example.com/metrics | \
            ai --apikey --haiku << 'EOF' > analysis.md
          Analyze the metrics JSON on stdin.
          Highlight any unusual trends or concerns.
          EOF
        env:
          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
      
      - name: Upload analysis
        uses: actions/upload-artifact@v3
        with:
          name: daily-analysis
          path: analysis.md

#!/bin/bash
# daily-db-analysis.sh

# Export database metrics
psql -c "SELECT * FROM daily_metrics WHERE date = CURRENT_DATE" -t -A -F"," | \
  ai --haiku --apikey << 'EOF' > db-analysis.txt
Analyze the database metrics CSV on stdin.
Compare to typical patterns and flag anomalies.
EOF

# Email results
mail -s "Daily DB Analysis" team@example.com < db-analysis.txt

# Wrong - passes filename as argument, not stdin
./analyze-stdin.md data.json

# Correct - pipes file contents to stdin  
cat data.json | ./analyze-stdin.md

# Debug: see what's being piped
cat data.json | tee /dev/stderr | ./analyze-stdin.md

# Don't pipe binary directly
cat image.png | ./analyze.md  # ERROR

# Convert to base64 first
base64 image.png | ./analyze-base64.md  # OK