---
name: seo-crawl-budget
description: |
  Crawl budget optimization and server log analysis for search engine bots.
  Triggers: crawl budget, log analysis, bot crawling, Googlebot, crawl waste,
  orphan pages, crawl efficiency, 크롤 예산, 로그 분석, 크롤 최적화.
---

# Crawl Budget Optimizer

Analyze server access logs to identify crawl budget waste and generate optimization recommendations for search engine bots (Googlebot, Yeti/Naver, Bingbot, Daumoa/Kakao).

## Capabilities

### Log Analysis
- Parse Nginx combined, Apache combined, and CloudFront log formats
- Support for gzip/bzip2 compressed logs
- Streaming parser for files >1GB
- Date range filtering
- Custom format via regex

### Bot Profiling
- Identify bots by User-Agent: Googlebot (and variants), Yeti (Naver), Bingbot, Daumoa (Kakao), Applebot, DuckDuckBot, Baiduspider
- Per-bot metrics: requests/day, requests/hour, unique URLs crawled
- Status code distribution per bot (200, 301, 404, 500)
- Crawl depth distribution
- Crawl pattern analysis (time of day, days of week)
- Most crawled URLs per bot

### Waste Detection
- **Parameter URLs**: ?sort=, ?filter=, ?page=, ?utm_* consuming crawl budget
- **Redirect chains**: Multiple redirects consuming crawl slots
- **Soft 404s**: 200 status pages with error/empty content
- **Duplicate URLs**: www/non-www, http/https, trailing slash variants
- **Low-value pages**: Thin content pages, noindex pages being crawled

### Orphan Page Detection
- Pages in sitemap but never crawled by bots
- Pages crawled but not in sitemap
- Crawled pages with no internal links pointing to them

## Workflow

### Step 1: Obtain Server Access Logs
Request or locate server access logs from the target site. Supported formats:
- Nginx: `/var/log/nginx/access.log`
- Apache: `/var/log/apache2/access.log`
- CloudFront: Downloaded from S3 or CloudWatch

### Step 2: Parse Access Logs
```bash
python scripts/log_parser.py --log-file access.log --json
python scripts/log_parser.py --log-file access.log.gz --streaming --json
python scripts/log_parser.py --log-file access.log --bot googlebot --json
```

### Step 3: Crawl Budget Analysis
```bash
python scripts/crawl_budget_analyzer.py --log-file access.log --sitemap https://example.com/sitemap.xml --json
python scripts/crawl_budget_analyzer.py --log-file access.log --scope waste --json
python scripts/crawl_budget_analyzer.py --log-file access.log --scope orphans --json
python scripts/crawl_budget_analyzer.py --log-file access.log --scope bots --json
```

### Step 4: Cross-Reference with External Data (Optional)
Use `our-seo-agent` CLI or provide pre-fetched JSON via `--input` to compare indexed pages vs crawled pages. WebSearch can supplement with current indexing data.

### Step 5: Generate Recommendations
Prioritized action items:
1. robots.txt optimization (block parameter URLs, low-value paths)
2. URL parameter handling (Google Search Console settings)
3. Noindex/nofollow for low-value pages
4. Redirect chain resolution (reduce 301 → 301 → 200 to 301 → 200)
5. Internal linking improvements for orphan pages

### Step 6: Report to Notion
Save Korean-language report to SEO Audit Log database.

| Property | Type | Description |
|----------|------|-------------|
| Issue | Title | Report title (Korean + date) |
| Site | URL | Audited website URL |
| Category | Select | Crawl Budget |
| Priority | Select | Based on efficiency score |
| Found Date | Date | Analysis date (YYYY-MM-DD) |
| Audit ID | Rich Text | Format: CRAWL-YYYYMMDD-NNN |

## Data Source Selection

Crawl-budget analysis is **primarily log-based** — the authoritative signal lives in server access logs that this skill's local Python scripts parse. API backends supplement that with crawled URL inventories, index status, and third-party site-audit views. Pick per data class.

| Backend | Best for | Notes |
|---|---|---|
| **Server access logs** (via `scripts/log_parser.py` + `crawl_budget_analyzer.py`) | **Primary** — bot identification, request volume, status code distribution, waste detection, orphan pages | Requires actual server logs from the user (Nginx / Apache / CloudFront). No MCP substitute. |
| **OurSEO** (CLI + MCP) | **Default** for crawl-derived URL inventory, index status, redirect chains | CLI: `our collect crawl`, `our research google index`, `our audit tech`. MCP: `mcp__ourseo__crawl_website`, `mcp__ourseo__check_index`. Distributed crawl for large sites: `our collect distributed --workers N`. |
| **Ahrefs MCP** (`mcp__ahrefs__*`) | Orphan detection, redirect chain analysis via Ahrefs site audit | `site-audit-issues`, `site-audit-page-explorer`, `site-audit-page-content`. Useful when an Ahrefs project already exists for the domain. |
| **Semrush MCP** (`mcp__semrush__*`) | Alternative site audit when Ahrefs project doesn't exist | `siteaudit_research` → `get_report_schema` → `execute_report`. |
| **GSC** (via `our research search-console`) | First-party Googlebot crawl stats (Coverage / Crawl Stats reports) | Required for ground-truth Googlebot behaviour — server logs show what hit the origin, GSC shows what Google considers crawled. |

### How to pick

1. **User named a backend explicitly** → use it.
2. **User preference memory** — read `feedback_seo_tool_preferences.md`; honor the task-type default.
3. **Server access logs are available** → ALWAYS process them first (`log_parser.py` + `crawl_budget_analyzer.py`). They are the only source for actual bot behaviour at the origin.
4. **Sitemap vs. crawl comparison needed** → OurSEO `crawl_website` for URL inventory; cross-reference with logs.
5. **No logs available** → fall back to OurSEO crawl + GSC Coverage + Ahrefs/Semrush site audit. State this limitation explicitly in the report.
6. **Default**: server logs (when present) + **OurSEO crawl + index check** for URL inventory.
7. **Still ambiguous + non-trivial** → ask once via `AskUserQuestion`.

### Backend call patterns

**Local log analysis (primary):**
```bash
python scripts/log_parser.py --log-file access.log --json
python scripts/log_parser.py --log-file access.log.gz --streaming --json
python scripts/log_parser.py --log-file access.log --bot googlebot --json

python scripts/crawl_budget_analyzer.py --log-file access.log --sitemap https://<site>/sitemap.xml --json
python scripts/crawl_budget_analyzer.py --log-file access.log --scope waste --json
python scripts/crawl_budget_analyzer.py --log-file access.log --scope orphans --json
python scripts/crawl_budget_analyzer.py --log-file access.log --scope bots --json
```

**OurSEO CLI (URL inventory + index check):**
```bash
our collect crawl https://<site> --max-pages 5000
our collect distributed https://<site> --workers 8 --max-pages 50000
our research google index --domain <site>
our audit tech https://<site>
```

**OurSEO MCP (Claude Desktop):**
```
mcp__ourseo__crawl_website(url="<site>", max_pages=5000)
mcp__ourseo__check_index(domain="<site>")
```

**GSC (first-party crawl stats):**
```bash
our research search-console queries --site sc-domain:<site> --days 28
# See also: GSC Coverage / Crawl Stats reports (UI-only for some sections).
```

**Ahrefs MCP (third-party site audit):**
```
mcp__ahrefs__site-audit-projects()
mcp__ahrefs__site-audit-issues(project_id="<id>")
mcp__ahrefs__site-audit-page-explorer(project_id="<id>")
```

**Semrush MCP (alternative site audit):**
```
mcp__semrush__siteaudit_research(query="<site>", database="us")
```

Always record the chosen data source(s) in the report **Overview** so future audits can compare like-for-like — and explicitly state whether server logs were available.

## Output Format

```json
{
  "log_file": "access.log",
  "analysis_period": {"from": "2025-01-01", "to": "2025-01-31"},
  "total_bot_requests": 150000,
  "bots": {
    "googlebot": {
      "requests": 80000,
      "unique_urls": 12000,
      "avg_requests_per_day": 2580,
      "status_distribution": {"200": 70000, "301": 5000, "404": 3000, "500": 2000}
    },
    "yeti": {"requests": 35000},
    "bingbot": {"requests": 20000},
    "daumoa": {"requests": 15000}
  },
  "waste": {
    "parameter_urls": {"count": 5000, "pct_of_crawls": 3.3},
    "redirect_chains": {"count": 2000, "pct_of_crawls": 1.3},
    "soft_404s": {"count": 1500, "pct_of_crawls": 1.0},
    "total_waste_pct": 8.5
  },
  "orphan_pages": {
    "in_sitemap_not_crawled": [],
    "crawled_not_in_sitemap": []
  },
  "recommendations": [],
  "efficiency_score": 72,
  "timestamp": "2025-01-01T00:00:00"
}
```

## Korean Output Example

```
# 크롤 예산 분석 보고서 - example.com

## 분석 기간: 2025-01-01 ~ 2025-01-31

### 봇별 크롤 현황
| 봇 | 요청 수 | 고유 URL | 일 평균 |
|----|---------|---------|---------|
| Googlebot | 80,000 | 12,000 | 2,580 |
| Yeti (Naver) | 35,000 | 8,000 | 1,129 |

### 크롤 낭비 요인
- 파라미터 URL: 5,000건 (3.3%)
- 리다이렉트 체인: 2,000건 (1.3%)
- 소프트 404: 1,500건 (1.0%)

### 효율성 점수: 72/100
```

## Limitations

- Requires actual server access logs (not available via standard web crawling)
- Log format auto-detection may need manual format specification for custom formats
- CloudFront logs have a different field structure than Nginx/Apache
- Large log files (>10GB) may need pre-filtering before analysis
- Bot identification relies on User-Agent strings which can be spoofed

## Notion Output (Required)

All audit reports MUST be saved to the OurDigital SEO Audit Log:
- **Database ID**: `2c8581e5-8a1e-8035-880b-e38cefc2f3ef`
- **Category**: Crawl Budget
- **Audit ID Format**: CRAWL-YYYYMMDD-NNN
- **Language**: Korean with technical English terms (Crawl Budget, Googlebot, robots.txt)

## Reference Scripts

Located in `code/scripts/`:
- `log_parser.py` — Server access log parser with bot identification
- `crawl_budget_analyzer.py` — Crawl budget efficiency analysis
- `base_client.py` — Shared async client utilities
