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server-log-crawler-analyst

Analyze credential-free public website monitoring evidence from server access logs, HTTP fetches, robots/llms/sitemap checks, and exported summaries while protecting raw IP and private query data. Use this skill when the user asks whether crawlers or AI bots reached a site, whether public SEO/LLM discovery files respond, or how to separate observed facts from unknown Search Console, rank, analytics, or citation claims.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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설치 방법 보기

포함된 파일(9)

  • SKILL.md4.1 KB
  • agents/openai.yaml388 B
  • assets/crawler-monitor-report.template.md732 B
  • evals.json1.8 KB
  • fixtures/sample-access.log575 B
  • references/data-source-tiers.md2.0 KB
  • references/monitoring-backlog.md1.4 KB
  • scripts/analyze_access_log.py3.8 KB
  • validation-report.md1.2 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Server Log Crawler Analyst

Use this skill when credential-free monitoring evidence is enough, or when credentialed Search Console, rank tracking, analytics, or assistant citation tools are unavailable.

Read references/data-source-tiers.md before producing a monitoring report.

Owns

  • credential-free monitoring baseline;
  • server access-log summary with IP privacy protection;
  • crawler/user-agent observation reports;
  • public HTTP checks for robots, llms.txt, sitemaps, RSS, and representative URLs;
  • data-source tiering;
  • monitoring limitations and unknowns;
  • backlog handoff for Search Console, rank/SERP, analytics, and LLM citation monitoring.

Does Not Own

  • credentialed Search Console or analytics API work;
  • scraping search engines;
  • bypassing bot protections;
  • live firewall or WAF changes;
  • claims about rankings or assistant citations without direct evidence;
  • storing raw IP logs in public artifacts.

Workflow

  1. Identify available evidence: public URL fetches, access-log snippet, exported summary, or synthetic fixture.
  2. Label the evidence tier and collection time.
  3. If logs are provided, summarize requests by path, status, user-agent class, and redacted IP hash.
  4. Separate known crawler hits from unknown bot-like traffic.
  5. Check public discovery endpoints when requested.
  6. Record limitations: no Search Console, no rank data, no assistant citation data unless explicitly provided.
  7. Produce a monitoring report using assets/crawler-monitor-report.template.md.
  8. Hand off credentialed or citation tasks to the backlog.

Non-Negotiables

  • Do not publish raw IP addresses.
  • Do not store credentials, cookies, API keys, or private analytics exports in skill files.
  • Do not infer ranking, indexing, or citation success from server logs alone.
  • Do not claim a user-agent is verified Google/OpenAI/Claude/Perplexity unless verification method is documented.
  • Do not scrape SERPs or assistants against terms of service.
  • Do not change firewall, WAF, robots, or server config from this skill.

Safety And Privacy Boundaries

  • Treat raw logs as private by default.
  • Use aggregated counts and redacted identifiers.
  • Keep exact timestamps only when needed for evidence; otherwise aggregate by hour/day.
  • If the user asks to publish a report, remove raw IP, query strings, session IDs, auth paths, and admin URLs unless explicitly needed and safe.
  • Public reports should say "observed in provided logs" rather than "definitely crawled by" unless bot verification is complete.

Evidence Labels

  • Observed: directly in log, HTTP response, or exported report.
  • Verified: observed and checked by an accepted verification method.
  • Inferred: likely based on user-agent/path/status, but not verified.
  • Open: no evidence available in this run.

Validation

Validate skill edits with:

python3 <codex-skills-dir>/senior-skill-architect/scripts/lint_production_skill.py ./skills/server-log-crawler-analyst
python3 ./skills/server-log-crawler-analyst/scripts/analyze_access_log.py ./skills/server-log-crawler-analyst/fixtures/sample-access.log --report <tmp-dir>/server-log-crawler-report.json
python3 ./plans/seo-llm-skill-cluster/scripts/lint_skill_cluster.py .

Forward tests live in evals.json.

Output Shape

Return:

  1. Evidence source and timestamp.
  2. Data tier and privacy handling.
  3. Observed crawler/user-agent classes.
  4. Public endpoint status checks if performed.
  5. Suspicious or irrelevant bot traffic summary.
  6. Unknowns and claims refused.
  7. Monitoring backlog and next evidence needed.

리뷰

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