跳到正文
cccskills
免费在 GitHub 公开

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.

中文简介正在准备中。当前显示原文说明。

集成工具Search Console
查看安装方法

包含的文件(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(原文)

安装前可以查看交给 AI 代理的指令内容。

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.

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

Validate proposed or existing backlinks for relevance, editorial legitimacy, link attributes, spam/toxicity risk, platform compliance, anchor quality, target-page fit, and monitoring evidence. Use this skill when the user asks whether an external link opportunity or placed backlink is safe and useful, and reject toxic, irrelevant, paid-undisclosed, fake, or spam placements.

中文简介正在准备中。当前显示原文说明。

sergekostenchuk/seo-llm-skill-cluster402026年6月12日 更新

Review public site content for editorial quality, source support, prompt residue, duplicate blocks, translation drift, hidden filler, and human usefulness before SEO or LLM-friendly publication. Use this skill when the user asks to quality-gate news briefs, longform explainers, blog posts, topic pages, project pages, translated content, or generated article drafts without changing the live publishing pipeline.

中文简介正在准备中。当前显示原文说明。

sergekostenchuk/seo-llm-skill-cluster402026年6月12日 更新

Find and score legitimate external authority and backlink opportunities as a dry-run register, using relevance, platform rules, risk, target-page fit, and explicit user approval gates. Use this skill when the user asks where links to a target site could be placed effectively, but never post, submit, DM, email, open PRs, or edit external accounts without separate explicit approval.

中文简介正在准备中。当前显示原文说明。

sergekostenchuk/seo-llm-skill-cluster402026年6月12日 更新

Convert a semantic core into crawlable SEO and LLM-friendly information architecture. Use this skill when the user asks for URL structure, page taxonomy, canonical rules, hreflang groups, page roles, pillar/topic/news/article/blog/project separation, multilingual route strategy, or a durable content model such as one short brief plus one longform article. It prevents duplicate public content bodies and hands link-graph work to internal-link-graph-architect.

中文简介正在准备中。当前显示原文说明。

sergekostenchuk/seo-llm-skill-cluster402026年6月12日 更新

Design intentional internal link graphs for SEO, UX, and LLM-readable sites. Use this skill when the user asks for links between short briefs, longform articles, topics, projects, author pages, breadcrumbs, source trails, related stories, orphan-page checks, overlink checks, anchor intent, or validation that a link graph reinforces the canonical content model without creating duplicate story bodies or hidden links.

中文简介正在准备中。当前显示原文说明。

sergekostenchuk/seo-llm-skill-cluster402026年6月12日 更新

Plan and record evidence-backed LLM and AI-search citation checks across approved assistant/search surfaces with timestamped queries, locales, modes, cited URLs, snippets, screenshots or transcripts, and caveats. Use this skill when the user asks whether ChatGPT, Perplexity, Claude, Gemini, Copilot, or another assistant cites a target site, but do not claim citation without direct evidence.

中文简介正在准备中。当前显示原文说明。

sergekostenchuk/seo-llm-skill-cluster402026年6月12日 更新

查看 sergekostenchuk 的全部技能

报告此技能的问题