Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add pinkpixel-dev/skills-collection-1 --skill analyzing-web-server-logs-for-intrusiongit clone --depth 1 https://github.com/pinkpixel-dev/skills-collection-1Wrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/pinkpixel-dev/skills-collection-1/analyzing-web-server-logs-for-intrusion)<a href="https://agentmods.dev/skills/pinkpixel-dev/skills-collection-1/analyzing-web-server-logs-for-intrusion"><img src="https://agentmods.dev/badge/skills/pinkpixel-dev/skills-collection-1/analyzing-web-server-logs-for-intrusion/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/pinkpixel-dev/skills-collection-1/analyzing-web-server-logs-for-intrusion"><img src="https://agentmods.dev/badge/skills/pinkpixel-dev/skills-collection-1/analyzing-web-server-logs-for-intrusion.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00073 | $0.00600 |
| Opus 5.5 | $0.00029 | $0.00240 |
| Sonnet 5.5 | $0.00015 | $0.00120 |
| Haiku 4.5 | $0.00007 | $0.00060 |
Grade A, and why
analyzing-web-server-logs-for-intrusion scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
91% identical to analyzing-web-server-logs-for-intrusion — 32 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Analyzing Web Server Logs for Intrusion
When to Use
- When investigating security incidents that require analyzing web server logs for intrusion
- When building detection rules or threat hunting queries for this domain
- When SOC analysts need structured procedures for this analysis type
- When validating security monitoring coverage for related attack techniques
Prerequisites
- Familiarity with security operations concepts and tools
- Access to a test or lab environment for safe execution
- Python 3.8+ with required dependencies installed
- Appropriate authorization for any testing activities
Instructions
- Install dependencies:
pip install geoip2 user-agents - Collect web server access logs in Combined Log Format (Apache) or Nginx default format.
- Parse each log entry extracting: IP, timestamp, method, URI, status code, response size, user-agent, referer.
- Apply detection rules:
- SQL injection:
UNION SELECT,OR 1=1,' OR ', hex encoding patterns - LFI/Path traversal:
../,/etc/passwd,/proc/self,php://filter - XSS:
<script>,javascript:,onerror=,onload= - Scanner signatures: nikto, sqlmap, dirbuster, gobuster, wfuzz user-agents
- Brute force: >50 POST requests to login endpoints from same IP in 5 minutes
- SQL injection:
- Enrich with GeoIP data and generate a prioritized findings report.
python scripts/agent.py --log-file /var/log/nginx/access.log --geoip-db GeoLite2-City.mmdb --output web_intrusion_report.json
Examples
Detect SQLi in URI
192.168.1.100 - - [15/Jan/2024:10:30:45 +0000] "GET /products?id=1' UNION SELECT username,password FROM users-- HTTP/1.1" 200 4532
Scanner User-Agent Detection
Nikto/2.1.6, sqlmap/1.7, DirBuster-1.0-RC1, gobuster/3.1.0
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 62 lines · 73 tokens per session scan A 29ca08d02a65
analyzing-web-server-logs-for-intrusion is a skill published in the GitHub repository pinkpixel-dev/skills-collection-1 (9 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 73 tokens to every session and 600 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 91% identical to analyzing-web-server-logs-for-intrusion, differing in 32 lines, and is treated as a copy.
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analyzing-web-server-logs-for-intrusion
Parse Apache and Nginx access logs to detect SQL injection attempts, local file inclusion, directory traversal, web scanner fingerprints, and brute-force patterns. Uses regex-based pattern matching against OWASP attack signatures, GeoIP enrichment for source attribution, and statistical anomaly detection for request…
analyzing-web-server-logs-for-intrusion
Parse Apache and Nginx access logs to detect SQL injection attempts, local file inclusion, directory traversal, web scanner fingerprints, and brute-force patterns. Uses regex-based pattern matching against OWASP attack signatures, GeoIP enrichment for source attribution, and statistical anomaly detection for request…
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