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 Mikaru0Mystic/sectinel --skill analyzing-api-gateway-access-logsgit clone --depth 1 https://github.com/Mikaru0Mystic/sectinelWrote 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/mikaru0mystic/sectinel/analyzing-api-gateway-access-logs)<a href="https://agentmods.dev/skills/mikaru0mystic/sectinel/analyzing-api-gateway-access-logs"><img src="https://agentmods.dev/badge/skills/mikaru0mystic/sectinel/analyzing-api-gateway-access-logs/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/mikaru0mystic/sectinel/analyzing-api-gateway-access-logs"><img src="https://agentmods.dev/badge/skills/mikaru0mystic/sectinel/analyzing-api-gateway-access-logs.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 | $0.00072 | $0.00500 |
| Opus 5 | $0.00036 | $0.00250 |
| Sonnet 5 | $0.00014 | $0.00100 |
| Haiku 4.5 | $0.00007 | $0.00050 |
Grade A, and why
analyzing-api-gateway-access-logs 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 10d 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
89% identical to analyzing-api-gateway-access-logs — 22 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 API Gateway Access Logs
When to Use
- When investigating security incidents that require analyzing api gateway access logs
- 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
Parse API gateway access logs to identify attack patterns including broken object level authorization (BOLA), excessive data exposure, and injection attempts.
import pandas as pd
df = pd.read_json("api_gateway_logs.json", lines=True)
# Detect BOLA: same user accessing many different resource IDs
bola = df.groupby(["user_id", "endpoint"]).agg(
unique_ids=("resource_id", "nunique")).reset_index()
suspicious = bola[bola["unique_ids"] > 50]
Key detection patterns:
- BOLA/IDOR: sequential resource ID enumeration
- Rate limit bypass via header manipulation
- Credential scanning (401 surges from single source)
- SQL/NoSQL injection in query parameters
- Unusual HTTP methods (DELETE, PATCH) on read-only endpoints
Examples
# Detect 401 surges indicating credential scanning
auth_failures = df[df["status_code"] == 401]
scanner_ips = auth_failures.groupby("source_ip").size()
scanners = scanner_ips[scanner_ips > 100]
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.
- 10d ago First seen · 72 lines · 72 tokens per session scan A d18100e444b9
analyzing-api-gateway-access-logs is a skill published in the GitHub repository Mikaru0Mystic/sectinel (11 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 72 tokens to every session and 500 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to analyzing-api-gateway-access-logs, differing in 22 lines, and is treated as a copy.
Other skills, from other repositories
analyzing-api-gateway-access-logs
Use when parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect BOLA/IDOR attacks, rate limit bypass, credential scanning, and injection attempts. Uses pandas for statistical analysis of request patterns and anomaly detection. Use when investigating API abuse or building API-specific threat detection…
analyzing-api-gateway-access-logs
Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect BOLA/IDOR attacks, rate limit bypass, credential scanning, and injection attempts. Uses pandas for statistical analysis of request patterns and anomaly detection. Use when investigating API abuse or building API-specific threat detection rules.
analyzing-api-gateway-access-logs
A log-analysis guide for API gateways such as AWS API Gateway, Kong, and Nginx. It uses request records to spot patterns linked to common API attacks.
analyzing-api-gateway-access-logs
Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect BOLA/IDOR attacks, rate limit bypass, credential scanning, and injection attempts. Uses pandas for statistical analysis of request patterns and anomaly detection. Use when investigating API abuse or building API-specific threat detection rules.
analyzing-api-gateway-access-logs
Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect BOLA/IDOR attacks, rate limit bypass, credential scanning, and injection attempts. Uses pandas for statistical analysis of request patterns and anomaly detection. Use when investigating API abuse or building API-specific threat detection rules.
analyzing-api-gateway-access-logs
Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect BOLA/IDOR attacks, rate limit bypass, credential scanning, and injection attempts. Uses pandas for statistical analysis of request patterns and anomaly detection. Use when investigating API abuse or building API-specific threat detection rules.