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 agentmods add skills/milkyway008/hermes-otg/systematic-debuggingnpx skills add MilkyWay008/Hermes-OTG --skill systematic-debugginggit clone --depth 1 https://github.com/MilkyWay008/Hermes-OTGWrote 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/milkyway008/hermes-otg/systematic-debugging)<a href="https://agentmods.dev/skills/milkyway008/hermes-otg/systematic-debugging"><img src="https://agentmods.dev/badge/skills/milkyway008/hermes-otg/systematic-debugging.svg" alt="Measured on agentmods" 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 | $0.00016 | $0.03263 |
| Opus 5 | $0.00008 | $0.01631 |
| Sonnet 5 | $0.00003 | $0.00653 |
| Haiku 4.5 | $0.00002 | $0.00326 |
Grade A, and why
systematic-debugging scanned grade A with 1 finding 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 2d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
2. **HTTP script / curl** against a running dev server. This is a copy
100% identical to systematic-debugging — 2 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.
How it starts
The opening of the file, as written. The whole thing — 412 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Systematic Debugging
Overview
Random fixes waste time and create new bugs. Quick patches mask underlying issues.
Core principle: ALWAYS find root cause before attempting fixes. Symptom fixes are failure.
Violating the letter of this process is violating the spirit of debugging.
The Iron Law
NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRST
If you haven't completed Phase 1, you cannot propose fixes.
The Feedback Loop Rule
The feedback loop is the debugging work. Before reading code to build a theory, create or identify a tight command that can go red on the user's exact symptom and green when the bug is fixed. A tight loop is fast, deterministic, agent-runnable, and specific enough to catch this bug — not merely "doesn't crash".
When a clean repro is hard, spend disproportionate effort building the loop. Guessing without a red-capable loop is the failure mode this skill exists to prevent.
When to Use
Use for ANY technical issue:
- Test failures
- Bugs in production
- Unexpected behavior
- Performance problems
- Build failures
- Integration issues
Use this ESPECIALLY when:
- Under time pressure (emergencies make guessing tempting)
- "Just one quick fix" seems obvious
- You've already tried multiple fixes
- Previous fix didn't work
- You don't fully understand the issue
Don't skip when:
- Issue seems simple (simple bugs have root causes too)
- You're in a hurry (rushing guarantees rework)
- Someone wants it fixed NOW (systematic is faster than thrashing)
The Four Phases
You MUST complete each phase before proceeding to the next.
Phase 1: Root Cause Investigation
BEFORE attempting ANY fix:
1. Read Error Messages Carefully
- Don't skip past errors or warnings
- They often contain the exact solution
- Read stack traces completely
- Note line numbers, file paths, error codes
Action: Use read_file on the relevant source files. Use search_files to find the error string in the codebase.
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.
- 2d ago First seen · 412 lines · 16 tokens per session scan A ec6c97b1d553
systematic-debugging is a skill published in the GitHub repository MilkyWay008/Hermes-OTG (14 stars, last pushed 21d ago), licensed MIT. It adds 16 tokens to every session and 3,263 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 100% identical to systematic-debugging, differing in 2 lines, and is treated as a copy.
Other skills, from other repositories
acquiring-disk-image-with-dd-and-dcfldd
Create forensically sound bit-for-bit disk images with dd or dcfldd on a Linux forensic workstation, preserving evidence integrity through hash verification (MD5/SHA) during acquisition. Use when imaging a suspect drive, USB device, or memory card for investigation, preserving volatile disk evidence during incident…
analyzing-linux-kernel-rootkits
Detect kernel-level rootkits in Linux memory dumps using Volatility3 linux plugins (checksyscall, lsmod, hiddenmodules), rkhunter system scanning, and /proc vs /sys discrepancy analysis to identify hooked syscalls, hidden kernel modules, and tampered system structures.
hunt-auth-bypass
Hunting skill for auth bypass vulnerabilities. Built from 12 public bug bounty reports across SAML XSW / parser-differential (GitHub Enterprise CVE-2025-25291/25292), SAML signature stripping (Uber, Rocket.Chat, samlify CVE-2025-47949), SAML domain enforcement bypass via control characters (HackerOne 2024)…
subdomain-enumeration
Map subdomains via crt.sh and subfinder at recon kickoff.
tianya-gods-team
天涯神贴大神智囊团 — 20位传奇大神组成的多Agent决策系统。 基于ClawTeam多Agent协作原理，实现「一个目标 → 20位大神并行分析 → 综合决策」的完整工作流。 核心机制： 智能领导者Agent（协调者）接收目标，分配给20位专业大神 20位大神并行分析，各自输出专业视角 综合决策Agent整合所有视角，输出最终决策 使用方式： 「启动天涯智囊团」+ 你的问题/目标 「召集大神」+ 你的问题/目标 「20位大神分析」+ 你的问题/目标 涵盖领域：权谋分析、经济预言、历史解读、文化解构、都市情感、技术分析、 黑帮江湖、盗墓探险、玄幻武侠、都市异能、悬疑推理、法医刑侦、历史穿越等。.
attack-patterns-reference
Use when classifying a verified web or WordPress behavior and selecting a related validation skill.