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 DevelopersGlobal/ai-agent-skills --skill debugging-methodologygit clone --depth 1 https://github.com/DevelopersGlobal/ai-agent-skillsWrote 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/developersglobal/ai-agent-skills/debugging-methodology)<a href="https://agentmods.dev/skills/developersglobal/ai-agent-skills/debugging-methodology"><img src="https://agentmods.dev/badge/skills/developersglobal/ai-agent-skills/debugging-methodology/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/developersglobal/ai-agent-skills/debugging-methodology"><img src="https://agentmods.dev/badge/skills/developersglobal/ai-agent-skills/debugging-methodology.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 | $0.00026 | $0.00942 |
| Opus 5.5 | $0.00010 | $0.00377 |
| Sonnet 5.5 | $0.00005 | $0.00188 |
| Haiku 4.5 | $0.00003 | $0.00094 |
Grade A, and why
debugging-methodology 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.
How it starts
The opening of the file, as written. The whole thing — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
Random code changes in response to errors are not debugging — they're noise generation. This skill enforces a systematic, hypothesis-driven approach: understand the problem, form a hypothesis, test it, confirm the root cause, then fix.
AI agents often cycle through random fixes until something "works." This skill prevents that.
When to Use
- Any time a test fails unexpectedly
- Any time you encounter an error or exception
- When behavior differs between environments
- When performance degrades unexpectedly
Process
Step 1: Reproduce Reliably
- Before doing anything else: reproduce the bug reliably. If you can't reproduce it, you can't fix it.
- Write a failing test that captures the bug — this becomes your regression test.
- Note the exact conditions that trigger the bug: inputs, environment, sequence of actions.
Verify: You can trigger the bug on demand.
Step 2: Understand Before Diagnosing
- Read the full error message — not just the first line.
- Read the stack trace from bottom to top — the root cause is usually near the bottom.
- Identify: What was the program trying to do? What happened instead?
Verify: You can explain the bug in one sentence without using the word "error."
Step 3: Form a Hypothesis
- Based on what you know, form a specific hypothesis: "I think the bug is X because Y."
- The hypothesis must be falsifiable — you can design a test that proves or disproves it.
- Do not start making code changes until you have a hypothesis.
Verify: Your hypothesis is specific enough to design a test for.
Step 4: Test the Hypothesis
- Add targeted logging or a targeted test that confirms or refutes the hypothesis.
- Run it. Read the output carefully.
- If the hypothesis is wrong: update your understanding, form a new hypothesis, repeat.
- If the hypothesis is right: you've found the root cause.
Verify: Root cause is confirmed by evidence, not assumed.
Step 5: Fix the Root Cause (Not the Symptom)
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 · 102 lines · 26 tokens per session scan A 1e001991f84c
debugging-methodology is a skill published in the GitHub repository DevelopersGlobal/ai-agent-skills (177 stars, last pushed 5mo ago), licensed MIT. It adds 26 tokens to every session and 942 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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