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 05-deepak-patidar/claude-skills --skill root-cause-debugginggit clone --depth 1 https://github.com/05-deepak-patidar/claude-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/05-deepak-patidar/claude-skills/root-cause-debugging)<a href="https://agentmods.dev/skills/05-deepak-patidar/claude-skills/root-cause-debugging"><img src="https://agentmods.dev/badge/skills/05-deepak-patidar/claude-skills/root-cause-debugging/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/05-deepak-patidar/claude-skills/root-cause-debugging"><img src="https://agentmods.dev/badge/skills/05-deepak-patidar/claude-skills/root-cause-debugging.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 | $0.00079 | $0.01035 |
| Opus 5 | $0.00039 | $0.00517 |
| Sonnet 5 | $0.00016 | $0.00207 |
| Haiku 4.5 | $0.00008 | $0.00103 |
Grade A, and why
root-cause-debugging 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 9d 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 — 40 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Root-Cause Debugging
Debugging is not staring at code hoping for insight; it is running experiments against hypotheses until only one survives. The cardinal sin is fixing the symptom while the cause survives — that bug returns wearing a different coat.
The loop
- Reproduce first. A bug you can trigger on demand is 80% solved. Capture: exact input, environment, frequency (always? sometimes? one tenant?). If you can't reproduce, your job changes: add instrumentation to catch it in the act, don't guess-fix.
- State the expected vs actual precisely. "It's broken" → "POST /payments returns 500 for amounts > 999 since Tuesday's deploy". The precision itself often reveals the cause.
- Find what changed. Bugs that appear have causes that arrived: last deploy (check the version fingerprint, not assumptions), dependency bump, config/env change, data shape change (first tenant with 10k products), date rollover (month/FY boundaries), certificate/token expiry.
git logand deploy history before code-reading. - Bisect the path. Split the request's journey (client → network → handler → service → DB → response) and test the midpoint: is the data wrong entering the service or leaving it? Halving beats reading everything.
git bisectwhen "which commit" is the question. - One hypothesis, one experiment, one variable. Write the hypothesis down ("the total is wrong because line discounts apply twice"), design the cheapest experiment that could disprove it, run it. Changing three things and seeing improvement teaches you nothing.
- Confirm the mechanism before fixing. You've found root cause when you can (a) explain the mechanism end-to-end, (b) predict how to trigger AND how to prevent it, and (c) explain any weird details (why only Tuesdays? why only that tenant?). Unexplained details mean an unfound second cause — the fix that "works but I don't know why" is a time bomb.
Reading evidence properly
- Read the actual error, all of it, slowly. The answer is in the stack trace's first frame in your code and the message's exact wording, more often than pride admits. The error you see may be downstream wreckage — find the first error in the timeline.
- Logs: reconstruct the timeline around one failing request via its correlation ID (observability-readiness). Compare against one succeeding request — the diff between them is the clue.
- Trust evidence over models: if the logs say the function received X and your mental model says impossible, the model is wrong. Print/log the actual values at the boundary in dispute; don't re-read code that "obviously" can't do that. It did.
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.
- 9d ago First seen · 40 lines · 79 tokens per session scan A 8323b4dd2ce0
root-cause-debugging is a skill published in the GitHub repository 05-deepak-patidar/claude-skills (4 stars, last pushed 2mo ago), licensed MIT. It adds 79 tokens to every session and 1,035 once invoked, about $0.0004 per session on Opus 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 2026-08-31.
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optimize
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code-review
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