root-cause-diagnoser

root-cause-diagnoser is a skill for Claude Code, Codex from codebygarv/Ai-skills. It costs 36 tokens per session (309 once invoked), scanned A, original, MIT.

A structured way to investigate why a software failure happened, using repeated “why” questions and a cause-and-effect diagram. It looks beyond the immediate error to technical, process, and monitoring problems.

In plain words
What is it for?
Use it for post-incident reviews, recurring bugs, regressions, outages, and failures where the first fix did not solve the wider problem.
Why use it?
It helps avoid quick fixes that leave the same bug or outage likely to happen again. It turns an incident review into specific prevention work.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it for post-incident reviews, recurring bugs, regressions, outages, and failures where the first fix did not solve the wider problem.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/codebygarv/ai-skills/root-cause-diagnoser
Install

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.

Any agent
npx skills add codebygarv/Ai-skills --skill root-cause-diagnoser
Clone the repo
git clone --depth 1 https://github.com/codebygarv/Ai-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for root-cause-diagnoser

README.md
[![agentmods](https://agentmods.dev/badge/skills/codebygarv/ai-skills/root-cause-diagnoser.svg)](https://agentmods.dev/skills/codebygarv/ai-skills/root-cause-diagnoser)
Your own site
<a href="https://agentmods.dev/skills/codebygarv/ai-skills/root-cause-diagnoser"><img src="https://agentmods.dev/badge/skills/codebygarv/ai-skills/root-cause-diagnoser.svg" alt="Measured on agentmods" height="20"></a>
Per session 36 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 309 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00036 $0.00309
Opus 5 $0.00018 $0.00154
Sonnet 5 $0.00007 $0.00062
Haiku 4.5 $0.00004 $0.00031

Measured 5d ago against content hash 033984722a5b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

root-cause-diagnoser 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 5d 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.

skills/reasoning/root-cause-diagnoser/SKILL.md · 35 lines

What it actually says

Purpose

Drill past surface-level symptoms and immediate triggers to uncover the systemic, architectural, and organizational root causes of engineering failures.

When to Use

  • Post-incident reviews and postmortems.
  • Recurring bugs that keep resurfacing after quick patches.
  • Flaky systems where the immediate fix failed to prevent secondary fallout.

What to Analyze

  1. Immediate Trigger: What directly caused the error?
  2. Propagation Chain: How did the failure cascade through the system?
  3. Detection Delay: Why was the issue not caught in CI, staging, or monitoring?
  4. 5-Whys Iteration: Drill 5 levels deep into process, architecture, and code.
  5. Systemic Defenses: What automated safeguards or architectural barriers are missing?

Output Format

  • Incident Summary: Exact failure statement.
  • 5-Whys Diagnostic Chain: Clear causal progression from trigger to root cause.
  • Contributing Factors: Process, tooling, architecture, and observability factors.
  • Corrective Actions (Actionable): Specific engineering tickets categorized into Immediate, Medium-term, and Prevention.

Avoid

  • Stopping at human error ("Developer made a typo").
  • Proposing generic solutions like "Be more careful in code review".
Files

What ships with it

2 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.

Changes

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.

  1. 5d ago First seen · 35 lines · 36 tokens per session scan A 033984722a5b

Subscribe to this mod's changes

root-cause-diagnoser is a skill published in the GitHub repository codebygarv/Ai-skills (25 stars, last pushed 19d ago), licensed MIT. It adds 36 tokens to every session and 309 once invoked, about $0.0002 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 2026-09-03.

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