diagnostic-action
Use when the user asks why something happened, what’s wrong, root cause analysis, debugging, or incident explanation.
Best use case
diagnostic-action is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Use when the user asks why something happened, what’s wrong, root cause analysis, debugging, or incident explanation.
Teams using diagnostic-action should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/diagnostic-action/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How diagnostic-action Compares
| Feature / Agent | diagnostic-action | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Use when the user asks why something happened, what’s wrong, root cause analysis, debugging, or incident explanation.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Diagnostic Action Skill ## Purpose Identify and explain likely causes of an observed problem or symptom. ## When to use - “Why did this happen?” - Root cause analysis - Debugging / incident investigation - Failure analysis Do NOT use for: - recommendations or fixes (use prescriptive-actions) - plans or procedures (use planning-action / procedural-action) ## Operating rules 1. Restate symptoms and scope clearly. 2. Generate multiple plausible hypotheses. 3. Explain the mechanism for each hypothesis. 4. Rank by likelihood and impact. 5. Identify missing data that would disambiguate causes. 6. Do not prescribe fixes unless explicitly asked. ## Outputs ### Symptoms & scope ### Hypotheses (ranked) - Cause - Mechanism - Evidence for/against - Confidence ### Most likely causes ### Data needed / open questions
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