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 latestaiagents/agent-skills --skill root-cause-analysisgit clone --depth 1 https://github.com/latestaiagents/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/latestaiagents/agent-skills/root-cause-analysis)<a href="https://agentmods.dev/skills/latestaiagents/agent-skills/root-cause-analysis"><img src="https://agentmods.dev/badge/skills/latestaiagents/agent-skills/root-cause-analysis.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 | $0.00079 | $0.01621 |
| Opus 5 | $0.00039 | $0.00811 |
| Sonnet 5 | $0.00016 | $0.00324 |
| Haiku 4.5 | $0.00008 | $0.00162 |
Grade A, and why
root-cause-analysis 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 8d 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 — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Root Cause Analysis (RCA)
Find the real cause, not just the symptoms, to prevent recurrence.
RCA Principles
- Look for systems failures, not human errors
- Ask "why" until you find actionable causes
- Multiple contributing factors are common
- Prevention > blame
Method 1: 5 Whys
Keep asking "why" until you reach an actionable root cause.
Example: API Outage
Problem: API returned 500 errors for 45 minutes
Why #1: Why did the API return 500 errors?
→ The database connection pool was exhausted
Why #2: Why was the connection pool exhausted?
→ Connections weren't being released after queries
Why #3: Why weren't connections being released?
→ A code change introduced a bug that skipped connection.close()
Why #4: Why wasn't this caught before production?
→ Our integration tests don't check for connection leaks
Why #5: Why don't integration tests check for connection leaks?
→ We haven't implemented connection pool monitoring in tests
ROOT CAUSE: Missing connection leak detection in test suite
ACTION: Add connection pool assertions to integration tests
5 Whys Guidelines
| Do | Don't |
|---|---|
| Use data, not assumptions | Stop at "human error" |
| Consider multiple branches | Accept vague answers |
| Verify each "because" | Skip to conclusions |
| Look for systemic issues | Blame individuals |
Method 2: Contributing Factors Analysis
Most incidents have multiple contributing factors.
┌─────────────────────────────────────────────────────────────┐
│ INCIDENT: API OUTAGE │
├─────────────────────────────────────────────────────────────┤
│ │
│ Direct Cause: │
│ └─ Database connection pool exhaustion │
│ │
│ Contributing Factors: │
│ ├─ [Code] Connection leak bug in PR #1234 │
│ ├─ [Process] Code review didn't catch the bug │
│ ├─ [Testing] No connection leak tests │
│ ├─ [Monitoring] No alert for connection pool usage │
│ ├─ [Deploy] Deployed during high-traffic period │
│ └─ [Recovery] Runbook for this scenario was outdated │
│ │
│ Environmental Factors: │
│ ├─ Team was understaffed (vacation season) │
│ └─ Similar incident 6 months ago, action items incomplete │
│ │
└─────────────────────────────────────────────────────────────┘
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.
- 8d ago First seen · 215 lines · 79 tokens per session scan A 55bad5cee7e7
root-cause-analysis is a skill published in the GitHub repository latestaiagents/agent-skills (5 stars, last pushed 4mo ago), licensed MIT. It adds 79 tokens to every session and 1,621 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.
Other skills, from other repositories
ai-critique
A general diagnosis assistant for product, operations, technical, and software work. It analyzes supplied material and produces a summary, conclusions, action suggestions, and reusable deliverables.
ai-equipment-failure-rca-draft
A root-cause analysis draft assistant for investigating equipment failures. Root-cause analysis means looking for the underlying reason a failure happened, not only its visible symptom.
ai-performance
A performance diagnosis assistant for finding what may be slowing a service or product down.
ai-quality-complaint-8d-report
A business-diagnosis assistant for creating or reviewing an 8D report. An 8D report is a structured method for investigating a quality problem and documenting corrective action.
langsmith-tracing
LangSmith tracing and debugging setup for LLM applications. Configure observability, capture traces, and enable debugging for LangChain/LangGraph agents.
quality-hooks
Language-specific auto-lint/format/typecheck pipeline. Supports Python (ruff+pyright), TypeScript (prettier+eslint+tsc), Go (gofmt+golangci-lint). Auto-fix and convergence loops.