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 evangelosmeklis/thufir --skill root-cause-analysisgit clone --depth 1 https://github.com/evangelosmeklis/thufirWrote 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/evangelosmeklis/thufir/root-cause-analysis)<a href="https://agentmods.dev/skills/evangelosmeklis/thufir/root-cause-analysis"><img src="https://agentmods.dev/badge/skills/evangelosmeklis/thufir/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 | $0.00067 | $0.02196 |
| Opus 5 | $0.00034 | $0.01098 |
| Sonnet 5 | $0.00013 | $0.00439 |
| Haiku 4.5 | $0.00007 | $0.00220 |
Grade A, and why
Root Cause Analysis 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 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 — 304 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Root Cause Analysis Methodology
Overview
Root cause analysis (RCA) is a systematic investigation process to identify the underlying cause of production incidents, errors, and outages. This skill provides structured methodologies for conducting effective RCA that goes beyond surface-level symptoms to find actionable root causes.
When to Use This Skill
Apply this skill when:
- Production alerts fire indicating system degradation
- Users report errors or unexpected behavior
- Incidents occur requiring post-mortem investigation
- Metrics show anomalous patterns
- Any situation requiring systematic debugging of production issues
Core RCA Principles
1. Timeline Reconstruction
Establish a clear timeline of events:
- Identify when the issue first appeared (error logs, metrics, user reports)
- Note when alerts fired or detection occurred
- Map recent changes (deployments, configuration changes, infrastructure changes)
- Identify when the issue resolved (if applicable)
Create a visual timeline connecting:
- WHEN: Timestamps of key events
- WHAT: What changed or broke at each point
- WHERE: Which systems, services, or components were affected
2. Symptom vs. Root Cause
Distinguish between symptoms and root causes:
Symptoms are observable effects:
- "API returning 500 errors"
- "Database queries timing out"
- "Memory usage at 95%"
Root causes are underlying reasons:
- "Connection pool exhausted due to size reduction in deployment"
- "Missing database index causing full table scans"
- "Memory leak introduced in commit abc123"
Always trace from symptoms to root causes by asking "why?" repeatedly.
3. The Five Whys Technique
Ask "why?" five times to drill down from symptom to root cause:
Example:
- Why are users seeing errors? → API is returning 500s
- Why is API returning 500s? → Database queries are timing out
- Why are queries timing out? → Connection pool is exhausted
- Why is connection pool exhausted? → Pool size was reduced from 100 to 10
- Why was pool size reduced? → Deployment of commit abc123 changed configuration
What ships with it
3 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.
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 · 304 lines · 67 tokens per session scan A e850557fab94
Root Cause Analysis Methodology is a skill published in the GitHub repository evangelosmeklis/thufir (7 stars, last pushed 8mo ago), licensed MIT. It adds 67 tokens to every session and 2,196 once invoked, about $0.0003 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
debug-optimize-lcp
Guides debugging and optimizing Largest Contentful Paint (LCP) using Chrome DevTools MCP tools. Use this skill whenever the user asks about LCP performance, slow page loads, Core Web Vitals optimization, or wants to understand why their page's main content takes too long to appear. Also use when the user mentions…
systematic-debugging
Use when debugging a failing test, build error, or runtime issue that isn't immediately obvious. Guides a 4-phase root cause analysis instead of random fix attempts.
diagnose
Trace from a reproduced symptom to the source code that causes it. Pin the specific file and approximate line, rate confidence in the cause and clarity of the fix independently, and always propose a concrete fix.
repro-admin
Reproduce an EmDash admin UI bug. Attach a container, start the demo dev server, drive the admin with agent-browser using the dev-bypass session, and capture the reproduction as screenshots plus a replayable transcript.
log-error-digest
Analyze log files to troubleshoot errors, identify peak error periods, and produce error clustering, frequency statistics, and time distribution reports. Supports JSON, syslog, and Nginx formats with automatic detection. Use when a user uploads a .log file and asks to analyze errors, find patterns, debug issues, or…
byted-util-volcengine-detect-retry
An orchestration workflow for Volcengine Cloud Detect, a service that checks websites or network endpoints from test locations.