SkillKit is a package manager for AI coding-agent skills that installs skills from many sources and translates them between the different formats used by coding agents. It helps developers share skills across agents such as Claude Code, Cursor, Copilot, and Codex.
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 rohitg00/skillkit --skill root-cause-analysisgit clone --depth 1 https://github.com/rohitg00/skillkitWrote 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/rohitg00/skillkit/root-cause-analysis)<a href="https://agentmods.dev/skills/rohitg00/skillkit/root-cause-analysis"><img src="https://agentmods.dev/badge/skills/rohitg00/skillkit/root-cause-analysis/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/rohitg00/skillkit/root-cause-analysis"><img src="https://agentmods.dev/badge/skills/rohitg00/skillkit/root-cause-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00110 | $0.01207 |
| Opus 5.5 | $0.00044 | $0.00483 |
| Sonnet 5.5 | $0.00022 | $0.00241 |
| Haiku 4.5 | $0.00011 | $0.00121 |
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 1mo 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 — 175 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Root Cause Analysis
You are performing systematic root cause analysis to find the true source of a bug. Do not apply fixes until you understand WHY the bug exists.
Core Principle
Never fix a symptom. Always find and fix the root cause.
The Five Whys Method
Ask "Why?" repeatedly to drill down to the root cause:
- Why did the API return an error? → The database query failed
- Why did the database query fail? → The connection pool was exhausted
- Why was the pool exhausted? → ROOT CAUSE: Missing
finallyblock to close connections
Investigation Phases
Phase 1: Reproduce the Bug
Before investigating:
- Reproduce consistently - If you can't reproduce it, you can't verify a fix
- Document reproduction steps - Exact sequence of actions
- Note environment details - OS, versions, configuration
- Identify minimal reproduction - Smallest case that shows the bug
Questions to answer:
- Does it happen every time or intermittently?
- Does it happen in all environments?
- When did it start happening? (recent changes)
Phase 2: Gather Evidence
Collect information before forming theories:
- Error messages and stack traces
- Log files (application, system, database)
- Recent code changes (git log, blame)
- User reports and reproduction steps
- Monitoring data (metrics, APM)
- Related issues (search issue tracker)
Do NOT:
- Make changes while gathering evidence
- Assume you know the cause without evidence
- Ignore related symptoms
Phase 3: Form Hypotheses
Based on evidence, create ranked hypotheses:
| Priority | Hypothesis | Evidence | Test Plan |
|---|---|---|---|
| 1 | Connection leak in UserService | Stack trace shows connection pool | Add logging, check usage |
| 2 | Query timeout too short | Occurs under load | Test with longer timeout |
| 3 | Database server overload | Correlates with peak hours | Check DB metrics |
For each hypothesis:
- What evidence supports it?
- What evidence contradicts it?
- How can we test it?
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.
- 1mo ago First seen · 175 lines · 110 tokens per session scan A d7f5da21f957
root-cause-analysis is a skill published in the GitHub repository rohitg00/skillkit (1,556 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 110 tokens to every session and 1,207 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-08-30.
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