Agent skill · Code Review & Quality

rca-classical-methods

Classical root cause analysis techniques for quality improvement and incident investigation. Covers 5 Whys (with Card's 2017 critique and boundary conditions), Ishikawa/fishbone diagrams, Fault Tree Analysis (FTA), Failure Modes and Effects Analysis (FMEA), Cause Mapping, and Doggett's method-selection framework. Use when investigating a failure, running a post-incident analysis, building a fishbone, calculating fault tree cut sets, running an FMEA pass, or deciding which classical technique fits the problem. Not suitable alone for complex socio-technical failures — escalate to rca-systems-the

majiayu000github.com/majiayu000GitHub ↗
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill rca-classical-methods --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 15 KB
Bundled scripts: none
Path: skills/analysis/rca-classical-methods/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Classical RCA Methods Classical root cause analysis techniques are the inherited toolkit of quality and reliability engineering. They are best understood as a *family* with different strengths and known failure modes — not as interchangeable recipes. This skill teaches when to reach for each one, how to run it rigorously, and when to stop and escalate to a more capable method. ## The six techniques at a glance | Technique | Origin | Best for | Known weakness | |---|---|---|---| | **5 Whys** | Toyota Production System (Sakichi Toyoda, Taiichi Ohno) | Simple, linear, single-cause problems on the shop floor | Forces single pathway, non-reproducible across analysts (Card 2017) | | **Fishbone (Ishikawa)** | Kaoru Ishikawa, 1960s | Brainstorming categorized contributing factors | Can produce unwieldy diagrams, no intrinsic prioritization | | **Fault Tree Analysis (FTA)** | Bell Labs, Minuteman ICBM (1960s) | Safety-critical systems with quantifiable failure probabilities | Requires known failure modes upfront, expensive to build | | **FMEA** | U.S. military (MIL-P-1629, 1949); automotive (AIAG) | Proactive design review, risk prioritization via RPN | RPN ordinal-math is statistically u

What's inside
Steps it walks through
  1. The six techniques at a glance
  2. Technique 1 — The 5 Whys (with boundary conditions)
  3. What it is
  4. Why it often fails
  5. When you may still use it
  6. Running it rigorously
  7. Technique 2 — Fishbone / Ishikawa diagram
  8. The category spines
  9. Construction steps
  10. Evidence from Barsalou & Starzynska (2023)
  11. Technique 3 — Fault Tree Analysis (FTA)
  12. Notation
  13. Quantitative FTA
  14. When to use
Ships with 1 file
  • metadata.json
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About this skill
What does the rca-classical-methods skill do?

Classical root cause analysis techniques for quality improvement and incident investigation. Covers 5 Whys (with Card's 2017 critique and boundary conditions), Ishikawa/fishbone diagrams, Fault Tree Analysis (FTA), Failure Modes and Effects Analysis (FMEA), Cause Mapping, and Doggett's method-selection framework. Use when investigating a failure, running a post-incident analysis, building a fishbone, calculating fault tree cut sets, running an FMEA pass, or deciding which classical technique fits the problem. Not suitable alone for complex socio-technical failures — escalate to rca-systems-the

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill rca-classical-methods --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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