Agent skill · AI & Agents

rca-human-factors

Human-factors root cause analysis for incidents involving operators, crews, clinicians, or any human actors. Covers James Reason's Swiss Cheese Model and active/latent failure taxonomy, HFACS (Human Factors Analysis and Classification System), Just Culture algorithm (Marx/GAIN), Crew Resource Management (CRM) findings, and high-reliability organization (HRO) principles. Use when investigating incidents in aviation, healthcare, nuclear operations, emergency response, or any context where "operator error" is a tempting but shallow explanation and the real question is what organizational and desi

majiayu000github.com/majiayu000GitHub ↗
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill rca-human-factors --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-human-factors/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

# Human-Factors RCA "The root cause was human error" is almost never a useful investigation finding. It stops the inquiry exactly where it should begin. This skill teaches the frameworks that treat human performance as a window into the system that produced it — so you end up redesigning the system rather than retraining the operator. ## Why "human error" is a symptom, not a cause Sidney Dekker's principle (2014, *The Field Guide to Understanding "Human Error"*): if your RCA concludes with a person doing something wrong, you are roughly 20% through the investigation. The next 80% is the question: *why was that action reasonable from where the person was standing, with the information they had, under the pressures they faced?* This reframing is not soft on accountability. It is the opposite: it refuses to let organizational design off the hook by letting the nearest human take the blame. ## Framework 1 — Reason's Swiss Cheese Model James Reason (*Managing the Risks of Organizational Accidents*, 1997) proposed that defenses against accidents are arranged in layers, each with weaknesses ("holes"). An accident occurs when holes in successive layers align and a hazard passes through all

What's inside
Steps it walks through
  1. Why "human error" is a symptom, not a cause
  2. Framework 1 — Reason's Swiss Cheese Model
  3. The slices
  4. Active vs. latent failures
  5. Perneger's 2005 validation
  6. Framework 2 — HFACS (Human Factors Analysis and Classification System)
  7. The HFACS hierarchy
  8. How to apply HFACS
  9. Why it works
  10. HFACS variants
  11. Framework 3 — Just Culture algorithm
  12. The three behaviors
  13. The Marx algorithm for a specific action
  14. Counterexample: punitive response undoes reporting
Ships with 1 file
  • metadata.json
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About this skill
What does the rca-human-factors skill do?

Human-factors root cause analysis for incidents involving operators, crews, clinicians, or any human actors. Covers James Reason's Swiss Cheese Model and active/latent failure taxonomy, HFACS (Human Factors Analysis and Classification System), Just Culture algorithm (Marx/GAIN), Crew Resource Management (CRM) findings, and high-reliability organization (HRO) principles. Use when investigating incidents in aviation, healthcare, nuclear operations, emergency response, or any context where "operator error" is a tempting but shallow explanation and the real question is what organizational and desi

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill rca-human-factors --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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