SuperAPI AI Directory

root-cause-analyze

Data & analytics

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Updated 11-03 10:02

About this skill

Root Cause Analyze

Purpose

Deterministic root cause: Pareto ranks cause categories; rca_tree selects the evidenced why-chain, applies the systemic test and the operator-error rejection.

When to use

After ncr-intake in every CAPA run.

Inputs

capa-intake.json + complaints.csv + references cause-links library.

Steps

  1. Validate against the contract.
  2. Run scripts/pareto.py --capa capa-intake.json --complaints complaints.csv --out pareto.json
  3. Run python3 scripts/rca_tree.py --pareto pareto.json --links references/cause-links.json --out rca.json from this skill's folder, using workspace paths for the Pareto input and generated output.
  4. Quote the chain verbatim. If the engine rejected 'operator error' (capa-rules.md #3.4), say so explicitly - a recurring defect is never closed on retraining alone.

Output

rca.json - the {root_cause} hop with pareto[], why_chain, systemic flag.

Grounding requirements

Every why must be evidenced in the record set (rca-methods.md); the chain cites the cause-link library and complaint window.

Constraints

  • Method is engine-only; the model never invents or reorders a why-chain, and never accepts the incident narrative's cause label over the Pareto.

Escalation / uncertainty

Engine escalations (#3.4 rejection, #2.2 depth, #7 confidence) pass through verbatim.