quantipixels Root Cause
Find the smallest causal mechanism or sufficient causal set that explains the observed failure and downstream symptoms. Diagnosis stays separate from triage/review/correction delivery.
Delegate substantial analysis, research, and expert work to subagents, returning concise findings and evidence links to keep the main context lean.
A useful model may be:
trigger + enabling conditions + propagation + missing containment/detection → observed failure
Pin the failure
Record exact symptom, expected behavior, first known trigger, candidate/revision or event identity, environment/context, reproducibility, evidence, scope, and read/probe authority. A report, stack trace, correlation, changed artifact, or temporal order is evidence, not a cause.
Reproduce safely when possible; otherwise pin one equivalent direct observation. Separate primary failure from secondary errors, retries, compensating behavior, and recovery noise.
Competing mechanisms
Write a small hypothesis table. For each hypothesis state trigger/mechanism, enabling conditions, propagation, evidence explained, one distinguishing observation, and the smallest safe probe.
Read probe commands when bounded source/history/Git evidence can discriminate hypotheses. Prefer existing observations, tests, logs, traces, configuration, history, measurements, and reversible diagnostics that fit the domain.
Choose each next observation or intervention for its ability to distinguish the live hypotheses, not because a preferred debugging ritual exists. Control material confounders where practical. Vary one factor at a time only when that probe can actually discriminate the mechanism; when interactions or coupled conditions are plausible, design the observation/probe to expose those interactions rather than pretending the factors are independent.
For a proposed causal explanation establish:
- Explanatory sufficiency — the proposed mechanism/set explains or reproduces the observed failure and material downstream symptoms without an unresolved causal gap.
- Factor support — each claimed causal factor has discriminating evidence showing its contribution in the relevant context. When feasible, removing/controlling a factor should change the outcome or a predicted mediator as expected.
- Conditionality and alternatives — do not label a factor globally necessary when another sufficient pathway, interaction, or context can produce the same failure. State whether the factor is necessary for this observed pathway, contributing, enabling, interacting, or unresolved.
A factor lacking discriminating support is contextual/contributing/unresolved, not a confirmed root cause merely because it occurred before the failure.
Stop on evidence
Continue only while another safe observation can materially update the causal model. Stop when remaining hypotheses cannot be distinguished, no safe probe can change the diagnosis, required environment/observability/authority is unavailable, or the failure cannot be reproduced and no equivalent direct evidence exists.
Do not perform correction attempts inside Root Cause.
Result
Return one:
CONFIRMED_ROOT_CAUSE— minimal causal mechanism/set and discriminating evidence are sufficient for the observed failure/path;DIAGNOSED_BUT_UNPROVED— best explanation has a material causal/evidence gap;EVIDENCE_BLOCKED— named evidence/environment/authority/observability gap;NOT_REPRODUCED— pinned failure not observed and no equivalent direct evidence.
Include failure identity, minimal mechanism/set, per-factor evidence and causal role, interactions/alternative sufficient paths when material, contributing/contextual/unresolved factors, propagation/containment, decisive evidence, falsified alternatives, affected boundary, confidence limits, and smallest useful next action.
Preserve a needed durable diagnosis in the existing project destination. Use akosile for a selected shared workspace and html-artifact as needed.