AI Bubble Stress Engine
Author-described financial research project prepared with Claude assistance that scores stress in the AI investment cycle from public evidence, records counter-evidence and states conditions that would change its verdict.
L1Task assistance (workflow level, not a quality rating) Source: Repository-documented
Research applicability
Illustrates AI-assisted financial research with explicit assumptions, counter-evidence and uncertainty; it does not establish predictive accuracy.
Getting started
Editorial, untested guidance: inspect the protocol, methodology and source-linked indicator files; resolve README version inconsistencies and independently check inputs before considering the documented Python scoring command.
Maintenance
Moving README snapshot only; ongoing maintenance not verified.
License
Rights and licence terms not independently audited.
Workflow & human oversight
Researcher-directed AI assistance; humans choose objectives, inspect sources and approve outputs. L1 denotes disclosed assistance, not autonomous collection or scoring.
Workflow level is not an evidence score; higher is not better evidence.
Limitations
README-only review; scoring code, underlying indicators and tests were not executed or audited. README current block says 18 indicators while the known-weakness section says 17, and robustness prose retains 58 while the current block shows 57: version drift is unresolved. Judgment-heavy inputs, unconfirmed private-company figures and no historical backtesting limit inference. Not investment advice.
Reproducibility
No installation, execution, experiment or independent outcome reproduction.
Adoption requirements
Python 3 according to README; analyst judgment and independently checked public financial sources. Verify dataset permissions and private-company claims. Retain researcher oversight and do not use the stress index as a validated trading signal.
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