Agentic Research Directory

Use case

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 Outcome: Author-reported

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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