market evidence regime monitor

The problem

Traders and analysts often struggle to distinguish between verified market facts, uncorroborated source assertions, and their own analytical biases. Manually tracking how evidence changes over time across disparate source packs leads to fragmented intelligence and "regime drift" without clear invalidation triggers.

What it does

• Normalizes dated market excerpts into a structured source register with freshness and conflict tracking.
• Builds a claim ledger that separates observed facts from inferences using a standardized evidence tagging system.
• Performs delta analysis between current and prior market snapshots to identify strengthened, weakened, or contradicted claims.
• Generates qualitative regime hypotheses with explicit invalidation conditions based on observable triggers.
• Produces a portable, schema-compliant YAML record for integration into larger trading systems.

Why this beats prompting it yourself

This skill enforces a strict evidence protocol that prevents the model from hallucinating probabilities or ignoring conflicting data. It automates the tedious process of source-ID mapping and delta reporting, ensuring that every market claim is bound to a specific piece of user-supplied evidence rather than generic training data.

Use cases

• Audit a collection of research reports to identify consensus vs. conflicting market narratives.
• Compare a fresh macro data pack against a previous month's snapshot to detect structural shifts.
• Define concrete, observable metrics that would prove a current market hypothesis wrong.
• Create a structured evidence record to pass into a separate trade-execution or risk-management agent.

Known limitations

Does not perform autonomous market monitoring or real-time data retrieval. Limited to a maximum of four inputs per run and explicitly excludes trade execution, position sizing, and price forecasting.