1. Adversarial Search
- Verification Queries: Designing "Fact-Check" queries to find counter-perspectives.
- Source Auditing: Identifying "fake news", predatory journals, or echo chambers.
2. Data Triangulation
- Cross-Referencing: Mapping overlapping claims across text, data, and academic preprints.
- Inconsistency Forensics: Identifying exactly where two reports diverge and analyzing the reason (bias vs. data).
3. Investigative Narrative
- Truth Mapping: Visualizing the landscape of evidence from "Verified" to "Debunked".
- Evidence Weighting: Assessing the "Preponderance of Evidence".
<source_resolution>
When a sub-claim rests on academic work (a study, paper, or preprint), verify it through the database backends owned by the literature-review skill, not through web search alone: uv run <literature-review-dir>/scripts/openalex_cli.py resolves DOIs/titles and exposes retraction-relevant metadata, europepmc_api.py fetches life-science full text and citation graphs, read_paper.py retrieves full text for any DOI/arXiv/PMCID. A cited study that cannot be resolved in these databases is marked "could not verify" in the Verification Matrix — that is itself a finding. Prerequisite uv: see the literature-review skill's <search_backend> section for setup and invocation details.
</source_resolution>
<output_format>
Investigation Report: [Subject]
Core Question: [The central claim/topic being investigated]
Verification Matrix:
| Claim | Status | Basis of Verification | Confidence |
|---|---|---|---|
| [C1] | [Verified/Refuted] | [Source A, B, C] | [High/Low] |
Source Credibility Audit:
- [Source A]: [Reliability Rating + Notes on Bias]
- [Source B]: [Reliability Rating + Notes on Bias]
Conclusion: [Final verdict based on preponderance of evidence] </output_format>