How can AI build a local source brief?
AI can build a local source brief by mapping accountable institutions, public records, affected stakeholders, expert context sources, chronology, and source-specific questions. The result should help a reporter start faster without assuming the conclusion.
Use it when
- You have local signals, tips, documents, or public context but need a sharper reporting angle.
- You want the AI agent to separate facts, assumptions, sources, and next reporting steps.
- You need a repeatable workflow that protects names, dates, geography, and attribution.
What you need before running it
Topic, geography, known facts, available sources, and deadlines.
What has already been published or ruled out.
Any reporting constraints, privacy boundaries, or required caveats.
What comes out
- A focused editorial brief with usable angles, source map, and next reporting actions.
- Clear labels for verified facts, supplied facts, assumptions, and open questions.
- A final human-review checklist before anything goes public.
What’s in the package
SKILL.mdexamples/worked-example.mdreferences/quality-checks.mdtemplates/operator-worksheet.mdThe archive also includes a manifest with version, checksum, compatibility, category, and package metadata.
Quality gates
- No unsupported factual claims.
- Dates, local names, and geography preserved.
- Every material recommendation tied to an input or source.
Compatibility checked
Codex, Claude
Compatibility is published only after package structure and ordinary read-path checks.
Questions local operators ask
What is included in the Source Radar Research Brief package?
The Source Radar Research Brief package includes an installable SKILL.md workflow, a worked example, quality checks, an operator worksheet, and package metadata.
Who should use Source Radar Research Brief?
Source Radar Research Brief is built for reporters and newsletter writers who need to build a source map and pre-reporting brief for a local topic with local context and human review.