Radar Analysis

Source Contract

Select an explicit track before analysis:

• systems for AI systems engineering, infrastructure, runtime, security, evaluation, and architecture signals;
• creation for AI-native works and products admitted under the repository's creation evidence standard.

Keep the tracks separate. Perform a cross-track comparison only when the user explicitly requests one, and preserve radar_type provenance in every claim.

Treat these sources in this order:

1. data/<track>/YYYY/*.json as the primary structured record;
2. radars/<track>/YYYY/*.md as Ray-reviewed narrative context;
3. themes/<track>/*.md as curated track-specific recurring judgments;
4. indexes/radars.json, indexes/tracks.json, indexes/themes.json, indexes/summary.json, and themes/README.md as generated navigation.

Filter shared indexes by radar_type; a matching date or theme alone does not make an entry part of the selected track. Do not use external sources or OpenAI API calls unless the user explicitly changes the analysis contract.

Before producing an analysis report, run or mentally apply the target repository's consistency checks:

uv run python scripts/validate_content.py

If indexes are stale and the user asked for a committed artifact, regenerate them with the target repository's script first:

uv run python scripts/update_indexes.py

Do not edit reviewed weekly source files under data/ or radars/ during analysis. Those files are human-reviewed ingestion outputs.

Monthly Reviews

For a deterministic monthly scaffold, use the target repository's generator:

uv run python scripts/generate_monthly_review.py YYYY-MM --track <systems|creation>

The generated file belongs at reports/monthly/<track>/YYYY-MM.md. Keep deterministic extraction and counts separate from any interpretive prose. Ground interpretation in cited dates, signal titles, themes, and track-scoped source paths.

Quarterly Reviews

For a quarter such as 2026-Q2:

1. Select one track, or declare an explicit cross-track comparison.
2. Read indexes/tracks.json, indexes/themes.json, and indexes/summary.json for navigation and repository-level counts.
3. Load canonical records only from data/<track>/YYYY/ whose dates fall within the quarter; verify radar_type on every record.
4. Group by theme frequency, repeated signal language, recurring ideas or patterns, and evidence links.
5. Compare against prior quarters only when matching track records exist locally.
6. Report sparse coverage and missing weeks before making a trend claim.

Use headings such as Coverage, Recurring Themes, Signals Worth Rechecking, Idea Candidates, and Open Questions.

The current repository has no canonical quarterly generator or default quarterly output path. If the user requests a file, use their destination or an existing checked-in convention; do not invent a new authoritative path during analysis.

Idea Mining

When mining ideas:

1. Start from ideas arrays in the selected track's JSON records.
2. Add supporting signals from records with overlapping themes.
3. Distinguish repository evidence from your inference.
4. Prefer a short ranked list over a broad brainstorm.
5. Do not create or update files under ideas/ unless the user explicitly asks for an artifact.

For creation analysis, preserve each source's observation window and evidence level, and use recurring patterns as supporting context rather than upgrading creator or vendor claims. For cross-track idea mining, present track-local evidence first and label the cross-track synthesis as inference.

Each idea should include:

• working title
• source dates
• supporting themes
• strongest supporting signals
• why it may matter
• what would falsify or weaken it

Output Rules

Keep analysis anchored to repository facts:

• Cite dates and file paths for claims.
• Preserve Ray's reviewed framing; do not rewrite weekly reports as news summaries.
• Avoid claiming a trend from a single record unless labeled as an early signal.
• Separate deterministic extraction, track-local interpretation, and cross-track inference.
• If producing a file, run validation after generation when practical.

Portability Notes

• Specific to the author's current workflow: this assumes a multi-track ai-signal-radar repository with systems and creation records, Ray's reviewer framing, shared generated indexes, track-specific curated themes, and track-specific monthly reports.
• Reusable: select an explicit corpus, treat structured data as primary, retain provenance, use reviewed narrative as context, separate extraction from interpretation, and validate generated artifacts.
• Adapt before reuse: replace track taxonomy, repository paths, validator/generator commands, reviewer naming, evidence standards, theme/index conventions, and report periods with the target repository's checked-in contract.