Upgrade request analysis routing
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@ -15,13 +15,14 @@ Turn raw launcher submissions into structured request items that are:
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## Recommended Pipeline
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1. Read one pending submission.
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2. Split it into candidate atomic requests.
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2. Run a light routing pass that maps slang and broad language onto likely systems and tags.
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3. Retrieve likely systems from `docs/kb/systems.json`.
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4. Load the matching `docs/kb/systems/*.md` files.
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5. Load the standardized request tags from `docs/kb/tags.json`.
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6. Ask the local model for JSON that matches `docs/kb/request-analysis-schema.json`.
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7. Validate the JSON.
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8. Promote high-confidence items and hold low-confidence items for review.
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4. Retrieve likely focused modules from `docs/kb/modules.json`.
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5. Load only the matching `docs/kb/systems/*.md` and `docs/kb/modules/*.md` files.
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6. Load the standardized request tags from `docs/kb/tags.json`.
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7. Ask the local model for JSON that matches `docs/kb/request-analysis-schema.json`.
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8. Validate the JSON.
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9. Promote high-confidence items and hold low-confidence items for review.
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## Suggested Confidence Rules
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@ -41,7 +42,9 @@ Search `systems.json` using:
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- aliases
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- past tags from similar requests
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If a submission touches multiple subsystems, feed the model the top two to four matching docs instead of the whole KB.
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If a submission touches multiple subsystems, feed the model the top two to four matching system docs plus the smallest helpful focused modules instead of the whole KB.
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Use `terminology.json` during the routing pass so phrases like "sprite editor", "painting tool", "recoloring", and "engine" still resolve to the intended Worldshaper concepts.
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## Prompt Skeleton
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@ -67,6 +70,7 @@ If you are unsure, lower confidence and use statusRecommendation = "needs_review
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Then provide:
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- the raw submission
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- the routing summary
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- the JSON schema
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- the retrieved KB docs
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@ -121,6 +125,7 @@ Behavior:
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- high-confidence all-active results replace the pending submission with active request rows
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- mixed or lower-confidence results stay on the pending submission as review metadata
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- failures are stored as analysis errors instead of silently disappearing
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- broad or ambiguous requests should still receive a useful interpretation, likely systems, and possible directions instead of a dead-end review
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## What Not To Automate First
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