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schema_validations DB table is write-only — validation data accumulates with zero product-code readers

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Assessment

Difficulty
3/5
Estimated time
1-2 days
Newbie friendliness
68/100
Issue type
Feature
Clarity
Clearly specified
Activity status
Quiet
Tech stack
python
Domain
cli, databases

Research direction

Start with the webhook-allowlist diagnostic pattern in cmd_doctor.py and the SchemaValidator.validate() insert path at tokenjam/core/db.py:1224; check the schema_validations schema and existing query in tests/integration/test_db.py. Add a doctor check and a test that exercises it against validation failures. Done means tj doctor reports recent failure counts and a sample of errors from real data.

Written by the indexing model from the issue text.

Description

good first issue help wanted

Severity: Medium
Model: sonnet

Where: tokenjam/core/db.py:1224 (SchemaValidator.validate() insert path). Present on main
and self-improve-loop.

Observed: Every span ingest runs SchemaValidator.validate(), which inserts a pass/fail-plus-errors
row into the schema_validations table at tokenjam/core/db.py:1224. There are zero product-code
readers anywhere in the tree — the only SELECT against the table in the whole repo is in
tests/integration/test_db.py. No CLI command, no API route, and no tj doctor check surfaces this
data. A whole validation subsystem accumulates rows no user can ever see.

Fix direction: Surface it. The most natural fit is a tj doctor check reporting recent
validation-failure counts and samples — cmd_doctor.py already has this exact pattern for other
diagnostics (e.g. the webhook-allowlist check), so a new check can follow that shape: query recent
schema_validations failures, summarize counts, print a sample of failing errors.

Done when: A tj doctor check (or equivalent surfaced path) reports schema-validation failures
from real data, with a test exercising it.

Dominant language
Python
Stars
122
Forks
35
Avg merge
1d 18h
Merged PRs (30d)
8

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  4. Open a pull request that references the issue number.

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