| name | database-import-validation |
| description | Use when importing datasets, AV1 answers, prompts, rubrics, or judge outputs into PostgreSQL and validating referential integrity. Do not use for judge prompt design or pure Python project setup. |
Database Import Validation
1. Purpose
Ensure imported data is deterministic, duplicate-safe, referentially valid, and auditable in PostgreSQL.
2. When to use
Use this skill when the task involves:
- importing J1/J2 questions;
- importing AV1 candidate answers;
- importing model metadata;
- importing rubrics or prompt versions;
- validating foreign keys;
- checking duplicates;
- designing or testing idempotent import commands.
3. When not to use
Do not use this skill for:
- generating judge prompts;
- executing judge models;
- backup and restore validation;
- generic code review;
- standalone statistical analysis.
4. Required inputs
Identify:
- source file paths;
- source format: JSONL, CSV, Parquet, SQL, or JSON;
- target tables;
- primary keys or natural keys;
- expected row counts;
- duplicate policy;
- foreign key relationships;
- required metadata fields.
5. Required commands
Use project-specific commands when available.
Generic inspection:
find resources -maxdepth 3 -type f -print
find outputs -maxdepth 4 -type f -print
.venv/bin/python -m atividade_2.cli --help
.venv/bin/python -m pytest
Database validation examples:
psql "$DATABASE_URL" -c "\dt"
psql "$DATABASE_URL" -f path/to/validation.sql
6. Workflow
Step 1 — Identify source contracts
Document for each source:
- file path;
- format;
- schema/fields;
- key fields;
- nullable fields;
- expected row count.
Step 2 — Identify target contracts
Document for each target table:
- columns;
- primary key;
- unique keys;
- foreign keys;
- JSONB metadata fields;
- insert/update behavior.
Step 3 — Normalize before insert
Normalize records into typed Python contracts before writing to PostgreSQL.
Validate:
- required fields;
- dataset labels;
- model identifiers;
- question identifiers;
- answer text presence;
- metadata shape.
Step 4 — Preserve idempotency
Imports should be duplicate-safe.
Use one of:
- natural key upsert;
- deterministic IDs;
- unique constraints;
- explicit duplicate rejection with clear error.
Do not rely on row order alone.
Step 5 — Validate referential integrity
Check:
dataset -> question
question -> candidate answer
candidate answer -> judge evaluation
rubric -> judge evaluation
prompt -> judge evaluation
execution -> judge evaluation
Step 6 — Add validation queries or scripts
Create deterministic checks for:
- row counts;
- duplicate natural keys;
- orphan records;
- missing answer text;
- missing model IDs;
- invalid dataset values;
- invalid score values when evaluations exist.
7. Output format
## Context & Goal
## Sources inspected
## Target tables
## Import strategy
## Idempotency policy
## Validation queries/scripts
## Validation performed
## Risks and follow-ups
8. Validation checklist
9. Guardrails
- Do not silently ignore malformed records.
- Do not hardcode local absolute paths.
- Do not infer missing model/question IDs without evidence.
- Do not treat generated outputs as source fixtures.
- Do not disable foreign keys to make imports pass.
- Do not overwrite existing data without explicit versioning.
