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Apply bottom-up systematic testing to a multi-layer system. Use when asked to test, debug, or verify a pipeline that spans multiple layers (e.g., Python worker → bridge → processor → API → UI).
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
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Template for creating Volatio research experiments with Optuna optimization and portfolio backtesting. Includes proper train/test splits, lookahead bias prevention, results saving, and validated patterns from Exp 051-053.
Financial research look-ahead bias verification for Volatio ML pipelines, mandatory baseline comparisons, timezone handling, feature leakage detection. Use when model accuracy seems too good or validating prediction experiments.
Creating PROGRESSION.md documents for research experiment series in Volatio. Captures the evolution of ideas, learnings, and breakthroughs across numbered experiments (001, 002, ...). Use when documenting experiment series or reviewing research evolution.
| name | systematic-testing |
| description | Apply bottom-up systematic testing to a multi-layer system. Use when asked to test, debug, or verify a pipeline that spans multiple layers (e.g., Python worker → bridge → processor → API → UI). |
When testing or debugging a multi-layer system, follow this method strictly.
Identify every distinct layer in the pipeline from data source to UI. Name each one. Draw the dependency graph showing which layers call which. Determine the bottom (fewest internal dependencies) and top (user-facing).
Start at the lowest layer. Move up one layer at a time. Never test a higher layer until the layer below it is verified.
Unit tests — Does this layer's logic produce correct output for known input? Mock everything external. Test edge cases: empty input, zero values, single item, max capacity, error states.
Contract tests — Does the output shape of this layer exactly match what the next layer expects as input? Assert every field name, type, and nesting level. This catches schema drift between producer and consumer.
Error tests — Does this layer handle failures from the layer below? Simulate timeouts, malformed responses, missing fields, empty results.
When Layer A calls Layer B, assert:
When a test reveals a mismatch, fix it immediately. Do not move to the next layer with a known broken contract below. Bugs compound across layers.
Only after all layers pass individually, run cross-layer smoke tests with realistic fixture data end-to-end. These confirm the full chain works. If an integration test fails, go back to the specific layer's unit/contract tests to debug — don't debug in the integration test.
