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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 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Market data integrity rules — gap filling, look-forward bias prevention, and stock split handling. Use when writing or reviewing code that loads prices, fills missing data, computes returns, backtests strategies, or makes trading decisions. Applies across Python, TypeScript, SQL, and TimescaleDB.
Analyze Volatio debug logs from /tmp/volatio-debug/. Use when the user asks to check debug logs, analyze problems, or diagnose simulation issues.
Drizzle Kit workflow for Volatio using idempotent migrations (generate + edit + migrate), never use push. Idempotent patterns prevent partial failure issues, enable safe re-runs, and work cleanly in CI. Use when schema changes, migration errors, or database sync issues occur.
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.
