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Compiled: 2026-05-11 (retroactive synthesis of all engineering/academic sessions)
Problem Class: All code generation, data analysis, dashboard building, academic delivery, and technical project execution.
Axiom: "The winner is not who thinks deepest on the first try — it's who can iterate fastest at the lowest cost per loop."
When to Use
Invoke whenever the user mentions:
Building/fixing a website, dashboard, or web app
Data analysis (CSV, JSON, Parquet, large datasets)
"Analyze this data" / "Build me a [thing]" / "Fix this bug"
Solution Architecture
Module 1: The RETO Engine Selector (COD-415 + MP-2)
Before writing ANY code, classify the project:
Is failure reversible? → Efficient Engine (Vibe Engineering: ship at 70%)
Is failure irreversible? → Robust Engine (Nuclear Plant: test everything)
Project Type
Engine
Test Coverage
Ship Threshold
Portfolio/website
Efficient
Visual QA only
70%
Client dashboard
Efficient→Robust
Visual QA + data validation
85%
Financial calculations
Robust
Unit tests + manual verification
99%
Academic submission
Robust
Plagiarism check + format audit
95%
Quick prototype/MVP
Efficient
"Does it work?"
60%
Module 2: Spec-Driven Development (COD-107)
NEVER build without a spec. The spec is the contract.
Phase 1: Interrogation (The /brief)
→ What does the user ACTUALLY want?
→ What are the constraints?
→ What does "done" look like?
Phase 2: design.md Generation
→ Architecture diagram
→ Component breakdown
→ Data flow
→ Acceptance criteria
Phase 3: User Approval
→ Review the spec
→ Confirm scope
→ THEN and ONLY THEN → build
Phase 4: Execution
→ Build to spec, not to vibes
→ Checkpoint every major component
Module 3: The De-Sloppify Protocol (ECC Steal)
After generating code, ALWAYS run this quality pass:
Dead Code Purge: Remove commented-out code, unused imports, placeholder TODOs
Console.log Sweep: Remove all debug logging from production code
Naming Consistency: Verify naming conventions match project standard
Error Handling: Ensure every async operation has error handling
Type Safety: If TypeScript, no any types unless explicitly justified
Module 4: Data Analysis Pipeline (DuckDB-Powered)
For large data dumps (CSV, Parquet, JSON):
Phase 1: Ingest
→ Identify file format + encoding
→ Load with DuckDB (NOT Pandas for large files)
→ Profile: row count, columns, types, nulls, distribution
Phase 2: Profile
→ Summary statistics per column
→ Outlier detection
→ Cardinality analysis
→ Missing data assessment
Phase 3: Query
→ User-directed analysis
→ SQL-based queries via DuckDB
→ Visualization where appropriate
Phase 4: File Insights
→ Key findings summary
→ Actionable recommendations
→ Export results
Rule: For files >100MB, ALWAYS use DuckDB. Pandas will crash.
Module 5: Academic Delivery Pipeline
For SUSS assignments, essays, capstones:
Step 1: Intake — Parse assignment brief, identify marking rubric
Step 2: Research — NotebookLM arbitrage for source material
Step 3: Outline — Structure mapped to rubric weightings
Step 4: Draft — Write with burstiness and perplexity variation
Step 5: Red-Team — Invoke red-team-review on key arguments
Step 6: Humanize — Run academic-humanizer if AI detection risk
Step 7: Format — APA/Harvard citation formatting
Step 8: Deliver — Final audit against rubric
The Bionic Academic Advantage (CS-467):
AI drafts at 80%, human polishes to 100%
Research Arbitrage: NotebookLM handles volume, Athena handles synthesis
SPSS/R/Python for statistical analysis (statistical-analysis skill)
Module 6: Dashboard/Website Architecture
For financial/trading dashboards:
Principle
Rule
Decimal Standard
4 decimal places for all statistical outputs (GTO compliance)
Render Stability
Extract primitive values for useEffect deps, never use object refs
Visual Hierarchy
Status indicators (green/amber/red) for institutional readability
Responsive
Mobile-first, then desktop adaptation
Performance
Lazy load heavy components, debounce real-time updates
For portfolio/marketing websites (CS-437 UI/UX Pro Max):
