| name | agentic-code-orchestrator |
| description | Unified codebase manipulation, AI deployment, data analysis, and academic delivery engine. Absorbs 6 coding protocols + data-analysis + academic-delivery + spec-driven-dev. |
| vibe | Ship at 70%, iterate to 95%. Never build what you haven't specced. |
| context_trigger | refactor, bug, architecture, data dump, deploy, website, dashboard, code, build, CSV, Parquet, JSON, DuckDB, assignment, essay, capstone, SUSS, academic, Python, React, Next.js, Supabase, vibe code |
| auto-invoke | true |
| model | default |
| source | Retroactively compiled from 1,900+ sessions (2025-2026) via skill-compiler |
| compiled_from | protocols/coding/COD-*, skills/data-analysis, skills/academic-delivery, skills/spec-driven-dev |
| absorbs | data-analysis, academic-delivery, spec-driven-dev, academic-humanizer, statistical-analysis |
| meta_patterns | ["MP-2","MP-11"] |
| pinned | true |
Agentic Code Orchestrator — Build × Analyze × Deliver
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)
- Academic assignments (essays, capstones, SUSS coursework)
- Code refactoring or architecture decisions
- Deploying to Supabase, Vercel, GitHub Pages
- "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):
| Principle | Rule |
|---|
| Above-the-fold | Hero → problem statement → CTA in first viewport |
| Social proof | Testimonials, logos, case study links |
| Speed | <3s load time or you lose 50% of visitors |
| SEO | Meta tags, semantic HTML, structured data |
| Conversion | One clear CTA per page section |
Output Template
ORCHESTRATOR REPORT
───────────────────
Project: [Description]
Engine: [Efficient / Robust — reversibility: ...]
Spec Status: [Approved / Pending — design.md: ...]
Data Pipeline: [DuckDB / Pandas / N/A — file size: ...]
Quality Gate: [De-Sloppified: Y/N — coverage: X%]
Ship Threshold: [60% / 70% / 85% / 95% / 99%]
STATUS: [BUILDING / TESTING / SHIPPED]
Absorbed Protocols & Skills
Coding (6)
COD-107 (Spec-Driven Development), COD-108 (Semantic Search Standards), COD-110 (Structured Decoding), COD-112 (Stop Pattern), COD-415 (Spec-Driven Velocity), COD-900 (Project Scaffolding)
Absorbed Skills
data-analysis → DuckDB-powered large file analyticsacademic-delivery → 8-step pipeline for academic deliverablesacademic-humanizer → AI detection bypass rewritingspec-driven-dev → Interrogation → design.md → buildstatistical-analysis → SPSS/R/Python statistical pipelines
Key Case Studies
CS-062 (Vibe Coding Gap), CS-100 (Project Vend Agentic Failure), CS-120 (Vibe Coding Zero-Cost Stack), CS-157 (ChunkHound Agentic Coding), CS-187 (Deep Data Analyst Post-Mortem), CS-235 (Over-Engineering Trap), CS-237 (Async Dev Workflow), CS-303 (Smart Mock vs Real API), CS-306 (Lovable Trap), CS-350 (Vibe Coding Security Failures), CS-370 (Vibe Coding Trap), CS-425 (Academic Essay Workflow), CS-430 (Vibe Coding MVP), CS-437 (UI/UX Pro Max Architecture), CS-438 (Biological Debt Coding), CS-440 (Velocity vs Craftsmanship), CS-467 (Bionic Leverage Academic Arbitrage), CS-486 (Component-Level AI Architecture), CS-508 (OpenClaw Architecture), CS-515 (Maestro Parallel Orchestration), CS-532 (Vibe Coding Agency Model), CS-539 (CEG3001 Capstone Debrief), CS-540 (Anti-Slop Website Pipeline), CS-543 (Vibe Coded SaaS $10K MRR)
Failure Modes & Mitigations
| Failure | Mitigation |
|---|
| Building without spec | NEVER proceed without design.md approval |
| Pandas on large files | Auto-route to DuckDB for files >100MB |
| AI Slop | De-Sloppify protocol is MANDATORY post-generation |
| Vibe Coding Security | CS-350: Never ship auth, payments, or PII without Robust engine |
| Over-Engineering | CS-235: Spec defines "done." Don't gold-plate. |
| Render Jitter | Extract primitive deps for useEffect. Never pass object refs. |
Validated Patterns (Empirical)
- [V] DuckDB > Pandas: For files >100MB, DuckDB is 10-50x faster and doesn't crash. | Reapply: Every large data analysis.
- [V] NotebookLM Research Arbitrage: Offload PDF ingestion to NotebookLM, keep Athena's context for synthesis. | Reapply: Every academic assignment.
- [V] 4-Decimal GTO Standard: Uniform precision prevents cognitive load in financial dashboards. | Reapply: Every statistical display.
- [V] Primitive Dependency Extraction:
[data.currentRatio] instead of [data] stops React re-render loops. | Reapply: Every dynamic status component.
- [V] Ship at 70%, iterate to 95%: For reversible projects, perfection is the enemy of shipped. | Reapply: Every portfolio/MVP build.
References
- META_PATTERNS.md — MP-2 (RETO engine), MP-11 (Iteration Economy)
- bionic-decision-engine — For build/buy/wait decisions
