Architecture Auditor

Architecture Auditor Skill: The CTO's Forensic Handbook

This skill is designed to perform exhaustive technical audits of startup architectures. It moves beyond high-level boxes into "Plumbing Forensics"—identifying specific libraries, API patterns, and infrastructure moats.

1. THE RESEARCH MANDATE (EVIDENCE ARTIFACTS)

Before specifying a diagram, you MUST harvest the "Ingredients of Truth":

• Library Forensics: Search for specific implementation details (e.g., "Uses LangChain for orchestration with custom Pydantic schemas").
• API Specifics: Identify actual endpoint names, event codes (e.g., DATA_INGESTED, INFERENCE_COMPLETE), and auth protocols.
• Model Tiering: Map exactly which models are used for which tasks (e.g., "Llama-3-8b for routing, GPT-4o for final aggregation").

2. TECH STACK AUDIT CHECKLIST

Identify the specific providers and libraries used in the following layers to determine if it is a "proprietary moat" or a "commodity wrapper."

2.1 Core AI & Orchestration

• Model Providers: OpenAI (GPT-4o/o1), Anthropic (Claude 3.5), Meta (Llama 3), or Google (Gemini).
• Orchestration: LangChain, LlamaIndex, Haystack, or custom Python/FastAPI logic.
• Fine-Tuning: LoRA/QLoRA implementations, Unsloth, or proprietary training on H100 clusters.

2.2 Vector & Data Infrastructure

• Vector Databases: Pinecone, Milvus, Weaviate, Qdrant, or pgvector.
• Data Curation: Snorkel, Scale AI, or proprietary labeling pipelines.
• Streaming/Real-time: Kafka, RabbitMQ, or Upstash.

2.3 Observability & Safety

• Evaluations: Ragas, TruLens, or Giskard.
• Monitoring: LangSmith, Weights & Biases (W&B), or Arize Phoenix.
• Guardrails: NeMo Guardrails, Llama Guard, or custom PII filtering.

3. SYSTEM ARCHITECTURE PATTERNS

Look for these high-fidelity patterns to understand the "intelligence density" of the system.

3.1 RAG (Retrieval-Augmented Generation)

• What to look for: Multi-stage retrieval (Hybrid search), reranking (Cohere Rerank), and metadata filtering.
• Moat check: Are they using "naive RAG" or sophisticated "Agentic RAG" with self-correction?

3.2 Gated Ensemble Pipelines

• What to look for: Routing logic that sends "easy" queries to small models (Llama-3-8b) and "hard" queries to large ones (o1).
• Moat check: This indicates a compute-efficiency moat.

3.3 Autonomous Agent Workflows

• What to look for: Tools like CrewAI, AutoGPT, or custom loops using Tool-Calling APIs.
• Moat check: Does the agent have "Environment Memory" or is it a single-shot execution?

4. THE COMPONENT-BY-COMPONENT TEARDOWN

For each technical section of the report:

1. Gated Logic: Describe the "Early Exit" or "Escalation" conditions between model layers.
2. Infrastructure Bottlenecks: Identify exactly where latency or cost issues exist (e.g., "Vector DB query latency at 500ms+ due to high dimensionality").
3. Moat Verification: Prove if a component is proprietary or off-the-shelf by auditing GitHub dependencies or whitepaper citations.

5. THE VISUAL DESIGN SPECIFICATION (VDS)

This is the formal prompt passed to the excalidraw-diagram skill. Generic specs are failures.

5.1 VDS Checklist:

• Technical Diagram: Map the data journey from Input to Output.
  • Annotations: Every box must contain its technical purpose + dependency name.
  • Binding: Use the Two-Way Binding Protocol (Shape boundElements <-> Text containerId).
• Market Diagram: Use the "Displacement Path" pattern.
  • Nodes: Annotate incumbents with their market share % and key technical weakness.
• Economic Graphs: Use annotated axes with specific milestones (e.g., "Seed Round Achieved").

6. INTEGRATION PROTOCOL

When called by the vc-diligence skill:

1. Read the raw findings in raw_findings/tech/.
2. Perform any missing "Least-to-Most" technical sub-queries.
3. Draft the technical_commercial_deep_dive.md section for Architecture.
4. Generate the finalized Visual Design Specification for delegation.

End of Architecture Auditor Skill.