What core problem does DecisionCouncil solve for developers?

DecisionCouncil is a structured multi-agent framework that uses internal debate (Scout, Advocate, Skeptic) to eliminate bias and surface hidden risks in complex coding or architectural decisions.

Which AI agents and environments are compatible with this skill?

This skill is specifically optimized for integration with Cursor (via Rules for AI) and Claude Code, allowing you to trigger a formal deliberation process directly within your CLI or IDE.

What exactly is included in the purchase?

You receive the complete configuration files and logic protocols needed to activate the Council, along with a guide on how to trigger specific roles like the Fact Checker or Skeptic.

How difficult is the installation process for this skill?

Setup is near-instant; you simply import the provided logic into your agent's configuration file (.cursorrules or claude_config), and the Council is ready to evaluate your first prompt.

How does a 'Ranked verdict with dissent' differ from a normal AI response?

Unlike standard prompts, this skill produces a structured 'Verdict' that includes a confidence score, a list of verified evidence, and a formal Dissenting Opinion to ensure you see all sides of an issue.

DecisionCouncil

I have convened the council to evaluate your vector database options. The Scout has established pricing and scaling baselines, the Advocate and Skeptic have debated the trade-offs, and the Fact Checker has verified performance benchmarks for 50M+ document clusters.

Council Decision: Vector Database Selection

Rank	Option	Deciding Trade-off	Verdict
#1	Weaviate (Self-hosted)	Highest performance at scale for the budget, despite ops overhead.	Recommended
#2	Pinecone (Serverless)	Superior DX and zero ops, but costs exceed $1k/mo at 50M docs.	Too Costly
#3	pgvector	Most cost-effective, but query latency degrades significantly at 50M.	Performance Risk

Dissenting Opinion

The Skeptic notes that Weaviate's memory management at 50M documents requires significant HNSW tuning. If the team lacks Kubernetes expertise, the "hidden cost" of engineering hours will far exceed the $1k cloud savings.

The Flip Condition

If your team has zero DevOps capacity, move to Pinecone. You would need to increase the budget to ~$1,800/mo, but it prevents a total failure to launch.

Fact Check Results

Claim: pgvector handles 50M docs under 100ms.

[FALSE] — Benchmarks show degradation to >500ms without extreme sharding.

Claim: Pinecone Serverless scales linearly. [VERIFIED] — Confirmed via recent technical blogs.

Next steps

Review the Weaviate HNSW tuning guide for large-scale indices.
Run a small-scale benchmark (1M docs) on pgvector to confirm local latency floor.
Verify if your Pinecone credits apply to the Serverless tier.

DecisionCouncil